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

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 dataset is about books. It has 1 row and is filtered where the book is Political economy past and present : a review of leading theories of economic policy. It features 7 columns including author, publication date, language, and book publisher.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Economy, PA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 33,587, while the mean income for the highest quintile (20% of households with the highest income) is 287,506. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 474,140, which is 164.91% higher compared to the highest quintile, and 1411.68% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Economy median household income. You can refer the same here

Brazil BR: Income Share Held by Highest 10% data was reported at 41.000 % in 2022. This records a decrease from the previous number of 41.600 % for 2021. Brazil BR: Income Share Held by Highest 10% data is updated yearly, averaging 44.550 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 51.100 % in 1989 and a record low of 39.500 % in 2020. Brazil BR: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

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.

Time series data for the statistic Real_GDP_Per_Capita_Constant_2015_USD and country Peru. Indicator Definition:GDP per capita is gross domestic product divided by midyear population. GDP 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. Data are in constant 2015 U.S. dollars.The statistic "Real GDP Per Capita Constant 2015 USD" stands at 6,711.19 United States Dollars as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.18 percent compared to the value the year prior.The 1 year change in percent is 2.18.The 3 year change in percent is 2.49.The 5 year change in percent is 1.28.The 10 year change in percent is 9.95.The Serie's long term average value is 4,088.41 United States Dollars. It's latest available value, on 12/31/2024, is 64.15 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1992, to it's latest available value, on 12/31/2024, is +154.18%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).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 more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This dataset compiles 133 academic articles from twelve leading economics journals that discuss "technological unemployment." It provides a comprehensive view of how economists have conceptualized the relationship between technological change and employment over time.

The articles were collected through a systematic search for the term "technological unemployment" across the full text of influential economics publications, including the "top five" journals (American Economic Review, Journal of Political Economy, Quarterly Journal of Economics, Review of Economic Studies, and Econometrica), five general interest journals (Economic Journal, Review of Economics and Statistics, Annual Review of Economics, Journal of Economic Perspectives, and Journal of Economic Surveys), and two field journals (Labour Economics and Economics of Innovation and New Technology). The collection spans from 1928 to recent publications.

After identifying 153 initial articles, we conducted a manual review to exclude non-substantive pieces, book reviews, and conference programs, resulting in the final corpus of 133 articles. Each article was then hand-coded along multiple dimensions:

• Contribution type: Theoretical, Empirical, or Survey/Review
• Theoretical possibility: Whether technological unemployment is considered possible in theory
• Empirical occurrence: Whether technological unemployment is observed to have occurred in practice
• Examples: Specific instances of technologically-induced job loss cited by authors
• Temporal dimension: Whether effects are classified as permanent or temporary
• Scale dimension: Whether effects are discussed at aggregate, sectoral/occupational, or individual levels

The detailed coding methodology is available in the Appendix of our paper "What is Technological Unemployment?". The dataset facilitates quantitative and qualitative analysis of how this concept has been treated in mainstream economics literature across different historical periods of technological change. More generally, it offers valuable resources for researchers studying the historical evolution of economic thought or patterns in academic concerns.

When using this dataset, please cite: Schneider, B., & Küsters, A. (2025). Dataset: Technological Unemployment in Leading Economics Journals [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15011373.
For the complete analysis, refer to our (working) paper: Küsters, A., & Schneider, B. (2025). What is Technological Unemployment?

The dataset has been created from obtaining post-pilot answers from 106 participants of four different countries in the EU (the questionnaire can be studied in GreenSoul_Validation_Questionnaire-POST.pdf). It is composed by several factors which are explained in POST-coding.ods file. All these factors are contained in: "POST-results-socio-economic-model.ods" and "POST-results-treatments-evaluation.ods" along with their answers by participants. Finally, we provided a cleaned version of the dataset to study how can a researcher is able to forecast the ranking that a user will give to different persuasion strategies according to user profiles: "POST-results-ranking-model.ods"

This U.S. Geological Survey (USGS) data release provides the descriptions of the only U.S. sites—including mineral regions, mineral occurrences, and mine features—that have reported production and (or) resources of tantalum (Ta). The sites in this data release have contained resource and (or) past production of more than 900 metric tons Ta metal, which was the approximate average annual consumption of Ta in the U.S. from 2016 through 2020. This dataset contains the Bokan Mountain deposit in Alaska and the Round Top deposit in Texas. Tantalum primarily occurs in the mineral tantalite, which may be found in carbonatites, alkaline granite-syenite complexes, and lithium-cesium-tantalum (LCT) pegmatites. The largest Ta deposits can be found in Australia, where the Greenbushes and Wodgina Mines have been producing Ta from pegmatites since the late 1880s. The Greenbushes is an LCT pegmatite deposit that contains more than 135 million metric tons of ore with an average grade of 0.022 percent Ta2O5. The Wodgina LCT pegmatite deposit contains more than 85 million metric tons of ore at a grade of 0.032 percent Ta2O5 (Schulz and others, 2017). In comparison, the largest Ta deposit in the U.S. is the Round Top deposit in Texas, which has reported resources of more than 480 million metric tons with an average grade of 67.2 grams per metric ton Ta2O5 (Hulse and others, 2019). There are no current U.S. producers of Ta. Tantalum is necessary for strategic, consumer, and commercial applications. Tantalum is highly conductive to heat and electricity and known for its resistance to acidic corrosion, thereby making this metal an ideal component for electronic capacitors, telecommunications, data storage, and implantable medical devices. In 2020, the U.S. was 100 percent net import reliant on Ta from countries such as China, Germany, Australia, and others. Tantalum is imported to the U.S. as ore and concentrate, metal and powder, as well as waste and scrap (U.S. Geological Survey, 2021). The entries and descriptions in the database were derived from published papers, reports, data, and internet documents representing a variety of sources, including geologic and exploration studies described in State, Federal, and industry reports. Resources extracted from older sources might not be compliant with current rules and guidelines in minerals industry standards such as National Instrument 43-101 (NI 43-101). The presence of a Ta mineral deposit in this database is not meant to imply that the deposit is currently economic. Rather, these deposits were included to capture the characteristics of the largest Ta deposits in the United States. Inclusion of material in the database is for descriptive purposes only and does not imply endorsement by the U.S. Government. The authors welcome additional published information in order to continually update and refine this dataset. Hulse, D.E., Malhotra, D., Matthews, T., and Emanuel, C., 2019, NI 43-101 preliminary economic assessment Round Top project, Sierra Blanca, Texas, prepared for USA Rare Earth LLC and Texas Mineral Resources Corp. [Filing Date July 1, 2019]: Gustavson Associates, LLC, 218 p., accessed October 17, 2019, at http://usarareearth.com/. Schulz, K.J., Piatak, N.M., and Papp, J.F., 2017, Niobium and tantalum, chap. M of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. M1–M34, https://doi.org/10.3133/pp1802M. U.S. Geological Survey, 2021, Mineral commodity summaries 2021: U.S. Geological Survey, 200 p., https://doi.org/10.3133/mcs2021.

The average for 2024 based on 138 countries was 5.56 points. The highest value was in Finland: 7.74 points and the lowest value was in Afghanistan: 1.72 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

This dataset includes important economic, demographic and sport data related to Summer Olympic games in Rio de Janeiro 2016. Dataset includes variables such as: GDP, GDP per capita, Inflation, Population total, Population 15-64, Economic Active Population, Corruption Perception Index, Medal rankings, and World Bank's country classification by income. Dataset can be used for any Rio 2016 Olympic games related analysis and any classical economic models.

Time series data for the statistic Real_GDP_Per_Capita_Constant_2015_USD and country Croatia. Indicator Definition:GDP per capita is gross domestic product divided by midyear population. GDP 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. Data are in constant 2015 U.S. dollars.The statistic "Real GDP Per Capita Constant 2015 USD" stands at 17,770.87 United States Dollars as of 12/31/2024, the highest value at least since 12/31/1991, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 3.64 percent compared to the value the year prior.The 1 year change in percent is 3.64.The 3 year change in percent is 15.44.The 5 year change in percent is 21.38.The 10 year change in percent is 49.79.The Serie's long term average value is 11,355.18 United States Dollars. It's latest available value, on 12/31/2024, is 56.50 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1993, to it's latest available value, on 12/31/2024, is +173.81%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

Time series data for the statistic Gross_Domestic_Product_Current_USD and country China. Indicator Definition: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. 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.The statistic "Gross Domestic Product Current USD" stands at 18,743,803,170,827.20 United States Dollars as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.59 percent compared to the value the year prior.The 1 year change in percent is 2.59.The 3 year change in percent is 2.98.The 5 year change in percent is 28.73.The 10 year change in percent is 75.59.The Serie's long term average value is 3,590,131,888,959.60 United States Dollars. It's latest available value, on 12/31/2024, is 422.09 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1962, to it's latest available value, on 12/31/2024, is +39,518.50%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

Attitudes to current national and internation questions. Topics: judgement on current situation in one's country, the USSR, the USA, China, Japan, Great Britain, France, Israel, Egypt, Chile, Kuba, Brasil; judgement on future development in one's country, the USSR, the USA, China and Japan; effort for world peace; opinion on USSR, USA and China; respect for USSR, USA, China and Japan; conduct of USSR, USA and China on international questions; agreement of fundamental interests of FRG and USSR; common interests of FRG, USA and Japan; efforts of USSR, USA and China for peace; trust in ability of USSR, USA, China and Japan to solve world problems; strongest nation currently and in future; strongest military power currently and in future; strongest economic power currently and in future; development standard of USSR, Japan and China; development of USA; trust in USA regarding fundamental interests of Germany; influence of USA on European affairs; influence of USA in international affairs; China policies of USSR; China policies of USA. Demography: country; age; education; sex. Einstellungen zu aktuellen nationalen und internationalen Fragen. Themen: Beurteilung der gegenwärtigen Lage im eigenen Land, in der UdSSR, in den USA, in China, in Japan, in Großbritannien, in Frankreich, in Israel, in Ägypten, in Chile, in Kuba, in Brasilien; Beurteilung der zukünftigen Entwicklung im eigenen Land, in der UdSSR, in den USA, in China und in Japan; Bemühen um den Weltfrieden; Meinung über die UdSSR, die USA und China; Achtung vor der UdSSR, den USA, China und Japan; Verhalten der UdSSR, USA und Chinas bei internationalen Fragen; Übereinstimmung grundlegender Interessen der BRD und der UdSSR; gemeinsame Interessen der BRD, der USA und Japans; Bemühungen der UdSSR, der USA und Chinas um Frieden; Vertrauen in die Fähigkeit der UdSSR, USA, Chinas und Japans Weltprobleme zu lösen; stärkste Nation derzeit und in Zukunft; stärkste militärische Kraft derzeit und in Zukunft; stärkste Wirtschaftsmacht derzeit und in Zukunft; Entwicklungsstandard der UdSSR, Japans und Chinas; Entwicklung der USA; Vertrauen in die USA in Bezug auf grundlegende Interessen Deutschlands; Einflußnahme der USA auf europäische Angelegenheiten; Einflußnahme der USA in internationale Angelegenheiten; China-Politik der UdSSR; China-Politik der USA. Demographie: Land; Alter; Bildung; Geschlecht.

To reconcile global sustainability goals, such as protecting biodiversity and the climate, with agricultural production, a spatial understanding of potential future cropland expansion and potentially resulting trade-offs is required. Assuming that the globally most profitable land for cropland expansion is also under the highest pressure to be converted into cropland, we provide a global dataset on the areas under globally highest expansion pressure until 2030 considering future socio-economic and environmental conditions.

Using the integrative land-use change model iLANCE (integrative land-allocation sequencer), the relative profitability of cropland expansion is assessed globally at 0.5° spatial resolution. Thereby, future environmental conditions for crop growth (under SSP585) are considered by the crop model PROMET. Socio-economic drivers of land-use change, regional economic conditions and global trade are taken into account by the Computable General Equilibrium model DART-BIO. Thereon based, the areas under the globally highest expansion pressure up to a global cropland increase of +30% (as an upper benchmark) are identified. The data on the area under expansion pressure at each pixel is provided in km² at 0.5° spatial resolution. Based on the relative profitability ranking, we provide additional spatial data at 0.5° spatial resolution indicating the percentage of global cropland expansion under which each pixel is among the globally most profitable ones (from 1% to 30% global cropland expansion). Accordingly, by overlaying both datasets, various scenarios of an increase in future cropland extent from 1% to 30% global cropland expansion can be investigated.

The areas under highest expansion pressure are assessed without any restrictions on cropland expansion (EXP scenario) and under a conservation policy scenario that prohibits cropland expansion into forests, wetlands and strictly protected areas (CON scenario), thereby reflecting key aims of the Sustainable Development goals and recent efforts to stop deforestation, protect the climate and preserve biodiversity.

Additionally, information on the area under expansion pressure under both scenarios, EXP and CON, is provided in km² at country level for different global cropland expansion scenarios from 1% to 30% (in 1% increments).

The provided data could be used in integrated assessment models or impact studies to investigate various potential effects of different future cropland expansion scenarios, for example regarding biodiversity, climate, hydrology, local or regional agricultural production or socio-economic effects. In the study associated with this dataset, potential impacts on agricultural markets, biodiversity intactness and carbon storage are assessed.

Information on the spatial patterns of future expansion pressure and resulting trade-offs as well as co-benefits could contribute to improving the spatial planning of conservation measures and to creating more efficient conservation policies.

Further information:

A detailed description on the methods and underlying data is available in:

Schneider, J.M., Delzeit, R., Neumann, C., Heimann, T., Seppelt, R., Schuenemann, F., Söder, M., Mauser, W., Zabel, F. (2024): Effects of profit-driven cropland expansion and conservation policies. Nature Sustainability.

https://doi.org/10.1038/s41893-024-01410-x

Contact:

Please contact: Julia M. Schneider (Schneider.ju@lmu.de), Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.

or

Florian Zabel (florian.zabel@unibas.ch), Departement of Environmental Sciences, University of Basel, Basel, Switzerland.

Funding:

This project was supported by the German Federal Ministry of Education and Research (grant 031B0230B and grant 031B0788B).

Time series data for the statistic Gross_Domestic_Product_Current_USD and country Azerbaijan. Indicator Definition: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. 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.The statistic "Gross Domestic Product Current USD" stands at 74,315,882,352.94 United States Dollars as of 12/31/2024. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.61 percent compared to the value the year prior.The 1 year change in percent is 2.61.The 3 year change in percent is 35.55.The 5 year change in percent is 54.26.The 10 year change in percent is -1.23.The Serie's long term average value is 31,934,962,021.53 United States Dollars. It's latest available value, on 12/31/2024, is 132.71 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1992, to it's latest available value, on 12/31/2024, is +16,613.02%.The Serie's change in percent from it's maximum value, on 12/31/2022, to it's latest available value, on 12/31/2024, is -5.70%.

Time series data for the statistic Gross_Domestic_Product_Current_USD and country Papua New Guinea. Indicator Definition: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. 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.The statistic "Gross Domestic Product Current USD" stands at 32,538,480,024.00 United States Dollars as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 5.59 percent compared to the value the year prior.The 1 year change in percent is 5.59.The 3 year change in percent is 24.62.The 5 year change in percent is 31.47.The 10 year change in percent is 40.19.The Serie's long term average value is 7,834,603,378.16 United States Dollars. It's latest available value, on 12/31/2024, is 315.32 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1960, to it's latest available value, on 12/31/2024, is +14,016.72%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.