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.)

IMF World Economic Outlook (WEO) database. The IMF World Economic Outlook is a twice-yearly survey by IMF staff that presents IMF staff economists' analyses of global economic developments during th...

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.

African Country data can be downloaded from the IMF for: Current account balance,- Employment,- General government gross debt,- General government net debt,- General government net lending/borrowing,- General government primary net lending/borrowing,- General government revenue,- General government structural balance,- General government total expenditure,- Gross domestic product based on purchasing-power-parity (PPP) share of world total,- Gross domestic product corresponding to fiscal year, current prices,- Gross domestic product per capita, constant prices,- Gross domestic product per capita, current prices,- Gross domestic product, constant prices,- Gross domestic product, current prices,- Gross domestic product, deflator,- Gross national savings,- Implied PPP conversion rate,- Inflation, average consumer prices,- Inflation, end of period consumer prices,- Output gap in percent of potential GDP,- Population,- Six-month London interbank offered rate (LIBOR),- Total investment,- Unemployment rate,- Volume of exports of goods,- Volume of exports of goods and services,- Volume of Imports of goods,- Volume of imports of goods and services,- IMF Copyright and Usage here https://www.imf.org/external/terms.htm

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]

The GDP per capita for countries is shown in this dataset for the different years. This economic metric shows the economic output per person and determines the country’s situation based on its economic growth. This dataset can be used to analyze the prosperity of a country based on its economic growth. Countries with higher GDP per countries are determined to be developed whereas countries with low GDP per capita are determined to be developing countries. This dataset can be used to analyze a country’s wealth and prosperity.

The entire World Economic Outlook database.

This dataset contains raw data for all of the variables measured at the Knowlton Fork Climate Station located in Red Butte Canyon (RB_KF_C). Each file contains a calendar year of data. The file for the current year is updated on a daily basis. The data values were collected by a variety of sensors at 15 minute intervals. The file header contains detailed metadata for the site and the variable and method associated with each column. This site is currently operated as part of the Wasatch Environmental Observatory. Prior to 2018 this site was operated as part of the iUTAH GAMUT Network.

This dataset contains a stage-discharge relationship developed for the Wasatch Environmental Observatory Red Butte Network aquatic site on Red Butte Creek near Cottam’s Grove Basic Aquatic (RB_CG_BA). Discharge measurements were collected by a SonTek FlowTracker. Measured stage and discharge and the curve are contained in the Rating Curve file. Information on the site conditions and any issues with discharge measurements are documented in the README file. Files associated with each measurement (e.g., output by the FlowTracker instrument) are contained in the .zip directory. New versions of these files may be loaded when new flow measurements are taken. Calculated discharge for this site can be found here: https://www.hydroshare.org/resource/4659632bae8a440698b1c6b0f4d3558a/

This dataset contains raw data for all of the variables measured for the climate monitoring site in Red Butte Canyon at Above Red Butte Reservoir (RB_ARBR_C). Each file contains a calendar year of data. The file for the current year is updated on a daily basis. The data values were collected by a variety of sensors at 15 minute intervals. The file header contains detailed metadata for the site and the variable and method associated with each column. This site is currently operated as part of the Wasatch Environmental Observatory. Prior to 2018 this site was operated as part of the iUTAH GAMUT Network.

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.

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.

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 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

This dataset contains raw data for all of the variables measured for the Aquatic monitoring site in Red Butte Canyon at 1300E (RB_1300E_A). Each file contains a calendar year of data. The file for the current year is updated on a daily basis. The data values were collected by a variety of sensors at 15 minute intervals. The file header contains detailed metadata for the site and the variable and method associated with each column. This site is currently operated as part of the Wasatch Environmental Observatory. Prior to 2018 this site was operated as part of the iUTAH GAMUT Network.

The database used includes annual frequency data for 43 countries, defined by the IMF as 24 advanced countries and 19 emerging countries, for the years 1992-2018.The database contains the fiscal stress variable and a set of variables that can be classified as follows: macroeconomic and global economy (interest rates in the US, OECD; real GDP in the US, y-o-y, OECD; real GDP in China, y-o-y, World Bank; oil price, y-o-y, BP p.l.c.; VIX, CBOE; real GDP, y-o-y, World Bank, OECD, IMF WEO; GDP per capita in PPS, World Bank); financial (nominal USD exchange rate, y-o-y, IMF IFS; private credit to GDP, change in p.p., IMF IFS, World Bank and OECD); fiscal (general government balance, % GDP, IMF WEO; general government debt, % GDP, IMF WEO, effective interest rate on the g.g. debt, IMF WEO); competitiveness and domestic demand (currency overvaluation, IMF WEO; current account balance, % GDP, IMF WEO; share in global exports, y-o-y, World Bank, OECD; gross fixed capital formation, y-o-y, World Bank, OECD; CPI, IMF IFS, IMF WEO; real consumption, y-o-y, World Bank, OECD); labor market (unemployment rate, change in p.p., IMF WEO; labor productivity, y-o-y, ILO).In line with the convention adopted in the literature, the fiscal stress variable is a binary variable equal to 1 in the case of a fiscal stress event and 0 otherwise. In more recent literature in this field, the dependent variable tends to be defined broadly, reflecting not only outright default or debt restructuring, but also less extreme events. Therefore, following Baldacci et al. (2011), the definition used in the present database is broad, and the focus is on signalling fiscal stress events, in contrast to the narrower event of a fiscal crisis related to outright default or debt restructuring. Fiscal problems can take many forms; in particular, some of the outright defaults can be avoided through timely, targeted responses, like support programs of international institutions. The fiscal stress variable is shifted with regard to the other variables: crisis_next_year – binary variable shifted by 1 year, all years of a fiscal stress coded as 1; crisis_next_period – binary variable shifted by 2 years, all years of a fiscal stress coded as 1; crisis_first_year1 – binary variable shifted by 1 year, only the first year of a fiscal stress coded as 1; crisis_first_year2 - binary variable shifted by 2 years, only the first year of a fiscal stress coded as 1.

Fault Lines Widen in the Global Recovery

Economic prospects have diverged further across countries since the April 2021 World Economic Outlook (WEO) forecast. Vaccine access has emerged as the principal fault line along which the global recovery splits into two blocs: those that can look forward to further normalization of activity later this year (almost all advanced economies) and those that will still face resurgent infections and rising COVID death tolls. The recovery, however, is not assured even in countries where infections are currently very low so long as the virus circulates elsewhere.

The global economy is projected to grow 6.0 percent in 2021 and 4.9 percent in 2022.The 2021 global forecast is unchanged from the April 2021 WEO, but with offsetting revisions. Prospects for emerging market and developing economies have been marked down for 2021, especially for Emerging Asia. By contrast, the forecast for advanced economies is revised up. These revisions reflect pandemic developments and changes in policy support. The 0.5 percentage-point upgrade for 2022 derives largely from the forecast upgrade for advanced economies, particularly the United States, reflecting the anticipated legislation of additional fiscal support in the second half of 2021 and improved health metrics more broadly across the group.

Recent price pressures for the most part reflect unusual pandemic-related developments and transitory supply-demand mismatches. Inflation is expected to return to its pre-pandemic ranges in most countries in 2022 once these disturbances work their way through prices, though uncertainty remains high. Elevated inflation is also expected in some emerging market and developing economies, related in part to high food prices. Central banks should generally look through transitory inflation pressures and avoid tightening until there is more clarity on underlying price dynamics. Clear communication from central banks on the outlook for monetary policy will be key to shaping inflation expectations and safeguarding against premature tightening of financial conditions. There is, however, a risk that transitory pressures could become more persistent and central banks may need to take preemptive action.

Risks around the global baseline are to the downside. Slower-than-anticipated vaccine rollout would allow the virus to mutate further. Financial conditions could tighten rapidly, for instance from a reassessment of the monetary policy outlook in advanced economies if inflation expectations increase more rapidly than anticipated. A double hit to emerging market and developing economies from worsening pandemic dynamics and tighter external financial conditions would severely set back their recovery and drag global growth below this outlook’s baseline.

Multilateral action has a vital role to play in diminishing divergences and strengthening global prospects. The immediate priority is to deploy vaccines equitably worldwide. A $50 billion IMF staff proposal, jointly endorsed by the World Health Organization, World Trade Organization, and World Bank, provides clear targets and pragmatic actions at a feasible cost to end the pandemic. Financially constrained economies also need unimpeded access to international liquidity. The proposed $650 billion General Allocation of Special Drawing Rights at the IMF is set to boost reserve assets of all economies and help ease liquidity constraints. Countries also need to redouble collective efforts to reduce greenhouse gas emissions. These multilateral actions can be reinforced by national-level policies tailored to the stage of the crisis that help catalyze a sustainable, inclusive recovery. Concerted, well-directed policies can make the difference between a future of durable recoveries for all economies or one with widening fault lines—as many struggle with the health crisis while a handful see conditions normalize, albeit with the constant threat of renewed flare-ups.

This dataset contains raw data for all of the variables measured for the storm drain monitoring site in Red Butte Canyon at Connor Road (RB_CR_SD). Each file contains a calendar year of data. The file for the current year is updated on a daily basis. The data values were collected by a variety of sensors at 15 minute intervals. The file header contains detailed metadata for the site and the variable and method associated with each column. This site is currently operated as part of the Wasatch Environmental Observatory. Prior to 2018 this site was operated as part of the iUTAH GAMUT Network.

This dataset contains raw data for all of the variables measured for the Aquatic monitoring site in Red Butte Canyon at Foothill Drive (RB_FD_AA). Each file contains a calendar year of data. The file for the current year is updated on a daily basis. The data values were collected by a variety of sensors at 15 minute intervals. The file header contains detailed metadata for the site and the variable and method associated with each column. This site is currently operated as part of the Wasatch Environmental Observatory. Prior to 2018 this site was operated as part of the iUTAH GAMUT Network.

Contains the GDP growth (% change) from 1980 to 2023 (predicted from 2019 and onwards) for countries around the world. The data was sourced from the International Monetary Fund (IMF), World Economic Outlook (Oct 2018), and from Focus Economics. The spatial data (polygons) were sourced from the World Countries layer by Esri.You can view and download the data here: https://www.imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/ADVEC/WEOWORLDhttps://www.focus-economics.com/blog/emerging-markets-2019-economic-outlook