The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in the United States expanded 2 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - United States GDP Annual Growth Rate - 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 United States expanded 3 percent in the second quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

The Gross Domestic Product per capita in the United States was last recorded at 66682.61 US dollars in 2024. The GDP per Capita in the United States is equivalent to 528 percent of the world's average. This dataset provides - United States GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Real gross domestic product per capita (A939RX0Q048SBEA) from Q1 1947 to Q1 2025 about per capita, real, GDP, and USA.

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).

Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)

Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.

Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).

Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf

Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318

Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.

World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators

CONTEXT

"The Gross Domestic Product per capita, or GDP per capita, is a measure of a country's economic output that accounts for its number of people. It divides the country's gross domestic product by its total population." - https://www.thebalance.com/gdp-per-capita-formula-u-s-compared-to-highest-and-lowest-3305848

CONTENT

• Year - Years from 1-2018 A.D.
• Afganistan...Zimbabwe - Country's GDP p.c.

FILE INFO

• GDP.csv - GDP p.c. by Country starting from year 1 in CSV File
• GDP.xlsx - GDP p.c. by Country starting from year 1 in XLSX File

On October 29, 1929, the U.S. experienced the most devastating stock market crash in it's history. The Wall Street Crash of 1929 set in motion the Great Depression, which lasted for twelve years and affected virtually all industrialized countries. In the United States, GDP fell to it's lowest recorded level of just 57 billion U.S dollars in 1933, before rising again shortly before the Second World War. After the war, GDP fluctuated, but it increased gradually until the Great Recession in 2008. Real GDP Real GDP allows us to compare GDP over time, by adjusting all figures for inflation. In this case, all numbers have been adjusted to the value of the US dollar in FY2012. While GDP rose every year between 1946 and 2008, when this is adjusted for inflation it can see that the real GDP dropped at least once in every decade except the 1960s and 2010s. The Great Recession Apart from the Great Depression, and immediately after WWII, there have been two times where both GDP and real GDP dropped together. The first was during the Great Recession, which lasted from December 2007 until June 2009 in the US, although its impact was felt for years after this. After the collapse of the financial sector in the US, the government famously bailed out some of the country's largest banking and lending institutions. Since recovery began in late 2009, US GDP has grown year-on-year, and reached 21.4 trillion dollars in 2019. The coronavirus pandemic and the associated lockdowns then saw GDP fall again, for the first time in a decade. As economic recovery from the pandemic has been compounded by supply chain issues, inflation, and rising global geopolitical instability, it remains to be seen what the future holds for the U.S. economy.

Recommended citation

Article citation will be added once the article is available.

Content

• Use of the dataset and full description
• Abstract
• Support
• Files included in the dataset
• Notes
• Data format description (columns)
• Data sources
• Changelog
• References

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section.

The article will be referenced here as soon as it is published.

Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below.

Support

If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.

Abstract

This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data.

Files included in the dataset

The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows:

The "Source" flag indicates which input scenarios were used.

• PMRCP: RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.
• PMSSP: Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.

the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed.

The "Downscaling" flag indicates the downscaling technique used.

• IE: Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.
• IC: Regional emission intensity growth rates for all countries.
• CS: Constant emission shares as a reference case independent of the socio-economic scenario.

All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.

Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf.

Notes

If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data.

Data format description (columns)

"source"

For PMRCP files source values are

• RCPSSP
• PMRCP
• PMRCPMISC

For PMSSP files source values are

• SSPIAM
• PMSSP
• PMSSPMISC

For possible values of

"scenario"

For PMRCP files the scenarios have the format

For PMSSP files the scenarios have the format

Model codes in scenario names

• AIMCGE: AIM-CGE
• IMAGE: IMAGE
• GCAM4: GCAM
• MESGB: MESSAGE-GLOBIOM
• REMMP: REMIND-MAGPIE
• WITGB: WITCH-GLOBIOM

"country"

ISO 3166 three-letter country codes or custom codes for groups:

Additional "country" codes for country groups.

• EARTH: Aggregated emissions for all countries
• ANNEXI: Annex I Parties to the UNFCCC
• NONANNEXI: Non-Annex I Parties to the UNFCCC
• AOSIS: Alliance of Small Island States
• BASIC: BASIC countries (Brazil, South Africa, India and China)
• EU28: European Union (still including the UK)
• LDC: Least Developed Countries
• UMBRELLA: Umbrella Group

"category"

Category descriptions.

• IPCM0EL: Emissions: National Total excluding LULUCF
• ECO: Economical data
• DEMOGR: Demographical data

"entity"

Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4).

Gases / gas baskets and underlying global warming potentials

• CH4: Methane (CH₄)
• CO2: Carbon Dioxide (CO₂)
• N2O: Nitrous Oxide (N₂O)
• FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF₆, NF₃
• FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF₆, NF₃
• KYOTOGHG: Kyoto greenhouse gases (SAR)
• KYOTOGHGAR4: Kyoto greenhouse gases (AR4)

"unit"

The following units are used:

• Million2011GKD: Million 2011 international dollars
• ThousandPers: Thousand persons
• kt: kilotonnes
• Mt: Megatonnes
• Gg: Gigagrams
• MtCO2eq: Megatonnes of CO₂ equivalents using the GWPs defined by "entity"
• GgCO2eq: Gigagrams of CO₂ equivalents using the GWPs defined by "entity"

Remaining columns

Years from 1850-2100.

Data Sources

The following data sources were used during the generation of this dataset:

Scenario data

• RCP scenarios website/data
• SSP basic elements website/data
• SSP IAM scenarios website/data
• SSP CMIP6 scenarios website/data

Historical data

• CDIAC data
• CEDS CMIP6 data paper/data
• EDGAR version 4.3.2: data, paper
• IMO GHG report report
• PRIMAP-hist v2.1 paper, <a

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.

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;

🔗 Check out my notebook here: Link

This dataset includes malnutrition indicators and some of the features that might impact malnutrition. The detailed description of the dataset is given below:

• Percentage-of-underweight-children-data: Percentage of children aged 5 years or below who are underweight by country.

• Prevalence of Underweight among Female Adults (Age Standardized Estimate): Percentage of female adults whos BMI is less than 18.

• GDP per capita (constant 2015 US$): 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.

• Domestic general government health expenditure (% of GDP): Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

• Maternal mortality ratio (modeled estimate, per 100,000 live births): Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

• Mean-age-at-first-birth-of-women-aged-20-50-data: Average age at which women of age 20-50 years have their first child.

• School enrollment, secondary, female (% gross): Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.

The United States recorded a Government Debt to GDP of 124.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - United States Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar 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.

The purpose of this data set is to allow exploration between various types of data that is commonly collected by the US government across the states and the USA as a whole. The data set consists of three different types of data:

• Census and Geographic Data;
• Energy Data; and
• Economic Data.

When creating the data set, I combined data from many different types of sources, all of which are cited below. I have also provided the fields included in the data set and what they represent below. I have not performed any research on the data yet, but am going to dive in soon. I am particularly interested in the relationships between various types of data (i.e. GDP or birth rate) in prediction algorithms. Given that I have compiled 5 years’ worth of data, this data set was primarily constructed with predictive algorithms in mind.

An additional note before you delve into the fields: * There could have been many more variables added across many different fields of metrics. I have stopped here, but it could potentially be beneficial to observe the interaction of these variables with others (i.e. the GDP of certain industries, the average age in a state, the male/female gender ratio, etc.) to attempt to find additional trends.

Census and Geographic Data

• StateCodes: The state 2-letter abbreviations. Note that I added "US" for the United States.
• Region: The number corresponding to the region the state lies within, according to the 2010 census. (1 = Northeast, 2 = Midwest, 3 = South, 4 = West)
• Division: The number corresponding to the division the state lies within, according to the 2010 census. (1 = New England, 2 = Middle Atlantic, 3 = East North Central, 4 = West North Central, 5 = South Atlantic, 6 = East South Central, 7 = West South Central, 8 = Mountain, 9 = Pacific)
• Coast: Whether the state shares a border with an ocean. (1 = Yes, 0 = No)
• Great Lakes: Whether the state shares a border with a great lake. (1 = Yes, 0 = No
• CENSUS2010POP: 4/1/2010 resident total Census 2010 population
• POPESTIMATE{year}: 7/1/{year} resident total population estimate
• RBIRTH{year}: Birth rate in period 7/1/{year - 1} to 6/30/
• RDEATH{year}: Death rate in period 7/1/{year - 1} to 6/30/
• RNATURALINC{year}: Natural increase rate in period 7/1/{year - 1} to 6/30/
• RINTERNATIONALMIG{year}: Net international migration rate in period 7/1/{year - 1} to 6/30/
• RDOMESTICMIG{year}: Net domestic migration rate in period 7/1/{year - 1} to 6/30/
• RNETMIG{year}: Net migration rate in period 7/1/{year - 1} to 6/30/

As noted from the census:

Net international migration for the United States includes the international migration of both native and foreign-born populations. Specifically, it includes: (a) the net international migration of the foreign born, (b) the net migration between the United States and Puerto Rico, (c) the net migration of natives to and from the United States, and (d) the net movement of the Armed Forces population between the United States and overseas. Net international migration for Puerto Rico includes the migration of native and foreign-born populations between the United States and Puerto Rico.

Codes for most of the data, information about the geographic terms and coditions, and more information about the methodology behind the population estimates can be found on the US Census website.

Energy Data

• TotalC{year}: Total energy consumption in billion BTU in given year.
• TotalP{year}: Total energy production in billion BTU in given year.
• TotalE{year}: Total Energy expenditures in million USD in given year.
• TotalPrice{year}: Total energy average price in USD/million BTU in given year.
• TotalC{first year}–{second year}: The first year’s total energy consumption divided by the second year’s total energy consumption, times 100. (The percent change between years in total energy consumption.)
• TotalP{first year}–{second year}: The first year’s total energy production divided by the second year’s total energy production, times 100. (The percent change between years in total energy production.)
• TotalE{first year}–{second year}: The first year’s total energy expenditure divided by the second year’s total energy expenditure, times 100. (The percent change between years in total energy expenditure.)
• TotalPrice{first year}–{second year}: The first year’s total energy average price divided by the second year’s total energy average price, times 100. (The percent change between years in total energy average price.)
• BiomassC{year}: Biomass total consumption in billion BTU in given year.
• CoalC{year}: Coal total consumption in billion BTU in given year.
• CoalP{year}: Coal total production in billion BTU in given year.
• CoalE{year}: Coal total expenditures in million USD in given year.
• CoalPrice{year}:...

We have successfully extracted a comprehensive news dataset from CNBC, covering not only financial updates but also an extensive range of news categories relevant to diverse audiences in Europe, the US, and the UK. This dataset includes over 500,000 records, meticulously structured in JSON format for seamless integration and analysis.

Diverse News Segments for In-Depth Analysis

This extensive extraction spans multiple segments, such as:

• Business and Market Analysis: Stay updated on major companies, mergers, and acquisitions.
• Technology and Innovation: Explore developments in AI, cybersecurity, and digital transformation.
• Economic Forecasts: Access insights into GDP, employment rates, inflation, and other economic indicators.
• Geopolitical Developments: Understand the impact of political events and global trade dynamics on markets.
• Personal Finance: Learn about saving strategies, investment tips, and real estate trends.

Each record in the dataset is enriched with metadata tags, enabling precise filtering by region, sector, topic, and publication date.

Why Choose This Dataset?

The comprehensive news dataset provides real-time insights into global developments, corporate strategies, leadership changes, and sector-specific trends. Designed for media analysts, research firms, and businesses, it empowers users to perform:

• Trend Analysis
• Sentiment Analysis
• Predictive Modeling

Additionally, the JSON format ensures easy integration with analytics platforms for advanced processing.

Access More News Datasets

Looking for a rich repository of structured news data? Visit our news dataset collection to explore additional offerings tailored to your analysis needs.

Sample Dataset Available

To get a preview, check out the CSV sample of the CNBC economy articles dataset.

Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the undesirable effects of wildfires increases with the understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the contiguous United States (CONUS). FPA FOD v6 contains information on the location, jurisdiction, discovery time, cause, and final size of >2.2 million wildfires from 1992-2020 in CONUS. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.

Title:
The Invisible Art Market: Informal Networks, Symbolic Capital, and Market Activity in Latin America

DOI:
10.5281/zenodo.15383517

Creators:
Brown, Scott (University of Puerto Rico – Río Piedras, Professor of Finance)

Description:
This dataset supports the empirical analysis presented in “The Invisible Art Market: Informal Networks, Symbolic Capital, and Market Activity in Latin America.” The study investigates how weak institutional frameworks, symbolic capital, and informal networks shape national art market development, with a special focus on BRIC countries and Latin America.

The dataset integrates four key sources:

• Art Sales Data (2021–2024) across Brazil, Russia, India, and China (BRIC) compiled from Artprice and Art Basel reports.

• Macroeconomic indicators from the World Bank’s GDP data.

• Institutional quality metrics using the International Property Rights Index (IPRI) and V-Dem rule of law variables.

A regression analysis (replicable via the art_market_brics.py Python script) demonstrates that stronger property rights (IPRI) and higher GDP are statistically associated with increased national art sales, even in non-Western economies. The data and code comply with FAIR principles and are structured for open replication and policy use.

Files included:

• art_market_brics.py (Python code to replicate regression models)

• BRIC_Art_Sales_2021_2024.csv (manually extracted and cleaned auction turnover data)

• GDP.csv (World Bank GDP data 2021–2024)

• IPRI_Country_Tables_Manual.xlsx (Institutional property rights index data, 2024)

• vdem_variables_filtered_1996_onward.xlsx (Governance data on Rule of Law from V-Dem)

Keywords:
art market, cultural economics, symbolic capital, institutional economics, BRIC countries, Latin America, intellectual property rights, informal economy, governance, development policy, Hernando de Soto

Subjects:

• Economics and Econometrics

• Cultural Studies

• Development Studies

• Public Policy

• Law and Society

License:
Creative Commons Attribution 4.0 International (CC BY 4.0)

Language:
English

Version:
1.0

Publication Date:
2025-05-13

Publisher:
Zenodo

Funding:
None (institutionally unfunded, conducted at a public university under resource constraints)

Related Works:
This dataset underpins the paper:
Brown, S. M. (2025). The Invisible Art Market: Symbolic Capital, Informal Institutions, and Development Constraints in Latin America [Manuscript in preparation].