Explore real GDP growth projections dataset, including insights into the impact of COVID-19 on economic trends. This dataset covers countries such as Spain, Australia, France, Italy, Brazil, and more.

growth rate, Real, COVID-19, GDP

Spain, Australia, France, Italy, Brazil, Argentina, United Kingdom, United States, Canada, Russia, Turkiye, World, China, Mexico, Korea, India, Saudi Arabia, South Africa, Germany, Indonesia, JapanFollow data.kapsarc.org for timely data to advance energy economics research..Source: OECD Economic Outlook database.- India projections are based on fiscal years, starting in April. The European Union is a full member of the G20, but the G20 aggregate only includes countries that are also members in their own right. Spain is a permanent invitee to the G20. World and G20 aggregates use moving nominal GDP weights at purchasing power parities. Difference in percentage points, based on rounded figures.

This table presents Gross Domestic Product (GDP) and its main components according to the expenditure approach. Data is presented in US dollars. In the expenditure approach, the components of GDP are: final consumption expenditure of households and non-profit institutions serving households (NPISH) plus final consumption expenditure of General Government plus gross fixed capital formation (or investment) plus net trade (exports minus imports).

When using the filters, please note that final consumption expenditure is shown separately for the Households/NPISH and General Government sectors, not for the whole economy. All other components of GDP are shown for the whole economy, not for the sector breakdowns.

The table shows OECD countries and some other economies, as well as the OECD total, G20, G7, OECD Europe, United States - Mexico - Canada Agreement (USMCA), European Union and euro area.

These indicators were presented in the previous dissemination system in the QNA dataset.
See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

This table presents Gross Domestic Product (GDP) and its main components according to the expenditure approach. Data is presented as growth rates. In the expenditure approach, the components of GDP are: final consumption expenditure of households and non-profit institutions serving households (NPISH) plus final consumption expenditure of General Government plus gross fixed capital formation (or investment) plus net trade (exports minus imports).

When using the filters, please note that final consumption expenditure is shown separately for the Households/NPISH and General Government sectors, not for the whole economy. All other components of GDP are shown for the whole economy, not for the sector breakdowns.

The data is presented for OECD countries individually, as well as the OECD total, G20, G7, OECD Europe, United States - Mexico - Canada Agreement (USMCA), European Union and euro area.

These indicators were presented in the previous dissemination system in the QNA dataset.
See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

AMECO is the annual macro-economic database of the European Commission's Directorate General for Economic and Financial Affairs (DG ECFIN). The database is regularly cited in DG ECFIN's publications and is indispensable for DG ECFIN's analyses and reports. To ensure that DG ECFIN's analyses are verifiable and transparent to the public, AMECO data is made available free of charge. AMECO contains data for EU-27, the euro area, EU Member States, candidate countries and other OECD countries (United States, Japan, Canada, Switzerland, Norway, Iceland, Mexico, Korea, Australia and New Zealand).

The current health expenditure as a share of the GDP in Mexico was forecast to continuously increase between 2024 and 2029 by in total 0.4 percentage points. After the seventh consecutive increasing year, the share is estimated to reach 6.61 percent and therefore a new peak in 2029. According to Worldbank health spending includes expenditures with regards to healthcare services and goods. It is depicted here in relation to the total gross domestic product (GDP) of the country or region at hand.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current health expenditure as a share of the GDP in countries like Canada and United States.

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

description: NOTICE: A significant issue with the precipitation variables in this dataset was found in January 2015. The precipitation data has two fewer columns than the temperature data, one from each edge. When merged into the same coordinate system, this caused the temperature data to be offset to the west by one pixel. The dataset is now broken into two sub-datasets, one for precipitation and one for temperature. This corrects the pixel location. Any use of precipitation data from this dataset from September 2013, when new precipitation files containing the issue were introduced, should be considered slightly in error. For more information please contact gdp@usgs.gov. In this project, we used an advanced statistical downscaling method that combines high-resolution observations with outputs from 16 different global climate models based on 4 future emission scenarios to generate the most comprehensive dataset of daily temperature and precipitation projections available for climate change impacts in the U.S. The gridded dataset covers the continental United States, southern Canada and northern Mexico at one-eighth degree resolution and Alaska at one-half degree resolution. The high-resolution projections produced by this work have been rigorously quality-controlled for both errors and biases in the global climate and statistical downscaling models. We also calculated projected future changes in a broad range of impact-relevant indicators, from seasonal temperature to extreme precipitation days. The results of the error and bias tests and the indicator calculations are made available as part of this database. Additional information and raw data from this dataset can be found here: https://cida.usgs.gov/thredds/catalog.html Before using this dataset, please review the material summarized here: https://my.usgs.gov/confluence/display/GeoDataPortal/2014/04/16/Notice%3A+Evaluation+of+Maurer+gridded+observational+datasets+and+their+impacts+on+downscaled+products Note that the CONUS temperature and precipitation data were split into two sub datasets in January 2015. This was done because the precipitation data uses a slightly different longitude axis than the temperature data.; abstract: NOTICE: A significant issue with the precipitation variables in this dataset was found in January 2015. The precipitation data has two fewer columns than the temperature data, one from each edge. When merged into the same coordinate system, this caused the temperature data to be offset to the west by one pixel. The dataset is now broken into two sub-datasets, one for precipitation and one for temperature. This corrects the pixel location. Any use of precipitation data from this dataset from September 2013, when new precipitation files containing the issue were introduced, should be considered slightly in error. For more information please contact gdp@usgs.gov. In this project, we used an advanced statistical downscaling method that combines high-resolution observations with outputs from 16 different global climate models based on 4 future emission scenarios to generate the most comprehensive dataset of daily temperature and precipitation projections available for climate change impacts in the U.S. The gridded dataset covers the continental United States, southern Canada and northern Mexico at one-eighth degree resolution and Alaska at one-half degree resolution. The high-resolution projections produced by this work have been rigorously quality-controlled for both errors and biases in the global climate and statistical downscaling models. We also calculated projected future changes in a broad range of impact-relevant indicators, from seasonal temperature to extreme precipitation days. The results of the error and bias tests and the indicator calculations are made available as part of this database. Additional information and raw data from this dataset can be found here: https://cida.usgs.gov/thredds/catalog.html Before using this dataset, please review the material summarized here: https://my.usgs.gov/confluence/display/GeoDataPortal/2014/04/16/Notice%3A+Evaluation+of+Maurer+gridded+observational+datasets+and+their+impacts+on+downscaled+products Note that the CONUS temperature and precipitation data were split into two sub datasets in January 2015. This was done because the precipitation data uses a slightly different longitude axis than the temperature data.

This data for global, regional (EU-27), and country-specific (G20 member countries) energy and emission pathways required to achieve a defined carbon budget of under 450 Gt/CO2, developed to limit the mean global temperature rise to 1.5°C, over 50% likelihood. The data were calculated with the 1.5°C sectorial pathways of the One Earth Climate Model—an integrated energy assessment model devised at the University of Technology Sydney (UTS). The data consist of the following six zip-folder datasets (refer to Section 2 for an explanation of the data): 1. Appendix folder: Each file contains one worksheet, which summarizes the overall 1.5°C scenario. 2. Sector folder (XLSX): Each file contains one worksheet, which summarizes the industry sectors analysed. 3. Sector folder (CSV): The data contained are the same as those described in point 2. 4. Sector emissions folder: Each file contains one worksheet, which summarizes the total annual emissions for each industry sector. 5. Scope emissions folder (XLSX): Each file contains one worksheet, which summarizes the total annual emissions for each industry sector—with the additional specificity of emission scope. 6. Scope emissions folder (CSV): The data contained are the same as those described in point 5. Methods The data consist of the following six zipped dataset folders, each containing 21 separate files for each of the areas assessed. 1. Appendix zip folder: contains 21 XLSX files. Each file contains one worksheet, which summarizes the overall 1.5 °C scenario. This tab is called the ‘Appendix’ and contains: electricity generation (TWh/a), transport—final energy (PJ/a), heat supply and air conditioning (PJ/a), installed capacity (GW), final energy demand (PJ/a), energy-related CO2 emissions (million tons/a), and primary energy demand (PJ/a). 2. Sector zip folder (XLSX): contains 21 XLSX files. Each file contains one worksheet, which summarizes the industry sectors analysed. Key industry metrics are provided, such as the energy and carbon intensities of the GICS sectors analysed. Due to industry specificity—and the choice of methodology—the units of data vary between the different sectors. 3. Sector zip folder (CSV): contains 21 CSV files. The data contained are the same as those described in point 2. However, the data have been organized in a database layout and saved in the CSV file format, significantly improving data parsing. 4. Sector emission zip folder: contains 21 XLSX files. Each file contains one worksheet, which summarizes the total annual emissions (MtCO2/a) for each industry sector. 5. Scope emissions zip folder (XLSX): contains 21 XLSX files. Each file contains one worksheet, which summarizes the total annual emissions (MtCO2/a) for each industry sector—and specifies the emission scopes. This tab also provides an additional breakdown of emissions into the categories of CO2 and total GHG emissions. Two accounting methodologies are presented: (i) the OECM approach, which defines Scope 1 emissions as those related to heat and energy use; and (ii) the production-centric approach, which places the emission burden of other non-energy and Scope 3 emissions on the producer, because they are categorized as Scope 1 emissions. 6. Scope emissions zip folder (CSV): contains 21 CSV files. The data contained are the same as those described in point 5. However, the data have been organized in a database layout and saved in the CSV file format to improve data parsing. The six datasets are summarized in Table 1, with further information on the data presented in the following sub-sections. Table 1: Overview of the data files/datasets

Label

Name of data file/dataset

File types

Data repository and identifier (DOI or accession number)

Dataset 1

Appendix

XLSX

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 2

Sector_XLSX

XLSX

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 3

Sector_CSV

CSV

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 4

Sector_Emission

XLSX

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 5

Scope_Emission_XLSX

XLSX

https://doi.org/10.5061/dryad.cz8w9gj82

Dataset 6

Scope_Emission_CSV

CSV

https://doi.org/10.5061/dryad.cz8w9gj82

1.1. Description of data parameters The datasets contain the following scenario input parameters: 1. Market development: current and assumed development of the demand by sector, such as cement produced, passenger kilometers travelled, or assumed market volume in US$2015 gross domestic product (GDP). 2. Energy intensity—activity based: energy use per unit of service and/or product; for example, in megajoules (MJ) per passenger kilometer travelled (MJ/pkm), MJ per ton of steel (MJ/ton steel), aluminum, or cement. 3. Energy intensity—finance based: energy use per unit of investment in MJ per US$ GDP (MJ/$GDP) contributed by, for example, the forestry or agricultural sector. The dataset contains the following scenario output parameters: 4. Carbon intensity: current and future carbon intensities per unit of product or service; for example, in tons of CO2 per ton of steel produced (tCO2/ton steel) or grams of carbon dioxide per passenger kilometer (gCO2/pkm). 5. Scope 1, 2, and 3 emissions: datasets for each of the industry sectors and countries analysed. In addition to the emissions data, the deviations of the emissions from those of the year 2019 are provided. 6. Country scenarios: complete country scenario datasets of historical data (2012, 2015–2020) and future projections (2025–2050 in 5-year increments). Energy demand and supply data by technology, fuel, and sector are provided, including the overall energy and carbon emissions balance of the country analysed. 1.2. Geographic resolution: country data provided The dataset contains data for the following 21 countries and regions: · Regions: global, EU-27 · Countries: G20 member countries—Canada, USA, Mexico, Brazil, Argentina, Germany, France, Italy, United Kingdom, Türkiye, Russian Federation, Saudi Arabia, South Africa, Indonesia, India, China, Japan, South Korea, and Australia 1.3. Sectorial resolution: industry sector data provided The dataset contains data for the following industry sectors: Agriculture & food processing, forestry & wood products, chemical industry, aluminum industry, construction and buildings, water utilities, textile & leather industry, steel industry, cement industry, transport sector (aviation: freight & passenger transport; shipping: freight & passenger transport; and road transport: freight & passenger transport). 1.4. Time resolution The scenario data are provided for the years 2017, 2018, 2019, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.

This table presents volume indices and the price indices (or deflators) for Gross Domestic Product (GDP) and its main components according to the expenditure approach. In the expenditure approach, the components of GDP are: final consumption expenditure of households and non-profit institutions serving households (NPISH) plus final consumption expenditure of General Government plus gross fixed capital formation (or investment) plus net trade (exports minus imports).

When using the filters, please note that final consumption expenditure is shown separately for the Households/NPISH and General Government sectors, not for the whole economy. All other components of GDP are shown for the whole economy, not for the sector breakdowns. The data is presented for OECD countries and some other economies, as well as the OECD total, G20, G7, OECD Europe, United States - Mexico - Canada Agreement (USMCA), European Union and euro area.

These indicators were presented in the previous dissemination system in the QNA dataset.
See User Guide on Quarterly National Accounts (QNA) in OECD Data Explorer: QNA User guide
See QNA Calendar for information on advance release dates: QNA Calendar
See QNA Changes for information on changes in methodology: QNA Changes
See QNA TIPS for a better use of QNA data: QNA TIPS
Explore also the GDP and non-financial accounts webpage: GDP and non-financial accounts webpage
OECD statistics contact: STAT.Contact@oecd.org

The United States recorded a trade deficit of 71.52 USD Billion in May of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The United States' total Exports in 2024 were valued at US$2.06 Trillion, according to the United Nations COMTRADE database on international trade. The United States' main export partners were: Canada, Mexico and China. The top three export commodities were: Mineral fuels, oils, distillation products; Machinery, nuclear reactors, boilers and Electrical, electronic equipment. Total Imports were valued at US$3.36 Trillion. In 2024, The United States had a trade deficit of US$1.29 Trillion.