The Gross Domestic Product (GDP) in European Union was worth 19423.32 billion US dollars in 2024, according to official data from the World Bank. The GDP value of European Union represents 18.29 percent of the world economy. This dataset provides the latest reported value for - European Union GDP - 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.

In this paper the authors conduct a meta-analysis to examine the link between R&D spending and economic growth in the EU and other regions. The results suggest that the growth-enhancing effect of R&D in the EU15 countries does not differ from that in other countries in general, but it is less significant than that for other industrialized countries. A closer inspection of the data reveals that the weak results for the EU15 stem from comparisons with the US – the US has been able to generate a stronger growth response from its R&D spending. Possible explanations for the US advantage include higher private sector investment in R&D and stronger public-private sector linkages than in the EU. Hence, to reduce the “innovation gap” vis-à-vis the US, it may not be enough for the EU to raise the share of R&D expenditures in GDP: continuous improvements in the European innovation system will also be needed, with focus on areas like private sector R&D and public-private sector linkages.

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.

This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Spatial extent This subdataset covers the South West CONUS, i.e.

AZ

NM

NV

TX

For the remaining CONUS, see the related identifiers.

Temporal extent The map is representative for ca. 2018.

Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.

Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).

Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.

t at 10m x 10m

kt at 100m x 100m

Mt at 1km x 1km

Gt at 10km x 10km

For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming.

Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv.

Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers):

A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337.

Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep

Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.

Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Since the oil price shock in 1974 unemployment increased significantly and also did not really decline in periods of economic upswings in Europe. This is especially the case for the countries of the European Union; therefore we face a special need for explanation. Looking at the member states on finds considerable differences. Since 1977 the unemployment rate within the EU is higher than the average unemployment rate of all OECD countries. The economic upswing in the second half of the 80s relaxed the labor market but nevertheless the unemployment rate remained on a high level. This study deals with the development of unemployment between 1974 and 1993 in four different G7 countries: Germany, France, Great Britain and Italy. Besides the common trend of an increasing unemployment rate, there are significantly different developments within the four countries. The analysis is divided in two parts: the first part looks at the reasons for the increase in unemployment in the considered countries; the second part aims to explain the difference between the developments of unemployment during economic cycles in the different countries. After the description of similarities and differences of labor markets in the four countries it follows a long term analysis based on annual data as well as a short and medium term analysis on quarterly data. This is due to the fact that short and medium term developments are mainly influenced by cyclical economic developments but long term developments are mainly influenced by other factors like demographical and structural changes. A concrete question within this framework is if an increase in production potential can contribute to a decrease in unemployment. For the long term analysis among others the Hysteresis-hypothesis (Hysteresis = Greek: to remain; denotes the remaining effect; in this context: remaining of unemployment) used for the explanation of the persistence of a high unemployment rate. According to this approach consisting unemployment is barely decreased after economic recovery despite full utilization of capacity. According to the Hysteresis-hypothesis there are two reasons for this. The first reason is that for long term unemployed the abilities to work and the qualification level decreased, their human capital is partly devalued. The second reason is that employees give up wage restraint, because they do not fear unemployment anymore and therefore enforce higher real wages. Besides economic recovery companies are not willing to hire long term unemployed with a lower expected productivity for the higher established tariff wages. In the context of the empirical investigation a multiple explanatory approach is chosen which takes supply side and demand side factors into consideration. The short and medium term analysis refers to Okun´s law (=an increase in the unemployment rate is connected with a decrease of the GDP; if the unemployment rate stays unchanged, the GDP grows with 3% p.a.) and aims to analyze more detailed the reactions of unemployment to economic cycles. A geometrical lag-model is compared with a lag-model ager Almon. This should ensure a precise as possible analysis of the Okun´s relations and coefficients. Register of tables in HISTAT: A.: Unemployment in the European G7 countries B.: Analysis of unemployment in the Federal Republic of Germany C.: Basic numbers: International comparison A.: Unemployment in the European G7 countries A.1. Determinates of unemployment in the EU, Germany (1974-1993) A.2. Determinates of unemployment in the EU, France (1974-1993) A.3. Determinates of unemployment in the EU, Great Britain (1974-1993) A.4. Determinates of unemployment in the EU, Italy (1974-1993) B: Analysis of unemployment in the Federal Republic of Germany B.1. Growth of unemployment in the Federal Republic of Germany (1984-1991) B.2. Output and unemployment in the Federal Republic of Germany (1961-1990) C: Basic numbers: International comparison C.1. Unemployment in EU countries, the USA, Japan and Switzerland (1960-1996) C.2. Gainful employments in EU countries, the USA, Japan and Switzerland (after inland and residency concept) (1960-1996) C.3. Employees in EU countries, the USA and Japan (1960-1996) C.4. Population in EU countries, the USA and Japan (1960-1996)

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.

This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Spatial extent This subdataset covers the Great Plains CONUS, i.e.

KS

ND

NE

OK

SD

For the remaining CONUS, see the related identifiers.

Temporal extent The map is representative for ca. 2018.

Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.

Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).

Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.

t at 10m x 10m

kt at 100m x 100m

Mt at 1km x 1km

Gt at 10km x 10km

For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming.

Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv.

Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers):

A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337.

Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep

Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.

Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

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.

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.