This dataset captures the relationship between home cost and average income across geographic regions of the United States over time. Ultimately I intend to augment this dataset with relevant economic indicators from Federal Reserve Economic Data (FRED) to better elucidate insights about the US Real Estate Market, the status of the US Economy, and the interaction between these constructs.

Alaska economic regions based on 2013 borough and census area geography. Boundaries are determined by the Alaska Department of Labor & Workforce Development. For more information, see Alaska Department of Labor Maps & GISThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data.

This dataset presents statistics for Construction: Value of Business Done for Kind-of-Business for the U.S., Regions, and States

The BEA regional economic accounts provide a wealth of statistics that detail the geographic distribution of U.S. economic activity and growth and provide a consistent framework for analyzing and comparing individual state and local area economies. Employment, compensation, wages and salaries, personal current transfer receipts, personal current taxes, and per capita personal income statistics are also available.

Graph and download economic data for Gross Domestic Product: State and Local in the New England BEA Region (NENGGOVSLNGSP) from 1997 to 2024 about New England BEA Region, state & local, GSP, government, industry, GDP, and USA.

This study provides employment estimates for a number of different fields. Using Standard Industrial Classification (SIC) designations and an aggregate of the SIC codes, data are presented by county and Bureau of Economic Analysis (BEA) economic regions, which are aggregates of counties. The BEA economic regions data files are categorized further by Metropolitan Statistical Areas and non-Metropolitan Statistical Areas. Industrial categories covered include agricultural services, eating and drinking places, and health services.

Graph and download economic data for Per Capita Personal Income in the Far West BEA Region (BEAFWPCPI) from 1929 to 2024 about Far West BEA Region, personal income, per capita, personal, income, and USA.

United States US: Built Up Area: % of Total Land data was reported at 1.790 % in 2014. This records an increase from the previous number of 1.500 % for 2000. United States US: Built Up Area: % of Total Land data is updated yearly, averaging 1.500 % from Dec 1990 (Median) to 2014, with 3 observations. The data reached an all-time high of 1.790 % in 2014 and a record low of 1.170 % in 1990. United States US: Built Up Area: % of Total Land data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.GGI: Environmental: Land Resources: OECD Member: Annual.

These data represent an update to the dataset, "State IO Two-Region Economic Input-Output Models for 50 U.S. States 2012-2017" based on the methods described by Li et al. (2022). They are an update to that dataset published with an expanded time series. These models were produced with the stateior R package, v0.4.0. Excel files (50 in total) are provided for two region (State of Interest and Rest of U.S.) Make and Use tables for each U.S. State IO model for years 2012-2023. Additional data files supporting this release including all intermediate and final products in native R format (.RDS) and can be opened directly in R software or through the stateior package. See the stateior github page for more details. https://dmap-data-commons-ord.s3.amazonaws.com/index.html#stateio/ All values are in current dollar years (e.g "Make 2012" is the Make table in 2012 USD in a given model). For a description of the methods used and survey of results see the Addendum 1 on the EPA Science Inventory page for the original publication. Please cite this dataset as: Young, Ben, Julie Chen, Jorge Vendries, and Wesley Ingwersen. 2025. “StateIO v0.4.0 Two-Region Economic Input-Output Models for 50 U.S. States: 2012-2023.” Data.gov. https://doi.org/10.23719/1532211. This dataset is associated with the following publication: Li, M., J. Ferreira, C.D. Court, D. Meyer, M. Li, and W.W. Ingwersen. StateIO - Open Source Economic Input-Output Models for the 50 States of the United States of America. International Regional Science Review. SAGE Publications, THOUSAND OAKS, CA, USA, 46(4): 428-481, (2023).

United States CPI U: Northeast: Size Class B/C data was reported at 156.752 Dec1996=100 in Oct 2018. This records a decrease from the previous number of 156.961 Dec1996=100 for Sep 2018. United States CPI U: Northeast: Size Class B/C data is updated monthly, averaging 132.049 Dec1996=100 from Dec 1996 (Median) to Oct 2018, with 263 observations. The data reached an all-time high of 157.350 Dec1996=100 in Aug 2018 and a record low of 100.000 Dec1996=100 in Jan 1997. United States CPI U: Northeast: Size Class B/C data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I014: Consumer Price Index: Urban: By Region. All metropolitan areas with population smaller than 1.5 million

This statistic shows the gross domestic product (GDP) per capita in selected world regions in 2024. In North America, the gross domestic product per capita in 2024 amounted to approximately 82,406.48 U.S. dollars.

A mesh of regular hexagons is created using a geoprocessing tool (http://www.arcgis.com/home/item.html?id=03388990d3274160afe240ac54763e57). This tool creates a mesh of hexagons overlapping a study area. The study area is the Gulf of Mexico region for GCOOS. The data is available at http://gis.gcoos.org:8080/arcgis/rest/services/Boundary/GoM_Regions/MapServer

US Industry Data by State, by Industry

Number of Establishments, Sales, Payroll, and Employees

By Gary Hoover [source]

About this dataset

This data set provides a detailed look into the US economy. It includes information on establishments and nonemployer businesses, as well as sales revenue, payrolls, and the number of employees. Gleaned from the Economic Census done every five years, this data is a valuable resource to anyone curious about where the nation was economically at the time. With columns including geographic area name, North American Industry Classification System (NAICS) codes for industries, descriptions of those codes meaning of operation or tax status, and annual payroll, this information-rich dataset contains all you need to track economic trends over time. Whether you’re a researcher studying industry patterns or an entrepreneur looking for market insight — this dataset has what you’re looking for!

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How to use the dataset

This dataset provides detailed US industry data by state, including the number of establishments, value of sales, payroll, and number of employees. All the data is based on the North American Industry Classification System (NAICS) code for each specific industry. This will allow you to easily analyze and compare industries across different states or regions.

Research Ideas

• Analyzing the economic impact of a new business or industry trends in different states: Comparing the change in the number of establishments, payroll, and employees over time can give insight into how a state is affected by a new industry trend or introduction of a new service or product.
• Estimating customer sales potential for businesses: This dataset can be used to estimate the potential customer base for businesses in different geographic areas. By analyzing total business done by non-employers in an area along with its estimated population can help estimate how much overall sales potential exists for a given region.
• Tracking competitor performance: By looking at shipments, receipts, and value of business done across industries in different regions or even cities, companies can track their competitors’ performance and compare it to their own to better assess their strategies going forward

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: 2012 Industry Data by Industry and State.csv | Column name | Description | |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | Geographic area name | The name of the geographic area the data is for. (String) | | NAICS code | The North American Industry Classification System (NAICS) code for the industry. (String) | | Meaning of NAICS code | The description of the NAICS code. (String) | | Meaning of Type of operation or tax status code | The description of the type of operation or tax status code. (String) ...

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2024, at 92,341 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 41,603 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 210,780 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

Graph and download economic data for Unemployment Rate in South Census Region (CSOUUR) from Jan 1976 to Aug 2025 about South Census Region, unemployment, rate, and USA.

The 2018 edition of Woods and Poole Complete U.S. Database provides annual historical data from 1970 (some variables begin in 1990) and annual projections to 2050 of population by race, sex, and age, employment by industry, earnings of employees by industry, personal income by source, households by income bracket and retail sales by kind of business. The Complete U.S. Database contains annual data for all economic and demographic variables for all geographic areas in the Woods & Poole database (the U.S. total, and all regions, states, counties, and CBSAs). The Complete U.S. Database has following components: Demographic & Economic Desktop Data Files: There are 122 files covering demographic and economic data. The first 31 files (WP001.csv – WP031.csv) cover demographic data. The remaining files (WP032.csv – WP122.csv) cover economic data. Demographic DDFs: Provide population data for the U.S., regions, states, Combined Statistical Areas (CSAs), Metropolitan Statistical Areas (MSAs), Micropolitan Statistical Areas (MICROs), Metropolitan Divisions (MDIVs), and counties. Each variable is in a separate .csv file. Variables: Total Population Population Age (breakdown: 0-4, 5-9, 10-15 etc. all the way to 85 & over) Median Age of Population White Population Population Native American Population Asian & Pacific Islander Population Hispanic Population, any Race Total Population Age (breakdown: 0-17, 15-17, 18-24, 65 & over) Male Population Female Population Economic DDFs: The other files (WP032.csv – WP122.csv) provide employment and income data on: Total Employment (by industry) Total Earnings of Employees (by industry) Total Personal Income (by source) Household income (by brackets) Total Retail & Food Services Sales ( by industry) Net Earnings Gross Regional Product Retail Sales per Household Economic & Demographic Flat File: A single file for total number of people by single year of age (from 0 to 85 and over), race, and gender. It covers all U.S., regions, states, CSAs, MSAs and counties. Years of coverage: 1990 - 2050 Single Year of Age by Race and Gender: Separate files for number of people by single year of age (from 0 years to 85 years and over), race (White, Black, Native American, Asian American & Pacific Islander and Hispanic) and gender. Years of coverage: 1990 through 2050. DATA AVAILABLE FOR 1970-2019; FORECASTS THROUGH 2050

rodrocking/Economic dataset hosted on Hugging Face and contributed by the HF Datasets community

This repository contains data relating to our book chapter on the anatomy of US megaregions, entitled: "On the road again: the geography and characteristics of American commuter megaregions".We are sharing the full datasets here for others to explore.There are four different files here, as below.1. megaregions_data_master_011217.xlsx - this file contains full details of the megaregions in relation to their characteristics - including demographics and transport modes.2. commutes-matrix.jpg - this is a graphic from the associated paper which shows the strength of connection between different megaregions.3. megaregions-and-cities.jpg - this is a graphic which shows the shape and size of megaregions in addition to their major cities.4. Megaregion Interflows.xlsx - this file provides more detail on the number of commutes between the megaregions.Please get in touch if you have any questions. You can find our previous work on this topic in another figshare repository, here: https://figshare.shef.ac.uk/articles/United_States_Commutes_and_Megaregions_data_for_GIS/4110156

US County Demographics

Social, Health, and Economic Indicators

By Danny [source]

About this dataset

This dataset contains US county-level demographic data from 2016, giving insight into the health and economic conditions of counties in the United States. Aggregated and filtered from various sources such as the US Census Small Area Income and Poverty Estimates (SAIPE) Program, American Community Survey, CDC National Center for Health Statistics, and more, this comprehensive dataset provides information on population as well as desert population for each county. Additionally, data is split between metropolitan and nonmetropolitan areas according to the Office of Management and Budget's 2013 classification scheme. Valuable information pertaining to infant mortality rates and total population are also included in this detailed set of data. Use this dataset to gain a better understanding of one of our nation's most essential regions

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How to use the dataset

• Look at the information within the 'About this Dataset' section to have an understanding of what data sources were used to create this dataset as well as any transformations that may have been done while creating it.
• Familiarize yourself with the columns provided in the data set to understand what information is available for each county such as total population (totpop), parental education level (educationLvl), median household income (medianIncome), etc.,
• Use a combination of filtering and sorting techniques to narrow down results and focus in on more specific county demographics that you are looking for such as total households living below poverty line by state or median household income per capita between two counties etc.,
• Keep in mind any additional transformations/simplifications/aggregations done during step 2 when using your data for analysis. For example, if certain variables were pivoted during step two from being rows into columns because it was easier to work with multiple years of income levels by having them all consolidated into one column then be aware that some states may not appear in all records due to those transformations being applied differently between regions which could result in missing values or other inconsistencies when doing downstream analysis on your selected variables.
• Utilize resources such as Wikipedia and government census estimates if you need more detailed information surrounding these demographic characteristics beyond what's available within our current dataset – these can be helpful when conducting further research outside of solely relying on our provided spreadsheet values alone!

Research Ideas

• Creating a US county-level heat map of infant mortality rates, offering insight into which areas are most at risk for poor health outcomes.
• Generating predictive models from the population data to anticipate and prepare for future population trends in different states or regions.
• Developing an interactive web-based tool for school districts to explore potential impacts of student mobility on their area's population stability and diversity

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Food Desert.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------| | year | The year the data was collected. (Integer) | | fips | The Federal Information Processing Standard (FIPS) code for the county. (Integer) | | state_fips | The FIPS code for the state. (Integer) | | county_fips | The FIPS code for the county. (Integer)...

United States Exports: FAS: Latin American Free Trade Area data was reported at 34.769 USD bn in Oct 2018. This records an increase from the previous number of 29.971 USD bn for Sep 2018. United States Exports: FAS: Latin American Free Trade Area data is updated monthly, averaging 12.029 USD bn from Jan 1988 (Median) to Oct 2018, with 370 observations. The data reached an all-time high of 34.769 USD bn in Oct 2018 and a record low of 2.393 USD bn in Jan 1988. United States Exports: FAS: Latin American Free Trade Area data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.JA009: Trade Statistics: Census Basis: by Region. Latin American Free Trade Area includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Venezuela.