world_development_indicators

World Development Indicators (WDI) is the World Bank's premier compilation of cross-country comparable data on development. Bulk data download is available at https://datatopics.worldbank.org/world-development-indicators/ This dataset is produced and published automatically by Datadex, a fully open-source, serverless, and local-first Data Platform that improves how communities collaborate on Open Data.

  Dataset Details

Number of rows:… See the full description on the dataset page: https://huggingface.co/datasets/datonic/world_development_indicators.

The Identification for Development (ID4D) Global Dataset, compiled by the World Bank Group’s Identification for Development (ID4D) Initiative, presents a collection of indicators that are of relevance for the estimation of adult and child ID coverage and for understanding foundational ID systems' digital capabilities. The indicators have been compiled from multiple sources, including a specialized ID module included in the Global Findex survey and officially recognized international sources such as UNICEF. Although there is no single, globally recognized measure of having a ‘proof of legal identity’ that would cover children and adults at all ages or, of the digital capabilities of foundational ID systems, the combination of these indicators can help better understand where and what gaps in remain in accessing identification and, in turn, in accessing the services and transactions for which an official proof of identity is often required.

Newly in 2022, adult ID ownership data is primarily based on survey data questions collected in partnership with the Global Findex Survey, while coverage for children is based on birth registration rates compiled by UNICEF. These data series are accessible directly from the World Bank's Databank: https://databank.worldbank.org/source/identification-for-development-(id4d)-data. Prior editions of the data from 2017 and 2018 are available for download here. Updates were released on a yearly basis until 2018; beginning in 2021-2022, the dataset will be released every three years to align with the Findex survey.

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. In addition to the data available here and through the Climate Data API, the Climate Change Knowledge Portal has a web interface to a collection of water indicators that may be used to assess the impact of climate change across over 8,000 water basins worldwide. You may use the web interface to download the data for any of these basins.

Relevant indicators drawn from the World Development Indicators, reorganized according to the goals and targets of the Sustainable Development Goals (SDGs). These indicators may help to monitor SDGs, but they are not always the official indicators for SDG monitoring.

The database provides daily updates of high-frequency indicators on global economic developments, encompassing both advanced economies and emerging market and developing economies. Data are provided at monthly and/or quarterly frequencies, as well as annual series. It includes data on consumer prices, exchange rates, foreign reserves, GDP, industrial production, merchandise trade, retail sales, stock markets, terms of trade, and unemployment.

Explore the Penn World Table dataset featuring key economic indicators like real GDP, population, human capital index, and more. Access detailed information and analysis for various countries.

Expenditure, GDP, PPP, output, Population, working hours, Index, Household, Consumption, Capital , IRR, prices

Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Venezuela, Yemen, Zambia, Zimbabwe, World Follow data.kapsarc.org for timely data to advance energy economics research. When using these data, please refer to the following paper:Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table" American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt

Graph and download economic data for Internet users for Mauritania (ITNETUSERP2MRT) from 1990 to 2023 about Mauritania, internet, and persons.

The World Telecommunication/ICT Indicators database contains time series data for more than 180 telecommunication/ICT (Information and Communication Technologies) statistics. It covers fixed-telephone networks, mobile-cellular telephone subscriptions, quality of service, Internet (including fixed- and mobile-broadband subscription data), traffic, staff, prices, revenue, investment and statistics on ICT access and use by households and individuals. Selected demographic, macroeconomic and broadcasting statistics are also included. The data is for the years 1960, 1965, 1970 and annually from 1975 to 2017. The WTI Database also includes: Economy yearbook pages featuring in the Yearbook of Statistics. These pages show data in economy tables allowing readers to view the evolution of telecommunication services by economy. Statistics are provided for the ten-year period 2007-2017. The latest (2017) data on ICT access and use by households and individuals. Data are presented in tables and broken down by socio-demographic variables, such as age, sex, income and education level etc. Please note: The World Telecommunication/ICT Indicators database is a relational database which must be used with the associated Software Application. In order to search and extract data from the Data file, users will need to download and install the Application and the Data file to the same folder on their personal computers. The database must be installed by first launching the executable (ending in “.exe”) file.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

Graph and download economic data for Gross Domestic Product Per Capita for Least Developed Countries (NYGDPPCAPCDLDC) from 1960 to 2023 about per capita and GDP.

The BILBI indicators portal is an interface where users can explore CSIRO's biodiversity indicators for any signatory countries of the United Nations Convention on Biological Diversity (CBD), plus USA. CSIRO’s global biodiversity indicators (BHI, BERI and PARC) enable governments and organisations to plan and track progress towards biodiversity goals. These indicators are all recognised as component indicators in the CBD Kunming-Montreal Global Biodiversity Framework and provide high-resolution coverage of a large proportion of Earth’s biodiversity across the entire land surface of the planet.

The Biodiversity Habitat Index (BHI) represents the proportion of biodiversity retained within a given area (such as a country or an ecoregion) in relation to the degree of habitat loss, degradation and fragmentation experienced.

The Bioclimatic Ecosystem Resilience Index (BERI) measures the capacity of landscapes to retain species diversity in the face of climate change, as a function of the area, connectivity and integrity of natural ecosystems across those landscapes.

The Protected Area Representativeness and Connectedness Indices (PARC) represent the diversity of biological communities within a protected area system, as well as how connected protected areas are within the broader landscape.

This portal provides a country-level overview of CSIRO's biodiversity indices with a link to download indicator values for countries as well as biomes within countries. More information can be found at https://research.csiro.au/macroecologicalmodelling/bilbi . Lineage: There are 3 indicators available via this interface: BHI, BERI, and PARC. All of these indicators are derived using CSIRO's BILBI biodiversity modeling infrastructure (Biogeographic modelling Infrastructure for Large-scale Biodiversity Indicators). BILBI operates on a global 30-second grid and integrates terrain-adjusted climate data from WorldClim (www.WorldClim.org, v1) and soil data from SoilGrids (www.SoilGrids.org, v1), alongside biodiversity records for more than 400,000 plant, vertebrate and invertebrate species from the Global Biodiversity Information Facility (GBIF). Spatial biodiversity models are generated using generalized dissimilarity modelling for each WWF biome/realm combination.

For BHI and BERI, habitat condition is assessed using downscaled Land Use Harmonisation surfaces, which combine land cover data from the ESA Land Cover Climate Change Initiative (www.esa-landcover-cci.org/) and coefficients from the PREDICTS database (www.nhm.ac.uk/our-science/our-work/biodiversity/predicts.html).

The PARC-representativeness Indicator additionally incorporates protected area data from the World Database on Protected Areas (www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas).

Graph and download economic data for Employment to Population Ratio: All Income Levels for Sub-Saharan Africa (SLEMPTOTLSPZSSSF) from 1991 to 2023 about Sub-Saharan Africa, employment-population ratio, income, employment, and population.

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

The Global Financial Inclusion Database provides 800 country-level indicators of financial inclusion summarized for all adults and disaggregated by key demographic characteristics-gender, age, education, income, and rural residence. Covering more than 140 economies, the indicators of financial inclusion measure how people save, borrow, make payments and manage risk. The reference citation for the data is: Demirguc-Kunt, Asli, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden. 2015. “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, DC.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.\

The sample size was 1005.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Virgin Islands (British) VG: Persistence to Grade 5: % of Cohort: Female data was reported at 33.508 % in 2017. Virgin Islands (British) VG: Persistence to Grade 5: % of Cohort: Female data is updated yearly, averaging 33.508 % from Dec 2017 (Median) to 2017, with 1 observations. The data reached an all-time high of 33.508 % in 2017 and a record low of 33.508 % in 2017. Virgin Islands (British) VG: Persistence to Grade 5: % of Cohort: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Virgin Islands (British) – Table VG.World Bank.WDI: Social: Education Statistics. Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 24, 2024. https://apiportal.uis.unesco.org/bdds.;Weighted average;

Abstract

Key components of the WFSO database cover the prevalence of severe food insecurity, including estimates for countries lacking official data, population sizes of the severely food insecure, and required safety net financing. Data is presented in a user-friendly format.

WFSO data primarily relies on hunger and malnutrition data from the State of Food Security and Nutrition in the World (SOFI) report, led by the Food and agriculture Organization (FAO) in collaboration with multiple UN agencies. WFSO complements SOFI data by providing estimates for unreported countries. Historical estimates are produced with a machine learning model leveraging World Development Indicators (WDI) for global coverage.

Financing needs for safety nets are calculated similarly to past approaches by the International Development Association (IDA) to assess food insecurity response needs (IDA (2020) and IDA (2021)). Preliminary estimates and projections rely on the same model and incorporate International Monetary Fund (IMF)'s World Economic Outlook (WEO) growth and inflation forecasts. WEO data reflects the IMF's expert analysis from various sources, including government agencies, central banks, and international organizations.

Minor gaps in WDI data inflation data are replaced with unofficial WEO estimates. Minor inflation data gaps not covered by both, are replaced with unofficial inflation estimates from the World Bank's Real Time Food Prices (RTFP) data.

The WFSO is updated three times a year, coinciding with IMF's WEO and SOFI releases. It provides food security projections that align with economic forecasts, aiding policymakers in integrating food security into economic planning.

The WFSO database serves various purposes, aiding World Bank economists and researchers in economic analysis, policy recommendations, and the assessment of global financing needs to address food insecurity.

Additionally, the WFSO enhances transparency in global food security data by tracking regional and global figures and breaking them down by individual countries. Historical estimates support research and long-term trend assessments, especially in the context of relating outlooks to past food security crises.

Geographic coverage

World

Geographic coverage notes

191 countries and territories mutually included by the World Bank's WDI and IMF's WEO databases. The country coverage is based on mutual inclusion in both the World Bank World Development Indicators database and the International Monetary Fund’s World Economic Outlook database. Some countries and territories may not be covered. Every attempt is made to provide comprehensive coverage. To produce complete historical predictions, missing data in the WDI are completed with unofficial data from the WEO and the World Bank's RTFP data when inflation data is not available in either database. Final gaps in the WDI and WEO are interpolated using a Kernel-based pattern-matching algorithm. See background documentation for equations.

Analysis unit

Country

Kind of data

Process-produced data [pro]

Virgin Islands (British) VG: Primary Completion Rate: % of Relevant Age Group data was reported at 74.239 % in 2020. This records an increase from the previous number of 69.114 % for 2019. Virgin Islands (British) VG: Primary Completion Rate: % of Relevant Age Group data is updated yearly, averaging 93.418 % from Dec 1985 (Median) to 2020, with 21 observations. The data reached an all-time high of 118.621 % in 1994 and a record low of 69.114 % in 2019. Virgin Islands (British) VG: Primary Completion Rate: % of Relevant Age Group data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Virgin Islands (British) – Table VG.World Bank.WDI: Social: Education Statistics. Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 24, 2024. https://apiportal.uis.unesco.org/bdds.;Weighted average;

Graph and download economic data for Literacy Rate, Adult Total for Developing Countries in Latin America and Caribbean (SEADTLITRZSLAC) from 1974 to 2023 about Caribbean Economies, Latin America, literacy, adult, and rate.

The Gini index measures economic inequality in a country. Specifically, it is the extent to which the distribution of income (or, in some cases, consumption expenditure) deviates from a perfectly equal distribution among individuals or households within an economy.