The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

For further details, please refer to https://datatopics.worldbank.org/world-development-indicators/

List of World Bank accepted inicators for various projects for Nigeria

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0. For further details, please refer to https://documents.worldbank.org/en/publication/documents-reports/documentdetail/815721616086786412/measuring-the-statistical-performance-of-countries-an-overview-of-updates-to-the-world-bank-statistical-capacity-index

Databank (databank.worldbank.org) is an online web resource that provides simple and quick access to collections of time series data. It has advanced functions for selecting and displaying data, performing customized queries, downloading data, and creating charts and maps. Users can create dynamic custom reports based on their selection of countries, indicators and years. They offer a growing range of free, easy-to-access tools, research and knowledge to help people address the world's development challenges. For example, the Open Data website offers free access to comprehensive, downloadable indicators about development in countries around the globe.

Nicaragua NI: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 46.200 % in 2014. This records an increase from the previous number of 43.900 % for 2009. Nicaragua NI: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 50.850 % from Dec 1993 (Median) to 2014, with 6 observations. The data reached an all-time high of 57.400 % in 1993 and a record low of 43.900 % in 2009. Nicaragua NI: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nicaragua – Table NI.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

The average for 2024 based on 48 countries was 6.62 points. The highest value was in Finland: 7.74 points and the lowest value was in Hong Kong: 5.32 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

Abstract

The Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.

The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.

Geographic coverage

80 countries

Analysis unit

Country

Kind of data

Observation data/ratings [obs]

Sampling procedure

The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.

Response rate

100%

Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 34.200 % in 2015. This records a decrease from the previous number of 35.100 % for 2014. Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 35.500 % from Dec 1993 (Median) to 2015, with 15 observations. The data reached an all-time high of 39.000 % in 2005 and a record low of 27.000 % in 1993. Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Latvia – Table LV.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education. The query also holds learning outcome data from international and regional learning assessments (e.g. PISA, TIMSS, PIRLS), equity data from household surveys, and projection/attainment data to 2050. For further information, please visit the EdStats website.

For further details, please refer to https://datatopics.worldbank.org/education/wRsc/about

Switzerland Gini Coefficient (GINI Index): World Bank Estimate data was reported at 32.300 % in 2015. This records a decrease from the previous number of 32.500 % for 2014. Switzerland Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 32.550 % from Dec 2006 (Median) to 2015, with 10 observations. The data reached an all-time high of 34.300 % in 2007 and a record low of 31.600 % in 2012. Switzerland Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

This dataset contains country level economic and social measures for 183 countries. Part 1, World Tables (1980 File), contains, where available, measures of (1)population, (2)national accounts and price data for 1950, 1955, 1960 through 1977, (3)data on external trade for 1962, 1965, 1970, and 1977, (4)data on balance of payments, debt, central government finance and trade indices for 1970-1977, and (5)social data for 1960, 1970, and (estimated) 1977. More specifically, the groupings include population, GDP by industrial origin and expenditures in constant local prices and current local prices, exchange rates and indices, balance of payments and external debt ($US), central government finance in local currency, social indicators, and external trade. Part 2, World Tables (1982 File), contains data on national accounts, prices, exchange rates and population for 1960-1981. The groupings include GDP by industrial origin as well as expenditure in current local prices and constant local prices, area, population, exchange rates, and indices and savings.

Luxembourg LU: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 33.800 % in 2015. This records an increase from the previous number of 31.200 % for 2014. Luxembourg LU: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 31.200 % from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 34.300 % in 2012 and a record low of 30.200 % in 2004. Luxembourg LU: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Luxembourg – Table LU.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 35.400 % in 2009. Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 35.400 % from Dec 2009 (Median) to 2009, with 1 observations. Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sudan – Table SD.World Bank.WDI: Poverty. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The World Bank’s Knowledge Assessment Methodology (KAM: www.worldbank.org/kam) is an online interactive tool that produces the Knowledge Economy Index (KEI)–an aggregate index representing a country’s or region’s overall preparedness to compete in the Knowledge Economy (KE). The KEI is based on a simple average of four subindexes, which represent the four pillars of the knowledge economy:  Economic Incentive and Institutional Regime (EIR)  Innovation and Technological Adoption  Education and Training  Information and Communications Technologies (ICT) Infrastructure The EIR comprises incentives that promote the efficient use of existing and new knowledge and the flourishing of entrepreneurship. An efficient innovation system made up of firms, research centers, universities, think tanks, consultants, and other organizations can tap into the growing stock of global knowledge, adapt it to local needs, and create new technological solutions. An educated and appropriately trained population is capable of creating, sharing, and using knowledge. A modern and accessible ICT infrastructure serves to facilitate the effective communication, dissemination, and processing of information.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Climate Change, External Debt, Trade.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

Syria SY: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data was reported at 1.363 NA in 2016. This records a decrease from the previous number of 2.154 NA for 2014. Syria SY: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data is updated yearly, averaging 2.154 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 2.870 NA in 2010 and a record low of 1.363 NA in 2016. Syria SY: Logistics Performance Index: 1=Low To 5=High: Ease of Arranging Competitively Priced Shipments data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Syrian Arab Republic – Table SY.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents assessed the ease of arranging competitively priced shipments to markets, on a rating ranging from 1 (very difficult) to 5 (very easy). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;