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 G20 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

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

The Gross Domestic Product (GDP) in Turkey was worth 1323.25 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Turkey represents 1.25 percent of the world economy. This dataset provides the latest reported value for - Turkey 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 Iran was worth 436.91 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Iran represents 0.41 percent of the world economy. This dataset provides - Iran GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Algeria GDP: Others: Manufacturing Industries data was reported at 1,354.100 DZD bn in 2022. This records an increase from the previous number of 1,230.800 DZD bn for 2021. Algeria GDP: Others: Manufacturing Industries data is updated yearly, averaging 594.050 DZD bn from Dec 1997 (Median) to 2022, with 26 observations. The data reached an all-time high of 1,354.100 DZD bn in 2022 and a record low of 223.200 DZD bn in 1997. Algeria GDP: Others: Manufacturing Industries data remains active status in CEIC and is reported by National Office of Statistics. The data is categorized under Global Database’s Algeria – Table DZ.A014: SNA 1993: GDP: by Main Sectors of Activity: Seasonally Adjusted: Annual.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

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

United States US: Domestic Credit: Provided by Financial Sector: % of GDP data was reported at 241.891 % in 2016. This records an increase from the previous number of 235.955 % for 2015. United States US: Domestic Credit: Provided by Financial Sector: % of GDP data is updated yearly, averaging 145.154 % from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 250.601 % in 2014 and a record low of 101.084 % in 1960. United States US: Domestic Credit: Provided by Financial Sector: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Bank Loans. Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies.; ; International Monetary Fund, International Financial Statistics and data files, and World Bank and OECD GDP estimates.; Weighted average;

This dataset consists of annual estimates of China's gross fixed capital formation at current and constant 2010 prices, investment deflators, depreciation rates, and real capital stock in four economic sectors: business, infrastructure, government, and housing. Such a breakdown is necessary for the purpose of analysis of economic development in China, as the normal models of economic development are based on a competitive economy, which is clearly not the case for the country’s infrastructure and government sectors. Moreover, the contribution of housing to gross domestic product in China is very poorly measured. China's official national accounts do not contain any estimate for the capital stock for the whole economy.

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.

Africa Domestic Credit to Private Sector by Banks (% of GDP) Dataset

  Overview

This dataset contains domestic credit to private sector by banks (% of gdp) data for African countries from the World Bank.

  Data Details

Indicator Code: FS.AST.DOMS.GD.ZS Description: Domestic Credit to Private Sector by Banks (% of GDP) Geographic Coverage: 14 African countries Time Period: 1965-2024 Data Points: 284 observations Coverage: 8.09% of possible country-year combinations… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Africa-Domestic-Credit-to-Private-Sector-by-Banks-percentage-of-GDP.

Time series data for the statistic Total Credit To Non-Financial Sector (% of GDP) and country Burundi.

This paper analyzes the relationship between the relative size of the small and medium enterprise (SME) Sector and the business environment in 76 countries. The paper first describes a new and unique cross-country database that presents consistent and comparable information on the contribution of the SME sector to total employment in manufacturing and GDP across different countries. We then relate the importance of SMEs and the informal economy to indicators of different dimensions of the business environment. We find that several dimensions of the business environment, such as lower costs of entry and better credit information sharing are associated with a larger size of the SME sector, while higher exit costs are associated with a larger informal economy.

This dataset provides values for DOMESTIC CREDIT TO PRIVATE SECTOR PERCENT OF GDP WB DATA.HTML reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Cambodia KH: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data was reported at 26.333 % in 2023. This records a decrease from the previous number of 27.122 % for 2022. Cambodia KH: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data is updated yearly, averaging 20.779 % from Dec 1993 (Median) to 2023, with 31 observations. The data reached an all-time high of 27.122 % in 2022 and a record low of 8.617 % in 1993. Cambodia KH: GDP: % of GDP: Gross Value Added: Industry: Manufacturing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cambodia – Table KH.World Bank.WDI: Gross Domestic Product: Share of GDP. Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;Note: Data for OECD countries are based on ISIC, revision 4.

Given both corruption's and bureaucratic inefficiency's importance for development and good governance, understanding their causes is paramount. This paper argues that majority state ownership of most the most important economic sectors of a country results in higher levels of corruption and inefficiency. When political and managerial elites both own and manage the country's most important economic resources, they have greater incentives for corrupt or inefficient behavior. These elites use national resources at their disposal more for short-term personal and political goals than for long-term economic ones. This paper tests this hypothesis on a relatively underused, but often cited, data set from the 1980s. Using a cross-national, regression analysis, this paper finds that the best predictors a country's level of corruption or bureaucratic inefficiency are these: majority state ownership of significant economic sectors, levels of GDP per capita, levels of government spending, and levels of democracy. Other factors, such as common law heritage, percent of population that is Protestant, federalism, economic freedoms, or mineral/ oil exporting, were not consistent, significant predictors of either bureaucratic inefficiency or corruption. We also argue that Tobit may be the best estimation procedure for these 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.

Time series data for the statistic Total Credit To Private Non-Financial Sector (% of GDP) and country Ethiopia.

The Gross Domestic Product (GDP) in Tanzania was worth 78.78 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Tanzania represents 0.07 percent of the world economy. This dataset provides the latest reported value for - Tanzania GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Time series data for the statistic Total Credit To Non-Financial Sector (% of GDP) and country British Virgin Islands.