The World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff's analysis and projections of economic developments at the global level, in major country groups and individual countries. The WEO dataset is released twice a year: April and September/October. Please fill out this online form for access to the confidential version--not for redistribution or transfer to any unauthorized third party. The public version is available on the IMF website.

The IMF's World Economic Outlook uses a "bottom-up" approach in producing its forecasts; that is, country teams within the IMF generate projections for individual countries. These are then aggregated, and through a series of iterations where the aggregates feed back into individual countries' forecasts, forecasts converge to the projections reported in the WEO.

Because forecasts are made by the individual country teams, the methodology can vary from country to country and series to series depending on many factors. To get more information on a specific country and series forecast, you may contact the country teams directly; from the Countries tab on the IMF website. (From: https://www.imf.org/en/Publications/WEO/frequently-asked-questions#:~:text=%2Ddatabase%2FDisclaimer.-,Q.,generate%20projections%20for%20individual%20countries.)

The entire World Economic Outlook database from the International Monetary Fund (IMF).

Reference: https://www.imf.org/external/pubs/ft/weo/2017/01/weodata/download.aspx

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The entire World Economic Outlook database.

The World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff's analysis and projections of economic developments at the global level, in major country groups and in many individual countries. The WEO is released in April and September/October each year. Use this database to find data on national accounts, inflation, unemployment rates, balance of payments, fiscal indicators, trade for countries and country groups (aggregates), and commodity prices whose data are reported by the IMF. Data are available from 1980 to the present, and projections are given for the next two years. Additionally, medium-term projections are available for selected indicators. For some countries, data are incomplete or unavailable for certain years. Changes to the April 2019 Database: FYR Macedonia is now called North Macedonia. In February 2019, Zimbabwe adopted a new local currency unit, the RTGS dollar, which has become the official unit of account. Efforts are underway to revise and update all national accounts series to the new RTGS dollar. Current data are based on IMF staff estimates of price and exchange rate developments in US (and RTGS) dollars. Staff estimates of US dollar values may differ from authorities’ estimates.

Abstract copyright UK Data Service and data collection copyright owner. The International Monetary Fund (IMF) World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff's analysis and projections of economic developments at the global level, in major country groups and in many individual countries. Main Topics: Topics covered include:national accountsinflationunemployment ratesbalance of paymentsdebtfiscal indicatorstrade for countries and country groups (aggregates)government financecommodity prices

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]

AFR Regional Economic Outlook (REO) provides information on recent economic developments and prospects for countries in Sub-Saharan Africa. Data for the REO for Sub-Saharan Africa is prepared in conjunction with the semi-annual World Economic Outlook (WEO) exercises, spring and fall. Data are consistent with the projections underlying the WEO. REO aggregate data may differ from WEO aggregates due to differences in group membership. Composite data for country groups are weighted averages of data for individual countries. Arithmetic weighted averages are used for all concepts except for inflation and broad money, for which geometric averages are used. PPP GDP weights from the WEO database are used for the aggregation of real GDP growth, real non-oil GDP growth, real per capita GDP growth, investment, national savings, broad money, claims on the nonfinancial private sector, and real and nominal effective exchange rates. Aggregates for other concepts are weighted by GDP in U.S. dollars at market exchange rates.

The WHD Regional Economic Outlook (REO) provides information on recent economic developments and prospects for countries in the Western Hemisphere. Data for the Western Hemisphere REO are prepared in conjunction and are consistent with the semi-annual World Economic Outlook (WEO) exercises. REO aggregate data may differ from WEO aggregates due to differences in group membership. Composite data for country groups are weighted averages of data for individual countries. Arithmetic weighted averages are used for all concepts except for inflation and broad money, for which geometric averages are used. PPP GDP weights from the WEO database are used for the aggregation of real GDP growth, real non-oil GDP growth, real per capita GDP growth, investment, national savings, broad money, claims on the nonfinancial private sector, and real and nominal effective exchange rates. Aggregates for other concepts are weighted by GDP in U.S. dollars at market exchange rates.

This is the data used to estimate the monetary value of Disability-Adjusted Life Years (DALYs) lost from all causes in Mauritius in 2019. The file has (a) Disability-Adjusted Life Years (DALYs) by cause data for Mauritius from 'Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2020. Available online from: http://ghdx.healthdata.org/gbd-results-tool. Accessed 27 October 2020'; (b) Mauritius gross domestic product (GDP) per capita in International Dollars (Int$) from 'International Monetary Fund (IMF). World Economic Outlook Database. [Updated: October 2020]. Available online from: https://www.imf.org/en/Publications/WEO/weo-database/2020/October/select-country-group . Accessed 28 October 2020.'; (c). Mauritius current health expenditure per capita (CHEPC) projected from 'World Health Organization (WHO). Global Health Expenditure Database. Available online from: Available online from: http://apps.who.int/nha/database/Select/Indicators/en . Accessed 28 October 2020.'; and (d) authors estimate of the net GDP per capita in 2019 (Int$) for Mauritius, which is the difference between GDP per capita and current health expenditure per capita.

Imports of environmental goods comprise all environmental goods entering the national territory. A relatively high share of environmental goods imports indicates that an economy purchases a significant share of environmental goods from other economies. Exports of environmental goods comprise all environmental goods leaving the national territory. A relatively high share of environmental goods exports indicates that an economy produces and sells a significant share of environmental goods to other economies. An economy’s environmental goods trade balance is the difference between its exports and imports of environmental goods.Comparative advantage is a measure of the relative advantage or disadvantage a particular economy has in a certain class of goods (in this case, environmental goods), and can be used to evaluate export potential in that class of goods. A value greater than one indicates a relative advantage in environmental goods, while a value of less than one indicates a relative disadvantage.Sources: Department of Economic and Social Affairs/United Nations. 2022. United Nations Comtrade database. https://comtrade.un.org. Accessed on 2023-06-28; International Monetary Fund (IMF) Direction of Trade Statistics (DOTS). https://data.imf.org/dot. Accessed on 2023-06-28. World Economic Outlook (WEO) Database. https://www.imf.org/en/Publications/WEO/weo-database/2022/April. Accessed on 2023-06-28; IMF staff calculations.Category: Cross-Border IndicatorsData series: Comparative advantage in environmental goodsEnvironmental goods exportsEnvironmental goods exports as percent of GDPEnvironmental goods exports as share of total exportsEnvironmental goods importsEnvironmental goods imports as percent of GDPEnvironmental goods imports as share of total importsEnvironmental goods trade balanceEnvironmental goods trade balance as percent of GDPTotal trade in environmental goodsTotal trade in environmental goods as percent of GDPMetadata:Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.Methodology:Environmental goods imports and exports are estimated by aggregating HS 6-digit commodities identified as environmental goods based on OECD and Eurostat, The Environmental Goods & Services Industry: Manual for Data Collection and Analysis, 1999, and IMF research. Total goods imports and exports are estimated by aggregating all commodities. Environmental goods trade balance is calculated as environmental goods exports less environmental goods imports. A positive trade balance means an economy has a surplus in environmental goods, while a negative trade balance means an economy has a deficit in environmental goods.Total goods are estimated by aggregating all commodities. Comparative advantage is calculated as the proportion of an economy’s exports that are environmental goods to the proportion of global exports that are environmental goods. Total trade in environmental goods is calculated as the sum of environmental goods exports and environmental goods imports. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.National-accounts basis GDP at current prices from the World Economic Outlook is used to calculate the percent of GDP. This measure provides an indication of an economy’s involvement (openness) to trade in environmental goods.Methodology Attachment Environmental Goods Harmonized System Codes

APD Regional Economic Outlook (REO) provides information on recent economic developments and prospects for countries in Asia and Pacific. Data for the REO for Asia and Pacific is prepared in conjunction with the semi-annual World Economic Outlook (WEO) exercises, spring and fall. Data are consistent with the projections underlying the WEO. REO aggregate data may differ from WEO aggregates due to differences in group membership. Composite data for country groups are weighted averages of data for individual countries. Arithmetic weighted averages are used for all concepts except for inflation and broad money, for which geometric averages are used. PPP GDP weights from the WEO database are used for the aggregation of real GDP growth, real non-oil GDP growth, real per capita GDP growth, investment, national savings, broad money, claims on the nonfinancial private sector, and real and nominal effective exchange rates. Aggregates for other concepts are weighted by GDP in U.S. dollars at market exchange rates.

GDP per capita (current US$) is an economic indicator that measures the average economic output per person in a country. It is calculated by dividing the total Gross Domestic Product (GDP) of a country by its population, both measured in current US dollars. GDP per capita provides a useful metric for comparing the economic well-being and living standards between different countries.

There are various sources where you can find GDP per capita data, including international organizations, government agencies, and financial institutions. Some prominent sources for GDP per capita data include:

1. World Bank: The World Bank provides comprehensive data on GDP per capita for countries around the world. They maintain the World Development Indicators (WDI) database, which includes GDP per capita figures for different years.

2. International Monetary Fund (IMF): The IMF also offers GDP per capita data through their World Economic Outlook (WEO) database. It provides economic indicators and forecasts, including GDP per capita figures for various countries.

3. National Statistical Agencies: Many countries have their own national statistical agencies that publish GDP per capita data. These agencies collect and analyze economic data, including GDP and population figures, to calculate GDP per capita.

4. Central Banks: In some cases, central banks may also provide GDP per capita data for their respective countries. They often publish economic indicators and reports that include GDP per capita figures.

When using GDP per capita data, it's important to note that it represents an average measure and does not necessarily reflect the distribution of wealth within a country. Additionally, GDP per capita figures are often adjusted for inflation to provide real GDP per capita, which accounts for changes in the purchasing power of money over time.

To access the most up-to-date and accurate GDP per capita data, it is recommended to refer to reputable sources mentioned above or consult the official websites of international organizations, government agencies, or central banks that specialize in economic data and analysis.

This dataset contains Sub-Saharan Africa Regional Economic Outlook from 2004 - 2021.Data from International Monetary Fund. Follow datasource.kapsarc.org for timely data to advance energy economics research.AFR Regional Economic Outlook (REO) provides information on recent economic developments and prospects for countries in Sub-Saharan Africa. Data for the REO for Sub-Saharan Africa is prepared in conjunction with the semi-annual World Economic Outlook (WEO) exercises, spring and fall. Data are consistent with the projections underlying the WEO. REO aggregate data may differ from WEO aggregates due to differences in group membership. Composite data for country groups are weighted averages of data for individual countries. Arithmetic weighted averages are used for all concepts except for inflation and broad money, for which geometric averages are used. PPP GDP weights from the WEO database are used for the aggregation of real GDP growth, real non-oil GDP growth, real per capita GDP growth, investment, national savings, broad money, claims on the nonfinancial private sector, and real and nominal effective exchange rates. Aggregates for other concepts are weighted by GDP in U.S. dollars at market exchange rates.

Imports of low carbon technology products comprise all low carbon technology products entering the national territory. A relatively high share of low carbon technology products imports indicates that an economy purchases a significant share of low carbon technology products from other economies. Exports of low carbon technology products comprise all low carbon technology products leaving the national territory. A relatively high share of low carbon technology products exports indicates that an economy produces and sells a significant share of low carbon technology products to other economies. An economy’s trade balance in low carbon technology products is the difference between its exports and imports of low carbon technology products.Comparative advantage is a measure of the relative advantage or disadvantage a particular economy has in a certain class of goods (in this case, low carbon technology products), and can be used to evaluate export potential in that class of goods. A value greater than one indicates a relative advantage in low carbon technology products, while a value of less than one indicates a relative disadvantage.Sources: Department of Economic and Social Affairs/United Nations. 2022. United Nations Comtrade database. https://comtrade.un.org. International Monetary Fund (IMF) Direction of Trade Statistics (DOTS). https://data.imf.org/dot. World Economic Outlook (WEO) Database. https://www.imf.org/en/Publications/WEO/weo-database/2022/April. IMF staff calculations.Category: Mitigation,Transition to a Low-Carbon EconomyData series: Comparative advantage in low carbon technology productsExports of low carbon technology productsExports of low carbon technology products as percent of GDPExports of low carbon technology products as share of total exportsImports of low carbon technology productsImports of low carbon technology products as percent of GDPImports of low carbon technology products as share of total importsTotal trade in low carbon technology productsTotal trade in low carbon technology products as percent of GDPTrade balance in low carbon technology productsTrade balance in low carbon technology products as percent of GDPMetadata:Sources: Trade data from UN Comtrade Database (https://comtrade.un.org/). Harmonized Commodity Description and Coding System (HS) 2017. Trade aggregates from IMF Direction of Trade Statistics (DOTS) (data.imf.org/dot). GDP data from World Economic Outlook.Methodology:Low carbon technology products are estimated by aggregating HS 6-digit commodities identified as low carbon technology products based on Pigato, Miria A., Simon J. Black, Damien Dussaux, Zhimin Mao, Miles McKenna, Ryan Rafaty, and Simon Touboul. 2020. Technology Transfer and Innovation for Low-Carbon Development. International Development in Focus. Washington, DC: World Bank, and IMF research. Trade balance in low carbon technology products is calculated as low carbon technology products exports less low carbon technology products imports. A positive trade balance means an economy has a surplus in low carbon technology products, while a negative trade balance means an economy has a deficit in low carbon technology products.Total goods are estimated by aggregating all commodities. Comparative advantage is calculated as the proportion of an economy’s exports that are low carbon technology products to the proportion of global exports that are low carbon technology products. Total trade in low carbon technology products is calculated as the sum of low carbon technology products exports and low carbon technology products imports. National-accounts basis GDP at current prices from the World Economic Outlook is used to calculate the percent of GDP. This measure provides an indication of an economy’s involvement (openness) to trade in low carbon technology products, which is important for understanding how these technologies can be transferred between economies.Methodology Attachment Low Carbon Technology Harmonized System Codes

This metadata considers the data on total and insured economic losses and the number of fatalities from weather- and climate-related events in EU Member States and EEA member countries since 1980. Weather- and climate-related hazards considered are those types classified as meteorological hazards (e.g. storms), hydrological hazards (e.g. floods) and climatological hazards (e.g. heatwaves) based on the classification by the International Council for Science (ICSU). The geophysical hazards (e.g. earthquakes and volcanoes) are included for comparison purposes. An event can occur in several countries, but the information is split per country.

The data is based on the RiskLayer CATDAT and the MunichRe NatCatSERVICE datasets (both received under institutional agreement), and on the Eurostat collection of economic indicators, whereas data from earlier years not covered by Eurostat have been completed using data from the Annual Macro-Economic Database of the European Commission (AMECO), the International Monetary Fund’s (IMF) World Economic Outlook (WEO), the Total Economy Database (TED) and the World Bank database. The average population of a country over the period of the time series is used.

The data contains details related to EEA’s indicator “Economic losses from climate-related events in Europe” (https://www.eea.europa.eu/ims/economic-losses-from-climate-related), updated annually. Additional detail on the data and the indicator can be found in the EEA briefing "Economic losses and fatalities from weather- and climate-related events in Europe", 2022 (https://www.eea.europa.eu/publications/economic-losses-and-fatalities-from/economic-losses-and-fatalities-from).

Switzerland IMF Forecast: General Government: Revenue: % of GDP data was reported at 33.329 % in 2023. This stayed constant from the previous number of 33.329 % for 2022. Switzerland IMF Forecast: General Government: Revenue: % of GDP data is updated yearly, averaging 32.416 % from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 33.497 % in 2015 and a record low of 28.201 % in 1991. Switzerland IMF Forecast: General Government: Revenue: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Switzerland – Table CH.IMF.FM: Government Finance Statistics.

Thailand TH: IMF Forecast: General Government: Primary Balance: % of GDP data was reported at -0.658 % in 2023. This records a decrease from the previous number of -0.649 % for 2022. Thailand TH: IMF Forecast: General Government: Primary Balance: % of GDP data is updated yearly, averaging -0.107 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 3.254 % in 2006 and a record low of -5.505 % in 2002. Thailand TH: IMF Forecast: General Government: Primary Balance: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Thailand – Table TH.IMF.FM: Government Finance Statistics.

This dataset contains national time series of gross domestic product (GDP) for the time period 1850-2021 to be used as input data for ISIMIP3a (www.isimip.org, Frieler et al. 2023). For the time period 1960–2021 data are based on the World Bank's World Development Indicator database (WDI, 2008) and converted into constant 2005 Int$PPP and US$MER using deflators and PPP conversion factors from WDI. For countries not covered in the WDI database, data from the MissingIslands dataset (Arujo et al., 2021). For the year 2021, data are derived from the IMF's World Economic Outlook short-term estimates of GDP per capita growth (International Monetary Fund, 2021) that comprise estimates of the growth impacts of the Covid-19 shock. For the years before 1960, the dataset is extended with the national annual GDP estimates provided for ISIMIP2a mostly based on the Maddison project (Maddison, 2016; Geiger, 2018).

Uganda UG: IMF Forecast: General Government: Overall Balance: % of GDP data was reported at 0.631 % in 2023. This records an increase from the previous number of -1.925 % for 2022. Uganda UG: IMF Forecast: General Government: Overall Balance: % of GDP data is updated yearly, averaging -2.097 % from Dec 1997 (Median) to 2023, with 27 observations. The data reached an all-time high of 0.631 % in 2023 and a record low of -5.931 % in 2019. Uganda UG: IMF Forecast: General Government: Overall Balance: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Uganda – Table UG.IMF.FM: Government Finance Statistics.

IMF Forecast: General Government: Gross Debt: % of GDP data was reported at 38.128 % in 2030. This records a decrease from the previous number of 43.301 % for 2029. IMF Forecast: General Government: Gross Debt: % of GDP data is updated yearly, averaging 34.348 % from Dec 2001 (Median) to 2030, with 30 observations. The data reached an all-time high of 76.773 % in 2023 and a record low of 21.690 % in 2015. IMF Forecast: General Government: Gross Debt: % of GDP data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Palau – Table PW.IMF.FM: Government Finance Statistics.