This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per Unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 data set, and CIESIN's Gridded Population of World, Version 2 (GPWv2) data set as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

We developed and presented a set of comparable spatially explicit global gridded gross domestic product (GDP) for both historical period (2005 as representative) and for future projections from 2030 to 2100 at a ten-year interval for all five SSPs. The DMSP-OLS nighttime light (NTL) images and the LandScan Global Population database were used to generate LitPop map, which reduces the limitations of saturation problem of using NTL images alone or the assumption of even GDP per capita within an administrative boundary of gridded data set in GDP disaggregation. We used the LitPop maps to disaggregate national GDP and over 800 provincial gross regional product (GRP, in 2005 PPP USD) across the globe in 2005 and to downscaled to a spatial resolution of 30 arc-seconds (~1 km at equator). National and supranational GDP growth rate projections in 2030-2100 under five SSPs were then downscaled to 1-km grids based on the LitPop approach, which used NPP-VIIRS product as fixed NTL image in 2015 and the population projections of 0.125 arc-degreee (Jones and O'Neill, 2016), which are downscaled to 1-km based on LandScan population distribution pattern in 2015. We then upscaled this gridded GDP dataset to 0.25 arc-degree and provided here.

There are 41 tif files (2005 and 2030 - 2100 at a ten-year interval for five SSPs) for each spatial resolution. The gridded GDP are distributed over land with value of zero filled in the Antarctica, oceans and some desert or wilderness areas (non-illuminated and depopulated zones). The spatial extents are 60S - 90N and 180E - 180W in standard WGS84 coordinate system.

For more details, please refer to the corresponding article: Global gridded GDP data set consistent with the shared socioeconomic pathways by Wang and Sun (2022).

In 2023, South Asia recorded the highest real gross domestic product (GDP) growth rate in the Asia-Pacific region at seven percent, at least 2.7 percentage points higher than other subregions. East Asia reported a real GDP growth rate of about 4.3 percent, while Southeast Asia's real GDP growth rate was around 4.1 percent that year. In 2025, South Asia was forecasted to remain the subregion with the highest real GDP growth rate at six percent, while Southeast Asia was projected to rank second at around 4.7 percent.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

GDP (current US$)

GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for the depreciation of fabricated assets or for the depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single-year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.

Global GDP data from Nationmaster.

The Gross Domestic Product (GDP) In the Euro Area was worth 16406.13 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Euro Area represents 14.74 percent of the world economy. This dataset provides the latest reported value for - Euro Area GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Historical dataset showing World GDP by year from 1960 to 2023.

The Gross Domestic Product (GDP) in Japan was worth 4026.21 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Japan represents 3.79 percent of the world economy. This dataset provides - Japan GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The global high resolution gridded Agricultural GDP (corresponding to "agriculture, forestry, and fishing, value added" in World Development Indicators, henceforth AgGDP, dataset at approximately 10x10 km is the result of a data fusion method based on cross-entropy optimization. We disaggregate national and subnational administrative statistics of Agricultural GDP (circa 2010) into the global gridded dataset at using satellite-derived indicators of the components that make up agricultural GDP, namely crop, livestock, fishery, hunting and timber production. The data resources include the gridded global estimates at approximately 10x10 km

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

The CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.

This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.

Dataset Highlights

Each record in the dataset is meticulously structured and includes:

• Article Titles
• Publication Dates
• Author Names
• Content Summaries
• URLs to Original Articles

This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.

Key Features

• Number of Articles: Hundreds of articles sourced directly from CNBC.
• Data Fields: Includes title, publication date, author, article content, summary, URL, and relevant keywords.
• Topics Covered: U.S. and global economy, GDP trends, inflation, employment, financial markets, and monetary policies.
• Format: Delivered in CSV format for easy integration with research tools and analytical platforms.
• Source: Extracted directly from CNBC’s economy news section, ensuring accuracy and relevance.

Use Cases

• Economic Research: Gain insights into U.S. and global economic policies, market trends, and industry developments.
• Sentiment Analysis: Assess the sentiment of economic articles to gauge market perspectives and investor confidence.
• Financial Modeling: Build forecasting models leveraging key economic indicators discussed in the dataset.
• Content Creation: Develop research-backed reports, articles, and presentations on economic topics.

Explore More News Datasets

Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.

Annual and quarterly data for UK gross domestic product (GDP) estimates, in chained volume measures and current market prices.

United States US: GDP: Market Price: Linked Series data was reported at 19,390.604 USD bn in 2017. This records an increase from the previous number of 18,624.475 USD bn for 2016. United States US: GDP: Market Price: Linked Series data is updated yearly, averaging 11,510.670 USD bn from Dec 1989 (Median) to 2017, with 29 observations. The data reached an all-time high of 19,390.604 USD bn in 2017 and a record low of 5,657.693 USD bn in 1989. United States US: GDP: Market Price: Linked Series data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Nominal. GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. Data are in current local currency.; ; World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database.; ;

The Gross Domestic Product (GDP) in Sweden was worth 610.12 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Sweden represents 0.57 percent of the world economy. This dataset provides the latest reported value for - Sweden GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product (GDP) in Russia was worth 2173.84 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Russia represents 2.05 percent of the world economy. This dataset provides the latest reported value for - Russia GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset shows the long-run projections for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032.

1. Temporal Coverage of Data: The data collection periods are 2010, 2030, and 2050.2. Spatial Coverage and Projection:Spatial Coverage: GlobalLongitude: -180° - 180°Latitude: -90° - 90°Projection: GCS_WGS_19843. Disciplinary Scope: The data pertains to the fields of Earth Sciences and Geography.4. Data Volume: The total data volume is approximately 31.5 MB.5. Data Type: Raster (GeoTIFF)6. Thumbnail (illustrating dataset content or observation process/scene): · 7. Field (Feature) Name Explanation:a. Name Explanation: IND: Industrial Value Addedb. Unit of Measurement: Unit: US Dollars (USD)8. Data Source Description:a. Remote Sensing Data:2010 Global Vegetation Index data (Enhanced Vegetation Index, EVI, from MODIS monthly average data) and 2010 Nighttime Light Remote Sensing data (DMSP/OLS)b. Meteorological Data:From the CMCC-CM model in the Fifth International Coupled Model Intercomparison Project (CMIP5) published by the United Nations Intergovernmental Panel on Climate Change (IPCC)c. Statistical Data:From the World Development Indicators dataset of the World Bank and various national statistical agenciesd. Gross Domestic Product Data:Sourced from the project "Study on the Harmful Processes of Population and Economic Systems under Global Change" under the National Key R&D Program "Mechanisms and Assessment of Risks in Population and Economic Systems under Global Change," led by Researcher Sun Fubao at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciencese. Other Data:Rivers, roads, settlements, and DEM, sourced from the National Oceanic and Atmospheric Administration (NOAA), Global Risk Data Platform, and Natural Earth9. Data Processing Methods(1) Spatialization of Baseline Industrial Value Added: Using 2010 global EVI vegetation index data and nighttime light remote sensing data, we addressed the oversaturation issue in nighttime light data by constructing an adjusted nighttime light index to obtain the optimal global light data. The EANTIL model was developed using NTL, NTLn, and EVI data, with the following formula:Here, EANTLI represents the adjusted nighttime light index, NTL represents the original nighttime light intensity value, and NTLn represents the normalized nighttime light intensity value. Based on the optimal light index EANTLI and the industrial value-added data from the World Bank, we constructed a regression allocation model to derive industrial value added (I), generating the global 2010 industrial value-added data with the formula:Here, I represents the industrial value added for each grid cell, and Ii represents the industrial value added for each country, EANTLi derived from ArcGIS statistical analysis and the regression allocation model.(2) Spatial Boundaries for Future Industrial Value Added: Using the Logistic-CA-Markov simulation principle and global land use data from 2010 and 2015 (from the European Space Agency), we simulated national land use changes for 2030 and 2050 and extracted urban land data as the spatial boundaries for future industrial value added. To comprehensively characterize the influence of different factors on land use and considering the research scale, we selected elevation, slope, population, GDP, distance to rivers, and distance to roads as land use driving factors. Accuracy validation using global 2015 land use data showed an average accuracy of 91.89%.(3) Estimation of Future Industrial Value Added: Based on machine learning and using the random forest model, we constructed spatialization models for industrial value added under different climate change scenarios: Here, tem represents temperature, prep represents precipitation, GDP represents national economic output, L represents urban land, D represents slope, and P represents population. The random forest model was constructed using factors such as 2010 industrial value added, urban land distribution, elevation, slope, distances to rivers, roads, railways (considering transportation), and settlements (considering noise and environmental pollution from industrial buildings), along with temperature and precipitation as climate scenario data. Except for varying temperature and precipitation values across scenarios, other variables remained constant. The model comprised 100 decision trees, with each iteration randomly selecting 90% of the samples for model construction and using the remaining 10% as test data, achieving a training sample accuracy of 0.94 and a test sample accuracy of 0.81.By analyzing the proportion of industrial value added to GDP (average from 2000 to 2020, data from the World Bank) and projected GDP under future Shared Socioeconomic Pathways (SSPs), we derived future industrial value added for each country under different SSP scenarios. Using these projections, we constructed regression models to allocate future industrial value added proportionally, resulting in spatial distribution data for 2030 and 2050 under different SSP scenarios.10. Applications and Achievements of the Dataseta. Primary Application Areas: This dataset is mainly applied in environmental protection, ecological construction, pollution prevention and control, and the prevention and forecasting of natural disasters.b. Achievements in Application (Awards, Published Reports and Articles):Achievements: Developed a method for downscaling national-scale industrial value-added data by integrating DMSP/OLS nighttime light data, vegetation distribution, and other data. Published the global industrial value-added dataset.