Country-wise GDP growth rate forecast for 2025 based on IMF World Economic Outlook data.

Context

GDP or Gross Domestic Product, as defined by the International Monetary Fund, "measures the monetary value of final goods and services—that is, those that are bought by the final user—produced in a country in a given period of time". GDP per capita shows a country's GDP divided by its total population. It is theoretically the amount of money that each individual earns in that particular country.

GDP per capita is a very handy data to have access to in complement to other datasets, especially when working with prices, salaries, etc. As there was no such updated dataset on kaggle, I uploaded this dataset which is updated up until 2021, with predictions going up to 2026, so that other kagglers can have easy access to it when needed. This data was extracted from the International Monetary Fund official website.

Content

This dataset contains the GDP per capita of 229 countries, from 1980 to 2021, with predictions up until 2026. It is organized as follows: each year is represented by a column, and each country by a row.

Acknowledgements

This data set was extracted from the following website: https://www.imf.org/external/datamapper/NGDPDPC@WEO/OEMDC/ADVEC/WEOWORLD .

** IMF's GDP Data 📈: 1980-2028 Global Trends Explore the economic trajectories of countries worldwide with the "IMF's GDP Data: 1980-2028 Global Trends" dataset. Providing a comprehensive overview of GDP per capita, this dataset measures the average economic output per person in current U.S. dollars. With actual data from 1980 to 2023 and predictions extending to 2028, it's an invaluable asset for understanding past progress and anticipating future growth.

In 2025, the United States had the largest economy in the world, with a gross domestic product of over 30 trillion U.S. dollars. China had the second largest economy, at around 19.23 trillion U.S. dollars. Recent adjustments in the list have seen Germany's economy overtake Japan's to become the third-largest in the world in 2023, while Brazil's economy moved ahead of Russia's in 2024. Global gross domestic product Global gross domestic product amounts to almost 110 trillion U.S. dollars, with the United States making up more than one-quarter of this figure alone. The 12 largest economies in the world include all Group of Seven (G7) economies, as well as the four largest BRICS economies. The U.S. has consistently had the world's largest economy since the interwar period, and while previous reports estimated it would be overtaken by China in the 2020s, more recent projections estimate the U.S. economy will remain the largest by a considerable margin going into the 2030s.The gross domestic product of a country is calculated by taking spending and trade into account, to show how much the country can produce in a certain amount of time, usually per year. It represents the value of all goods and services produced during that year. Those countries considered to have emerging or developing economies account for almost 60 percent of global gross domestic product, while advanced economies make up over 40 percent.

Based on IMF forecasts from April 2025, the real GDP growth in industrial countries will slow in 2030, only growing by *** percent. This is because of the impact of the high global inflation rates. On the other hand, the GDP of emerging and developing countries is expected to grow by around * percent both in 2022, 2030, and 2024.

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.

This study examines the determinants influencing the likelihood of Sub-Saharan African (SSA) countries seeking assistance from the International Monetary Fund (IMF). The IMF, as a global institution, aims to promote sustainable growth and prosperity among its member countries by supporting economic strategies that foster financial stability and collaboration in monetary affairs. Utilising panel-probit regression, this study analyses data from thirty-nine SSA countries spanning from 2000 to 2022, focusing on twelve factors: Current Account Balance (CAB), inflation, corruption, General Government Net Lending and Borrowing (GGNLB), General Government Gross Debt (GGGD), Gross Domestic Product Growth (GDPG), United Nations Security Council (UNSC) involvement, regime types (Closed Autocracy, Electoral Democracy, Electoral Autocracy, Liberal Democracy) and China Loan. The results indicate that corruption and GDP growth rate have the most significant influence on the likelihood of SSA countries seeking IMF assistance. Conversely, factors such as CAB, UNSC involvement, LD and inflation show inconsequential effects. Notable, countries like Sudan, Burundi, and Guinea consistently rank high in seeking IMF assistance over various time frames within the observed period. Sudan emerges with a probability of more than 44% in seeking IMF assistance, holding the highest ranking. Study emphasises the importance of understanding SSA region rankings and the variability of variables for policymakers, investors, and international organisations to effectively address economic challenges and provide financial assistance.

Vietnam VN: IMF Forecast: General Government: Gross Debt: % of GDP data was reported at 58.120 % in 2023. This records an increase from the previous number of 57.648 % for 2022. Vietnam VN: IMF Forecast: General Government: Gross Debt: % of GDP data is updated yearly, averaging 48.237 % from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 59.942 % in 2016 and a record low of 31.432 % in 2000. Vietnam VN: 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 Vietnam – Table VN.IMF.FM: Government Finance Statistics.

The Fiscal Monitor surveys and analyzes the latest public finance developments, it updates fiscal implications of the crisis and medium-term fiscal projections, and assesses policies to put public finances on a sustainable footing.

Country-specific data and projections for key fiscal variables are based on the April 2020 World Economic Outlook database, unless indicated otherwise, and compiled by the IMF staff. Historical data and projections are based on information gathered by IMF country desk officers in the context of their missions and through their ongoing analysis of the evolving situation in each country; they are updated on a continual basis as more information becomes available. Structural breaks in data may be adjusted to produce smooth series through splicing and other techniques. IMF staff estimates serve as proxies when complete information is unavailable. As a result, Fiscal Monitor data can differ from official data in other sources, including the IMF's International Financial Statistics.

The country classification in the Fiscal Monitor divides the world into three major groups: 35 advanced economies, 40 emerging market and middle-income economies, and 40 low-income developing countries. The seven largest advanced economies as measured by GDP (Canada, France, Germany, Italy, Japan, United Kingdom, United States) constitute the subgroup of major advanced economies, often referred to as the Group of Seven (G7). The members of the euro area are also distinguished as a subgroup. Composite data shown in the tables for the euro area cover the current members for all years, even though the membership has increased over time. Data for most European Union member countries have been revised following the adoption of the new European System of National and Regional Accounts (ESA 2010). The low-income developing countries (LIDCs) are countries that have per capita income levels below a certain threshold (currently set at $2,700 in 2016 as measured by the World Bank's Atlas method), structural features consistent with limited development and structural transformation, and external financial linkages insufficiently close to be widely seen as emerging market economies. Zimbabwe is included in the group. Emerging market and middle-income economies include those not classified as advanced economies or low-income developing countries. See Table A, "Economy Groupings," for more details.

Most fiscal data refer to the general government for advanced economies, while for emerging markets and developing economies, data often refer to the central government or budgetary central government only (for specific details, see Tables B-D). All fiscal data refer to the calendar years, except in the cases of Bangladesh, Egypt, Ethiopia, Haiti, Hong Kong Special Administrative Region, India, the Islamic Republic of Iran, Myanmar, Nepal, Pakistan, Singapore, and Thailand, for which they refer to the fiscal year.

Composite data for country groups are weighted averages of individual-country data, unless otherwise specified. Data are weighted by annual nominal GDP converted to U.S. dollars at average market exchange rates as a share of the group GDP.

In many countries, fiscal data follow the IMF's Government Finance Statistics Manual 2014. The overall fiscal balance refers to net lending (+) and borrowing ("") of the general government. In some cases, however, the overall balance refers to total revenue and grants minus total expenditure and net lending.

The fiscal gross and net debt data reported in the Fiscal Monitor are drawn from official data sources and IMF staff estimates. While attempts are made to align gross and net debt data with the definitions in the IMF's Government Finance Statistics Manual, as a result of data limitations or specific country circumstances, these data can sometimes deviate from the formal definitions.

This dataset provides GDP data for all recognized countries from 2020 to 2024 (disputed territories are not included), compiled from IMF data. It is a valuable resource for analyzing global economic trends and understanding individual countries’ growth or decline over this period."

Source: International Monetary Fund (IMF)

Country → Name of the country (no disputed territories included).

2020 → GDP in current USD for year 2020. 2021 → GDP in current USD for year 2021. 2022 → GDP in current USD for year 2022. 2023 → GDP in current USD for year 2023. 2024 → GDP in current USD for year 2024.

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.)

By the year 2030, it is projected that China will eclipse the United States and have the largest gross domestic product (GDP) in the world, at 31.7 trillion U.S. dollars. The United States is projected to have the second largest GDP, at 22.9 trillion U.S. dollars.

What is gross domestic product?

Gross domestic product, or GDP, is an economic measure of a country’s production in time. It includes all goods and services produced by a country and is used by economists to determine the health of a country’s economy. However, since GDP just shows the size of an economy and is not adjusted for the country’s size, this can make direct country comparisons complicated.

The growth of the global economy

Currently, the United States has the largest GDP in the world, at 20.5 trillion U.S. dollars. China has the second largest GDP, at 13.4 trillion U.S. dollars. In the coming years, production will become faster and more global, which will help to grow the global economy.

This dataset provides annual GDP data for 196 countries from 2005 to 2025, measured in USD billions. Data is sourced from the International Monetary Fund (IMF).

Coverage: 196 countries + header row Time span: 2005–2024 (reported), 2025 (projected) Notable trends: The data clearly shows the impact of the 2008 global financial crisis and the 2020 COVID-19 pandemic on world economies. Missing values: In some cases, GDP values are unavailable because countries did not report them.

Usability

Trend analysis — Study global and regional GDP growth patterns across two decades.

Forecasting models — Train ARIMA, Prophet, LSTM, or other models to predict future GDP.

Comparative studies — Benchmark economic performance between countries, continents, or economic blocs (e.g., G7, BRICS).

Impact assessment — Analyze the effect of global events such as the 2008 crisis and COVID-19 on GDP.

Correlation research — Combine with other datasets (population, inflation, CO₂ emissions) for cross indicator analysis.

Visualization projects — Build dashboards, choropleth maps, or interactive charts to illustrate global growth.

Educational use — Teach concepts of macroeconomics, time series data, and forecasting in classrooms.

Investment & policy insights — Support macro level decision making, financial market analysis, or policy research.

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

The statistic shows global gross domestic product (GDP) from 1985 to 2024, with projections up until 2030. In 2020, global GDP amounted to about 85.76 trillion U.S. dollars, two and a half trillion lower than in 2019. Gross domestic product Gross domestic product, also known as GDP, is the accumulated value of all finished goods and services produced in a country, often measured annually. GDP is significant in determining the economic health, growth and productivity in the country, and is a stat often used when comparing several countries at a time, most likely in order to determine which country has seen the most progress. Until 2020, Global GDP had experienced a growth every year since 2010. However, a strong growth rate does not necessarily lead to all positive outcomes and often has a negative effect on inflation rates. A severe growth in GDP leads to lower unemployment, however lower unemployment often leads to higher inflation rates due to demand increasing at a much higher rate than supply and as a result prices rise accordingly. In terms of unemployment, growth had been fairly stagnant since the economic downturn of 2007-2009, but it remains to be seen what the total impact of the coronavirus pandemic will be on total employment.

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

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.

IMF World Economic Outlook (WEO) database. The "http://www.imf.org/external/ns/cs.aspx?id=29">IMF World Economic Outlook is a twice-yearly survey by IMF staff that presents IMF staff economists' analyses of global economic developments during the near and medium term. Associated with the report is the "http://www.imf.org/external/ns/cs.aspx?id=28">World Economic Outlook Database, a country-level dataset of major macro-economic variables (GDP, Unemployment, Debt etc). It is the data from that database which is provided here.

Data

The source database is made of annual values for each country on 45 indicators since 1980. In addition the database includes the IMF projects approximately 6 years into the future.

We extract this data and normalize into 2 files:

• Indicators - data/indicators.csv - the list of indicators
• Values - data/values.csv - set of values for each indicator, country, year tuple.

Sources

Note the XLS files actual turn out to be tsv files!

• Listing page for WEO Database
• 2014 - http://www.imf.org/external/pubs/ft/weo/2014/01/weodata/index.aspx
  • http://www.imf.org/external/pubs/ft/weo/2014/01/weodata/WEOApr2014all.xls
• 2011 - http://www.imf.org/external/pubs/ft/weo/2011/02/weodata/index.aspx
  • http://www.imf.org/external/pubs/ft/weo/2011/02/weodata/WEOSep2011all.xls

Preparation

Code to extract the data from the source WEO Database is in the scripts directory.

This dataset has all the countries listed with their continents, GDP, population and GDP_per_capita. We can use GDP per capita as a label and play with the dataset. Using linear regression , is there a possibility to explore in terms of reciprocal relation between the features.

Potential national income loss from climate risks can be computed using simple damage functions that estimate damages based on the temperature outcomes inferred from the emissions trajectories projected by the transition scenarios. Potential national income benefit from avoided climate damages can be computed by contrasting the damages estimates based on the temperature outcomes from the transition scenarios with the policy, or mitigation, costs from climate action needed to meet a particular temperature outcome.Sources: Network for Greening the Financial System (2023), Scenarios Portal; and International Institute for Applied Systems Analysis (2023), NGFS Phase 4 Scenario Explorer; IMF Staff Calculations.Category: Transition to a Low-Carbon EconomyMetadataThe framework of the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) allows to simulate, in a forward-looking fashion, the dynamics within and between the energy, land-use, economy, and climate systems. Consistent with that framework, the NGFS explores a set of seven climate scenarios, which are characterized by their overall level of physical and transition risk. The scenarios in the current Phase IV (NGFS climate scenarios data set) are Low Demand, Net Zero 2050, Below 2°C, Delayed Transition, Nationally Determined Contributions (NDCs), Current Policies, and Fragmented World. Each NGFS scenario explores a different set of assumptions for how climate policy, emissions, and temperatures evolve. To reflect the uncertainty inherent to modeling climate-related macroeconomic and financial risks, the NGFS scenarios use different models, over and above the range of scenarios. These integrated assessment models (IAMs) are, by their acronyms: GCAM, MESSAGEix-GLOBIOM, and REMIND-MAgPIE. GDP losses and benefits are derived based on the National Institute Global Econometric Model (NiGEM). NiGEM consists of individual country models for the major economies, which are linked together through trade in goods and services and integrated capital markets. Country level data (or country aggregates, whenever country level disaggregation is not present) for GDP, population, primary energy consumption by fuel type, useful energy and carbon taxes from the IAM output is used as an input into the NiGEM scenarios. Climate scenarios within NiGEM can be broadly categorized into physical and transition events. While the effects of physical and transition shocks alongside policy decisions are contemporaneous, the scenarios in NiGEM can be run in a “stacked” manner, where each scenario uses the information provided by the previous scenario as its starting point. This allows for decomposition of shocks and their effects. Results are available for three scenarios: Net Zero 2050, Delayed Transition, and Current Policies. For details please see the NGFS climate scenarios presentation, the Climate scenarios technical documentation, and the User guide for data access.MethodologyThe NGFS climate scenarios database contains information on mitigation policy costs, business confidence losses, chronic climate damages, and acute climate damages. Mitigation policy costs reflect transition risk in a narrow sense and is measured against the Current Policies scenario (for which it is zero). Business confidence losses result from unanticipated policy changes, and only in the Delayed Transition scenario. GDP losses from chronic risks arise from an increase in global mean temperature. Estimates of the macroeconomic impact of acute risks are based on physical risk modelling covering different hazards. Acute risks are modeled independent of the input IAM. Results are available at the original sources for four hazards: droughts impacting on crop yields, tropical cyclones directly damaging assets, heatwaves affecting productivity and demand, and riverine floods directly damaging assets too. Apart from floods acute risks are the result of randomized stochastic output, yielding 60th to 99th percentile GDP impacts. In accordance with the presentation of the scenario results by the NGFS, the 90th percentile has been chosen as the representative confidence bound. That way, the results are focusing on tail risk. While the choice of the percentile will lead to marked differences for the GDP losses indicator, its influence on the GDP benefits indicator is muted due to comparing like-with-like. Further, the sum of the impacts from the four hazards is taken as the acute physical risk measure; see what follows for the methodology in deriving the net benefits. Net benefits can be calculated by comparing the impact of stronger climate action to the reference scenario, the Current Policies scenario: Net Benefit = 100 * (GDP[Policy scenario] / GDP[Current Policies] – 1). GDP in either scenario can be inferred from the hypothetical baseline with no transition nor physical risk and the percentage losses due to mitigation policy (MP), business confidence (BC), chronic climate (CC), and acute climate (AC): GDP = Baseline * (1 + (MP + BC + CC + AC) / 100). Plugging this into the above equation one finds after some algebra: Net Benefit = (MP[Policy scenario] – MP[Current Policies] + BC[Policy scenario] – BC[Current Policies] + CC[Policy scenario] – CC[Current Policies] + AC[Policy scenario] – AC[Current Policies]) / (1 + (MP + BC + CC + AC)[Current Policies] / 100). Obviously, MP[Current Policies] = BC[Current Policies] = BC[Net Zero 2050] = 0. In order to achieve consistency in aggregation of the four components to the total benefit, the denominator is kept fixed, while for the individual contributions only one component at a time, MP, BC, CC, or AC, is used in the numerator. Results are presented for the 49 countries, five geographic regions covering the remainder of countries, and a global and European total. The coverage of the five remainder regions refers to the country classification of emerging market and developing economies in the IMF’s World Economic Outlook.Data series: Potential National Income Loss From Climate RisksPotential National Income Benefit From Avoided Climate Damages