The Gross Domestic Product per capita in European Union was last recorded at 34601.03 US dollars in 2023. The GDP per Capita in European Union is equivalent to 274 percent of the world's average. This dataset provides the latest reported value for - European Union GDP Per Capita - 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 the Euro Area was worth 15780.69 billion US dollars in 2023, according to official data from the World Bank. The GDP value of Euro Area represents 14.97 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.

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

The Gross Domestic Product (GDP) in European Union expanded 1.40 percent in the fourth quarter of 2024 over the same quarter of the previous year. This dataset provides the latest reported value for - European Union GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

This table presents annual data on the output components, the final expenditure categories and the income components of gross domestic product of the Netherlands. In the national accounts gross domestic product is approached from three points of view: from the output, from the generation of income and from the final expenditure. Gross domestic product is a main macroeconomic indicator. The volume change of gross domestic product is a measure for the economic growth of a country.

Data available from: 1969 up to and including 2016.

Status of the figures: Data from 1969 up to and including 2015 are final. Data of 2016 are provisional. Since this table has been discontinued, data of 2016 will not become final.

Changes as of June 22nd 2018: None. This table has been discontinued. Statistics Netherlands has carried out a revision of the national accounts. New statistical sources and estimation methods have been used during the revision. Therefore this table has been replaced by table Approaches of domestic product (GDP); National Accounts. For further information see section 3.

When will new figures be published? Not applicable anymore.

Graph and download economic data for Real Gross Domestic Product for Germany (CLVMNACSCAB1GQDE) from Q1 1991 to Q4 2024 about Germany, real, and GDP.

Country, regional and world GDP in current US Dollars ($). Regional means collections of countries e.g. Europe & Central Asia. Data is sourced from the World Bank and turned into a standard normalized CSV.

This table presents economic growth data (percentage volume changes of gross domestic product) and the contributions to economic growth by expenditure components.

Gross domestic product can be calculated as the sum of final consumptions, gross capital formation and net exports. This expenditure approach allows to estimate the contribution of the various components of final expenditure to the volume change of GDP. For estimating the contribution, final expenditure components have to be adjusted for the incorporated imports. The adjusted final expenditure components sum up to GDP and are the bases of the calculation of the contribution of GDP growth. The attribution of imports to final expenditure components is performed using input-output analysis.

Contributions of final expenditure to GDP are provided in percentage point of GDP growth.

Data available from: 1995 up to and including 2016.

Status of the figures: Data from 1995 up to and including 2015 are final. Data of 2016 are provisional. Since this table has been discontinued, data of 2016 will not become final.

Changes as of June 22nd 2018: None. This table has been discontinued. Statistics Netherlands has carried out a revision of the national accounts. New statistical sources and estimation methods have been used during the revision. Therefore this table has been replaced by table Contribution final expenditure to volume growth of GDP; National Accounts. For further information see section 3.

When will new figures be published? Not applicable anymore.

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 the researchers chose 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, researchers combined some subcategories. First, they were interested in total tax revenue, as well as the shares of total revenue coming from direct and indirect taxes. Further, they measured two sub-categories of direct taxation, namely taxes on property and income. For indirect taxes, they separated excises, consumption, and customs.

Foreign direct investment (FDI) is a category of investment that reflects the objective of establishing a lasting interest by a resident enterprise in one economy (direct investor) in an enterprise (direct investment enterprise) that is resident in an economy other than that of the direct investor. The lasting interest implies the existence of a long-term relationship between the direct investor and the direct investment enterprise and a significant degree of influence on the management of the enterprise. The lasting interest is deemed to exist if the investor acquires at least 10% of the voting power of the direct investment enterprise. Data are expressed as % of GDP to remove the effect of differences in the size of the economies of the reporting countries. FDI comprises: - Equity capital comprises equity in branches as well as all shares in subsidiaries and associates. - Reinvested earnings consist of the offsetting entry to the direct investor’s share of earnings not distributed as dividends by subsidiaries or associates, and earnings of branches not remitted to the direct investor and which are recorded under Investment income. - debt instruments Direct investment is classified primarily on a directional basis: 1) Resident direct investment abroad (Outward direct investment) 2) Non-resident investment in the reporting economy (Inward direct investment). The Inward direct investment is investment by a non-resident direct investor in a direct investment enterprise resident in the host economy; the direction of the influence by the direct investor is inward for the reporting economy. Starting from October 2014 definitions are based on the IMF's Sixth Balance of Payments Manual (BPM6). Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

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

Foreign direct investment (FDI) is a category of investment that reflects the objective of establishing a lasting interest by a resident enterprise in one economy (direct investor) in an enterprise (direct investment enterprise) that is resident in an economy other than that of the direct investor. The lasting interest implies the existence of a long-term relationship between the direct investor and the direct investment enterprise and a significant degree of influence on the management of the enterprise. The lasting interest is deemed to exist if the investor acquires at least 10% of the voting power of the direct investment enterprise. Data are expressed as % of GDP to remove the effect of differences in the size of the economies of the reporting countries. FDI comprises: - Equity capital comprises equity in branches as well as all shares in subsidiaries and associates. - Reinvested earnings consist of the offsetting entry to the direct investor’s share of earnings not distributed as dividends by subsidiaries or associates, and earnings of branches not remitted to the direct investor and which are recorded under Investment income. - Debt instruments Direct investment is classified primarily on a directional basis: 1) Resident direct investment abroad (Outward direct investment) 2) Non-resident investment in the reporting economy (Inward direct investment). The Inward direct investment is investment by a non-resident direct investor in a direct investment enterprise resident in the host economy; the direction of the influence by the direct investor is inward for the reporting economy. Starting from October 2014 definitions are based on the IMF's Sixth Balance of Payments Manual (BPM6). Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

The Gross Domestic Product per capita In the Euro Area was last recorded at 55973.92 US dollars in 2023, when adjusted by purchasing power parity (PPP). The GDP per Capita, In the Euro Area, when adjusted by Purchasing Power Parity is equivalent to 315 percent of the world's average. This dataset provides the latest reported value for - Euro Area GDP per capita PPP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Introduction

This study reproduces the results of the article Relationship of gender differences in preferences to economic development and gender equality (DOI: 10.1126/science.aas9899) and partially its supplementary material.

The code for the analysis can be found at the following GitHub page: https://github.com/scerioli/Global-Preferences-Survey

Preparation of the data

Data Collection, Cleaning, and Standardization

The data used in the Falk & Hermle 2018 is not fully available because of two reasons:

1. Data paywall: Some part of the data is not available for free. It requires to pay a fee to the Gallup to access them. This is the case for the additional data set that is used in the article, for instance, the one that contains the education level and the household income quintile. Check the website of the briq - Institute on Behavior & Inequality for more information on it.

2. Data used in study is not available online: This is what happened for the LogGDP p/c calculated in 2005 US dollars (which is not directly available online). We decided to calculate the LogGDP p/c in 2010 US dollars because it was easily available, which should not change the main findings of the article.

Global Preferences Survey

This data is protected by copyright and cannot be given to third parties.

To download the GPS data set, go to the website of the Global Preferences Survey in the section "downloads". There, choose the "Dataset" form and after filling it, we can download the data set.

Hint: The organisation can be also "private".

The following two relevant papers have to be also cited in all publications that make use of or refer in any kind to GPS dataset:

• Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., & Sunde, U. (2018). Global evidence on economic preferences. Quarterly Journal of Economics, 133 (4), 1645–1692.

• Falk, A., Becker, A., Dohmen, T. J., Huffman, D., & Sunde, U. (2016). The preference survey module: A validated instrument for measuring risk, time, and social preferences. IZA Discussion Paper No. 9674.

GDP per capita

From the website of the World Bank, one can access the data about the GDP per capita on a certain set of years. We took the GDP per capita (constant 2010 US$), made an average of the data from 2003 until 2012 for all the available countries, and matched the names of the countries with the ones from the GPS data set.

Gender Equality Index

The Gender Equality Index is composed of four main data sets.

• Time since women’s suffrage: Taken from the Inter-Parliamentary Union Website. We prepared the data in the following way. For several countries more than one date where provided (for example, the right to be elected and the right to vote). We use the last date when both vote and stand for election right were granted, with no other restrictions commented. Some counties were a colony or within union of the countries (for instance, Kazakhstan in Soviet Union). For these countries, the rights to vote and be elected might be technically granted two times within union and as independent state. In this case we kept the first date. It was difficult to decide on South Africa because its history shows the racism part very entangled with women's rights. We kept the latest date when also Black women could vote. For Nigeria, considered the distinctions between North and South, we decided to keep only the North data because, again, it was showing the completeness of the country and it was the last date. Note: USA data doesn't take into account that also up to 1964 black women couldn't vote (in general, Blacks couldn't vote up to that year). We didn’t keep this date, because it was not explicitly mentioned in the original data set. This is in contrast with other choices made, but it is important to reproduce exactly the results of the publication, and the USA is often easy to spot on the plots.

• UN Gender Inequality Index: Taken from the Human Development Report 2015. We kept only the table called "Gender Inequality Index".

• WEF Global Gender Gap: WEF Global Gender Gap Index Taken from the World Economic Forum Global Gender Gap Report 2015. For countries where data were missing, data was added from the World Economic Forum Global Gender Gap Report 2006. We modified some of the country names directly in the csv file, that is why we provide it as an input file.

• Ratio of female and male labour force participation: Average International Labour Organization estimates from 2003 to 2012 taken from the World Bank database (http://data.worldbank.org/indicator/SL.TLF.CACT.FM.ZS). Values were inverted to create an index of equality. We took the average for the period between 2004 and 2013.

In our extended analysis, we also involved the following index:

• United Nations Development Programme Gender Development Index taken from Human Development Reports 2020. Note that we have downloaded the two tables of the Human Development Index for males and females, and used the ratio of the two as a GDI index, as described in the report.

This paper presents a new nonlinear time series model that captures a post-recession bounce-back in the level of aggregate output. While a number of studies have examined this type of business cycle asymmetry using recession-based dummy variables and threshold models, we relate the bounce-back effect to an endogenously estimated unobservable Markov-switching state variable. When the model is applied to US real GDP, we find that the Markov-switching regimes are closely related to NBER-dated recessions and expansions. Also, the Markov-switching form of nonlinearity is statistically significant and the bounce-back effect is large, implying that the permanent effects of recessions are small. Meanwhile, having accounted for the bounce-back effect, we find little or no remaining serial correlation in the data, suggesting that our model is sufficient to capture the defining features of US business cycle dynamics. When the model is applied to other countries, we find larger permanent effects of recessions.

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 dataset provides values for GOVERNMENT 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.

In September 2024, the global PMI amounted to 47.5 for new export orders and 48.8 for manufacturing. The manufacturing PMI was at its lowest point in August 2020. It decreased over the last months of 2022 after the effects of the Russia-Ukraine war and rising inflation hit the world economy, and remained around 50 since.

This table contains information about tourist key indicators. Tourism contributes to the Dutch economy (contribution to employment, gross domestic product and value added). Tourism is not an industry in itself, but covers a wide range of products and services. The figures are consistent with the conceptual framework of the National Accounts (NA) and can be compared with existing macroeconomic indicators such as gross domestic product, the total value added and the total number of jobs in the Netherlands. The tourism accounts offer an integrated macroeconomic overview of the importance of tourism to the economy. Data available from: 2010 up to and including 2016. Status of the figures: Data from 2010 up to and including 2014 are final. Data of 2015 and 2016 are provisional. Since this table has been discontinued, data will not become final. Changes as of 29 August 2018: None. This table has been discontinued. Statistics Netherlands has carried out a revision of the national accounts. New statistical sources and estimation methods have been used during the revision. Therefore this table has been replaced by table Tourism; key indicators, National Accounts. For further information see section 3. When will new figures be published? Not applicable anymore.