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.

Graph and download economic data for Infra-Annual Labor Statistics: Unemployment Rate Total: From 15 to 64 Years for United States (LRUN64TTUSM156N) from Jan 1955 to Jun 2025 about 15 to 64 years, unemployment, rate, and USA.

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.

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.

In October 2024, the civilian labor force amounted to 168.48 million people in the United States. The term civilian labor force is used by the U.S. Bureau of Labor Statistics (BLS) to describe the subset of Americans who have jobs or are seeking a job, are at least 16 years old, are not serving in the military, and are not institutionalized.

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

This dataset contains annual average CES data for California statewide and areas from 1990 to 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.

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 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).

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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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.

Graph and download economic data for All Employees, Federal (CES9091000001) from Jan 1939 to Jun 2025 about establishment survey, federal, government, employment, and USA.

Historical information on the Employment Dataset shows several current and historical annual statistics regarding population, the labor force, employment and unemployment in the City of Mesa. Monthly labor force, employment and unemployment information is at https://citydata.mesaaz.gov/External-Data/Employment-and-Labor-Force-Monthly/3vbg-xf63.

Sources: Population Data - United States Census Bureau -https://www.census.gov/topics/population/data.html Employment Data - Bureau of Labor Statistics - http://www.bls.gov/data/ Local Area Unemployment Statistics (LAUS) - https://www.bls.gov/lau/ To see how these terms are defined and what they include, please visit the Terms Glossary from the United State Department of Labor’s Bureau of Labor Statistics (BLS), which can be found at the following web address: http://www.bls.gov/bls/glossary.htm

The Mass Layoff Statistics program is a Federal-State cooperative statistical effort which uses a standardized, automated approach to identify, describe, and track the effects of major job cutbacks, using data from each State's unemployment insurance database. Establishments which have at least 50 initial claims for unemployment insurance (UI) filed against them during a consecutive 5-week period are contacted by State agencies to determine whether those separations are of at least 31 days duration, and, if so, information is obtained on the total number of persons separated, the reasons for these separations, and recall expectations. Establishments are identified according to industry classification and location, and unemployment insurance claimants are identified by such demographic characteristics as age, race, sex, ethnic group, and place of residence. The program yields information on an individual's entire spell of unemployment, to the point when regular unemployment insurance benefits are exhausted. It provides databases of establishments and claimants, both of which are used for further research and analysis. Data available Monthly data report summary information on all establishments which have at least 50 initial claims for unemployment insurance (UI) filed against them during a 5-week period. Data are available for 50 States, the District of Columbia, and Puerto Rico, as well as by industry. Quarterly data report on private sector nonfarm establishments which have at least 50 initial claims filed against them during a 5-week period and where the employer indicates that 50 or more people were separated from their jobs for at least 31 days. Information is obtained on the total number of persons separated; the reasons for separation; worksite closures; recall expectations; and socioeconomic characteristics on UI claimants such as gender, age, race, and residency. These characteristics are collected at two points in time when an initial claim is filed and when the claimant exhausts regular UI benefits. In between these points, the unemployment status of claimants is tracked through the monitoring of certifications for unemployment (continued claims) filed under the regular State UI program. Data are available for 50 States, the District of Columbia, and Puerto Rico, as well as by industry. Coverage Monthly, quarterly, and annual data for 50 States, the District of Columbia, and Puerto Rico. Monthly data are available since April 1995; quarterly data since second quarter 1995.

Table showing the number of child labor cases in 2023

Monthly statistics regarding the labor force, employment and unemployment in Mesa and nearby municipalities. Unemployment rate sourced at BLS.gov Data Viewer. Employment Data - Bureau of Labor Statistics - http://www.bls.gov/data/ Local Area Unemployment Statistics (LAUS) - https://www.bls.gov/lau/ (See for next data release dates). To see how these terms are defined and what they include, please visit the Terms Glossary from the United State Department of Labor’s Bureau of Labor Statistics (BLS), which can be found at the following web address: http://www.bls.gov/bls/glossary.htm

This dataset from the Bureau of Labor Statistics provides monthly estimates regarding total employment and unemployment, which together comprise the labor force. Our data extract lists all data published for North Carolina’s counties from January 2019 to the present. This dataset is a comprehensive nationwide representation using estimates derived from the national Current Population Survey (CPS) and American Community Survey 5-year estimates. No disaggregations by demographic or worker characteristics are included in the labor force estimate. Time series reports for each variable (employment, unemployment, and labor force) are available for each geography (county) using the BLS multi-screen data tool. Preliminary estimates are released within 30 days of each month and finalized within another 30 days, resulting in a 2-month data lag. The data is available from BLS for a variety of geographic areas, including states, MSAs, counties, cities and towns, and other census regions.

The Mass Layoff Statistics (MLS) program collects reports on mass layoff actions that result in workers being separated from their jobs. Monthly mass layoff numbers are from establishments which have at least 50 initial claims for unemployment insurance (UI) filed against them during a 5-week period. Extended mass layoff numbers (issued quarterly) are from a subset of such establishments—where private sector nonfarm employers indicate that 50 or more workers were separated from their jobs for at least 31 days. MLS was eliminated in 2013 under sequestration. For more information and data visit: https://www.bls.gov/mls/

Graph and download economic data for Infra-Annual Labor Statistics: Labor Force Total: From 15 to 74 Years for United States (LFAC74TTUSQ647S) from Q1 1981 to Q1 2025 about 15 to 74 years, labor force, labor, personal, and USA.

Number of people working full-time and part-time..