The Occupational Employment and Wage Statistics (OES) program conducts a semi-annual survey to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates by geographic area and by industry. Estimates based on geographic areas are available at the National, State, Metropolitan, and Nonmetropolitan Area levels. The Bureau of Labor Statistics produces occupational employment and wage estimates for over 450 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and 5-digit North American Industry Classification System (NAICS) industrial groups. More information and details about the data provided can be found at http://www.bls.gov/oes

Historical Employment Statistics 1990 - current. The Current Employment Statistics (CES) more information program provides the most current estimates of nonfarm employment, hours, and earnings data by industry (place of work) for the nation as a whole, all states, and most major metropolitan areas. The CES survey is a federal-state cooperative endeavor in which states develop state and sub-state data using concepts, definitions, and technical procedures prescribed by the Bureau of Labor Statistics (BLS). Estimates produced by the CES program include both full- and part-time jobs. Excluded are self-employment, as well as agricultural and domestic positions. In Connecticut, more than 4,000 employers are surveyed each month to determine the number of the jobs in the State. For more information please visit us at http://www1.ctdol.state.ct.us/lmi/ces/default.asp.

About this Dataset

This dataset combines automation probability data with a breakdown of the number of jobs and salary in each occupation by state within the USA. Automation probability was acquired from the work of Carl Benedikt Freyand Michael A. Osborne; State employment data is from the Bureau of Labor Statistics. Note that for simplicity of analysis, all jobs where data was not available or there were less than 10 employees were marked as zero.

How to Cite this Dataset

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

Salary Data

@misc{u.s. bureau of labor statistics, title={Occupational Employment Statistics}, url={https://www.bls.gov/oes/current/oes_nat.htm}, journal={U.S. BUREAU OF LABOR STATISTICS}}

Automation Data

@article{frey_osborne_2017, title={The future of employment: How susceptible are jobs to computerisation?}, volume={114}, DOI={10.1016/j.techfore.2016.08.019}, journal={Technological Forecasting and Social Change}, author={Frey, Carl Benedikt and Osborne, Michael A.}, year={2017}, pages={254–280}}

License

License was not specified at the source.

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This dataset represents the CHANGE in the number of jobs per industry category and sub-category from the previous month, not the raw counts of actual jobs. The data behind these monthly change values is from the Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) program. CES data represents businesses and government agencies, providing detailed industry data on employment on nonfarm payrolls.

Graph and download economic data for All Employees, Professional, Scientific, and Technical Services (CES6054000001) from Jan 1990 to Apr 2025 about professional, establishment survey, business, services, employment, and USA.

Graph and download economic data for All Employees: Manufacturing in Vermont (VTMFG) from Jan 1990 to May 2025 about VT, manufacturing, employment, and USA.

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values. The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: May 2025 (preliminary values at the state and county level) The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: July 2nd, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and County NationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS"s county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova. As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties. To better understand the different labor force statistics included in this map, see the diagram below from BLS:

This dataset contains non-fatal injury and illness data by industry from US Bureau of Labor Statistics for 2016. The industries are classified according to the North American Industry Classification System (NAICS).

Abstract

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS)

Documentation

Update frequency: Monthly Dataset source: U.S. Bureau of Labor Statistics Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/bls-public-data/bureau-of-labor-statistics

Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

This dataset contains the Local Area Unemployment Statistics (LAUS), annual averages from 1990 to 2023. The Local Area Unemployment Statistics (LAUS) program is a Federal-State cooperative effort in which monthly estimates of total employment and unemployment are prepared for approximately 7,600 areas, including counties, cities and metropolitan statistical areas. These estimates are key indicators of local economic conditions. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS. Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.

This dataset contains annual average CES data for California statewide and areas from 1990 - 2023. The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States. CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

The Occupational Employment and Wage Statistics (OEWS) Survey collects data from a sample of establishments and calculates employment and wage estimates by occupation, industry, and geographic area. The semiannual survey covers all non-farm industries. Data are collected by the Employment Development Department in cooperation with the Bureau of Labor Statistics, US Department of Labor. The OEWS Program estimates employment and wages for over 800 occupations from an annual sample of approx. 34,000 California employers. It also produces employment and wage estimates for statewide, Metropolitan Statistical Areas (MSAs), and Balance of State areas. Estimates are a snapshot in time and should not be used as a time series.

Job Openings and Labor Turnover Survey data from the U.S. Bureau of Labor Statistics

The Local Area Unemployment Statistics (LAUS) program is a Federal-State cooperative effort in which monthly estimates of total employment and unemployment are prepared for approximately 7,600 areas, including counties, cities and metropolitan statistical areas. These estimates are key indicators of local economic conditions. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS. Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.

Graph and download economic data for All Employees, Manufacturing (MANEMP) from Jan 1939 to Jun 2025 about headline figure, establishment survey, manufacturing, employment, and USA.

Graph and download economic data for Unemployment Rate in Oceana County, MI (MIOCEA7URN) from Jan 1990 to May 2025 about Oceana County, MI; MI; unemployment; rate; and USA.

The Current Employment Statistics (CES) program provides estimates of employment, hours, and earnings information on a national basis and in considerable industry detail. The Bureau of Labor Statistics collects payroll data each month from a sample of business and government establishments in all nonfarm activities.

AHE: sa: PW: EH: Child & Youth Services data was reported at 24.850 USD in Mar 2025. This records an increase from the previous number of 24.750 USD for Feb 2025. AHE: sa: PW: EH: Child & Youth Services data is updated monthly, averaging 15.010 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 24.850 USD in Mar 2025 and a record low of 8.520 USD in Jan 1990. AHE: sa: PW: EH: Child & Youth Services data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

This dataset uses seasonally adjusted data from the US Bureau of Labor Statistics to present information on Maryland's labor force participation rate, employment rate, and unemployment rate.