100+ datasets found
  1. Occupational Employment and Wage Statistics (OES)

    • catalog.data.gov
    Updated May 16, 2022
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    Bureau of Labor Statistics (2022). Occupational Employment and Wage Statistics (OES) [Dataset]. https://catalog.data.gov/dataset/occupational-employment-and-wage-statistics-oes
    Explore at:
    Dataset updated
    May 16, 2022
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Description

    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

  2. Occupational Employment and Wage Statistics (OEWS)

    • data.ca.gov
    csv
    Updated Jul 14, 2025
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    California Employment Development Department (2025). Occupational Employment and Wage Statistics (OEWS) [Dataset]. https://data.ca.gov/dataset/oews
    Explore at:
    csv(105364359)Available download formats
    Dataset updated
    Jul 14, 2025
    Dataset provided by
    Employment Development Departmenthttp://www.edd.ca.gov/
    Authors
    California Employment Development Department
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Occupational Employment and Wage Statistics (OEWS) Survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). The BLS provides the procedures and technical support, draws the sample, and produces the survey materials, while the SWAs collect the data. SWAs from all fifty states, plus the District of Columbia, Puerto Rico, Guam, and the Virgin Islands participate in the survey. Occupational employment and wage rate estimates at the national level are produced by BLS using data from the fifty states and the District of Columbia. Employers who respond to states' requests to participate in the OEWS survey make these estimates possible.

    The 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 approximately 830 occupations. 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.

    The OEWS estimates are published annually.

    SOURCE: https://www.bls.gov/oes/oes_emp.htm

  3. Occupational Outlook Handbook

    • catalog.data.gov
    • gimi9.com
    Updated May 16, 2022
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    Bureau of Labor Statistics (2022). Occupational Outlook Handbook [Dataset]. https://catalog.data.gov/dataset/occupational-outlook-handbook-51009
    Explore at:
    Dataset updated
    May 16, 2022
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Description

    The Occupational Outlook Handbook (OOH) is a nationally recognized source of career information, designed to provide valuable assistance to individuals making decisions about their future work lives. The Handbook is revised every two years. The OOH offers information on the hundreds of occupations that provide the majority of jobs in the United States. Each occupational profile describes the typical duties performed by the occupation, the work environment of that occupation, the typical education and training needed to enter the occupation, the median pay for workers in the occupation, and the job outlook over the coming decade for that occupation. For information on occupations, please visit: https://www.bls.gov/ooh/

  4. F

    Expenditures: Total Average Annual Expenditures by Occupation: Wage and...

    • fred.stlouisfed.org
    json
    Updated Sep 25, 2024
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    (2024). Expenditures: Total Average Annual Expenditures by Occupation: Wage and Salary Earners: Service Workers [Dataset]. https://fred.stlouisfed.org/series/CXUTOTALEXPLB1206M
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 25, 2024
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Expenditures: Total Average Annual Expenditures by Occupation: Wage and Salary Earners: Service Workers (CXUTOTALEXPLB1206M) from 1984 to 2023 about occupation, salaries, workers, average, expenditures, wages, services, and USA.

  5. Modeled Wage Estimates

    • db.nomics.world
    Updated Aug 23, 2024
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    DBnomics (2024). Modeled Wage Estimates [Dataset]. https://db.nomics.world/BLS/wm
    Explore at:
    Dataset updated
    Aug 23, 2024
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Authors
    DBnomics
    Description

    The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location.

  6. d

    BLS Jobs by Industry Category

    • catalog.data.gov
    • opendata.maryland.gov
    • +4more
    Updated Jun 21, 2025
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    opendata.maryland.gov (2025). BLS Jobs by Industry Category [Dataset]. https://catalog.data.gov/dataset/bls-jobs-by-industry-category
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    Dataset updated
    Jun 21, 2025
    Dataset provided by
    opendata.maryland.gov
    Description

    Data 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.

  7. F

    Employed full time: Wage and salary workers: Packers and packagers, hand...

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
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    (2025). Employed full time: Wage and salary workers: Packers and packagers, hand occupations: 16 years and over [Dataset]. https://fred.stlouisfed.org/series/LEU0254524000A
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 22, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Employed full time: Wage and salary workers: Packers and packagers, hand occupations: 16 years and over (LEU0254524000A) from 2000 to 2024 about occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

  8. USA Bureau of Labor Statistics

    • kaggle.com
    zip
    Updated Aug 30, 2019
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    US Bureau of Labor Statistics (2019). USA Bureau of Labor Statistics [Dataset]. https://www.kaggle.com/bls/bls
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    zip(0 bytes)Available download formats
    Dataset updated
    Aug 30, 2019
    Dataset authored and provided by
    US Bureau of Labor Statistics
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    The Bureau of Labor Statistics (BLS) is a unit of the United States Department of Labor. It is the principal fact-finding agency for the U.S. government in the broad field of labor economics and statistics and serves as a principal agency of the U.S. Federal Statistical System. The BLS is a governmental statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the U.S. Congress, other Federal agencies, State and local governments, business, and labor representatives. Source: https://en.wikipedia.org/wiki/Bureau_of_Labor_Statistics

    Content

    Bureau of Labor Statistics including CPI (inflation), employment, unemployment, and wage data.

    Update Frequency: Monthly

    Querying BigQuery Tables

    Fork this kernel to get started.

    Acknowledgements

    https://bigquery.cloud.google.com/dataset/bigquery-public-data:bls

    https://cloud.google.com/bigquery/public-data/bureau-of-labor-statistics

    Dataset Source: http://www.bls.gov/data/

    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.

    Banner Photo by Clark Young from Unsplash.

    Inspiration

    What is the average annual inflation across all US Cities? What was the monthly unemployment rate (U3) in 2016? What are the top 10 hourly-waged types of work in Pittsburgh, PA for 2016?

  9. d

    BLS Jobs Data - Change from the Previous Month

    • catalog.data.gov
    • opendata.maryland.gov
    • +2more
    Updated Jun 21, 2025
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    opendata.maryland.gov (2025). BLS Jobs Data - Change from the Previous Month [Dataset]. https://catalog.data.gov/dataset/bls-jobs-data-change-from-the-previous-month
    Explore at:
    Dataset updated
    Jun 21, 2025
    Dataset provided by
    opendata.maryland.gov
    Description

    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.

  10. National Compensation Survey - Modeled Wage Estimates

    • catalog.data.gov
    • s.cnmilf.com
    Updated May 16, 2022
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    Bureau of Labor Statistics (2022). National Compensation Survey - Modeled Wage Estimates [Dataset]. https://catalog.data.gov/dataset/national-compensation-survey-modeled-wage-estimates-5de7e
    Explore at:
    Dataset updated
    May 16, 2022
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Description

    The National Compensation Survey (NCS) program produces information on wages by occupation for many metropolitan areas.The Modeled Wage Estimates (MWE) provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation. The modeled wage estimates are produced using a statistical procedure that combines survey data collected by the National Compensation Survey (NCS) and the Occupational Employment Statistics (OES) programs. Borrowing from the strengths of the NCS, information on job characteristics and work levels, and from the OES, the occupational and geographic detail, the modeled wage estimates provide more detail on occupational average hourly wages than either program is able to provide separately. Wage rates for different work levels within occupation groups also are published. Data are available for private industry, State and local governments, full-time workers, part-time workers, and other workforce characteristics.

  11. F

    Employed full time: Wage and salary workers: Pest control workers...

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
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    (2025). Employed full time: Wage and salary workers: Pest control workers occupations: 16 years and over [Dataset]. https://fred.stlouisfed.org/series/LEU0254494500A
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 22, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Employed full time: Wage and salary workers: Pest control workers occupations: 16 years and over (LEU0254494500A) from 2000 to 2024 about occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

  12. Current Employment Statistics - Employment, Hours, and Earnings - State and...

    • catalog.data.gov
    • data.amerigeoss.org
    Updated May 16, 2022
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    Bureau of Labor Statistics (2022). Current Employment Statistics - Employment, Hours, and Earnings - State and Metro Area [Dataset]. https://catalog.data.gov/dataset/current-employment-statistics-employment-hours-and-earnings-state-and-metro-area-b02b3
    Explore at:
    Dataset updated
    May 16, 2022
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Description

    The Current Employment Statistics (CES) program produces detailed industry estimates of employment, hours, and earnings of workers on nonfarm payrolls. CES State and Metro Area produces data for all 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, and about 450 metropolitan areas and divisions. Each month, CES surveys approximately 142,000 businesses and government agencies, representing 689,000 individual worksites. For more information and data, visit: https://www.bls.gov/sae/

  13. F

    Employed full time: Median usual weekly nominal earnings (second quartile):...

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
    + more versions
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    (2025). Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer programmers occupations: 16 years and over: Men [Dataset]. https://fred.stlouisfed.org/series/LEU0254637300A
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 22, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Computer programmers occupations: 16 years and over: Men (LEU0254637300A) from 2000 to 2024 about second quartile, computers, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

  14. Occupation, Salary and Likelihood of Automation

    • kaggle.com
    Updated May 24, 2020
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    Larxel (2020). Occupation, Salary and Likelihood of Automation [Dataset]. https://www.kaggle.com/andrewmvd/occupation-salary-and-likelihood-of-automation/activity
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 24, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Larxel
    Description

    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.

    Splash Banner

    Photo by Alex Knight on Unsplash

  15. Utah Occupational Employment And Wages Data By Job

    • opendata.utah.gov
    • data.virginia.gov
    application/rdfxml +5
    Updated Dec 23, 2014
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    Bureau of Labor Statistics (2014). Utah Occupational Employment And Wages Data By Job [Dataset]. https://opendata.utah.gov/Jobs/Utah-Occupational-Employment-And-Wages-Data-By-Job/88ws-42u9
    Explore at:
    tsv, xml, application/rdfxml, application/rssxml, csv, jsonAvailable download formats
    Dataset updated
    Dec 23, 2014
    Dataset authored and provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    Utah
    Description

    The Occupational Employment Statistics (OES) program produces employment and wage estimates annually for over 800 occupations. These estimates are available for the nation as a whole, for individual States, and for metropolitan and nonmetropolitan areas; national occupational estimates for specific industries are also available.

  16. T

    Vital Signs: Jobs by Industry (Location Quotient) by County (2022)

    • data.bayareametro.gov
    application/rdfxml +5
    Updated Dec 14, 2022
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    (2022). Vital Signs: Jobs by Industry (Location Quotient) by County (2022) [Dataset]. https://data.bayareametro.gov/Economy/Vital-Signs-Jobs-by-Industry-Location-Quotient-by-/uijm-ykyx
    Explore at:
    json, tsv, xml, csv, application/rdfxml, application/rssxmlAvailable download formats
    Dataset updated
    Dec 14, 2022
    Description

    VITAL SIGNS INDICATOR
    Jobs by Industry (EC1)

    FULL MEASURE NAME
    Employment by place of work by industry sector

    LAST UPDATED
    December 2022

    DESCRIPTION
    Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

    DATA SOURCE
    Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) - https://www.bls.gov/cew/downloadable-data-files.htm
    1990-2021

    CONTACT INFORMATION
    vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator)
    Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.

    Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.

    The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

    Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.

    QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.

    For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:

    Farm:
    (aggregation level: 74, NAICS code: 11) - Contra Costa: 2008-2010 - Marin: 1990-2006, 2008-2010, 2014-2020 - Napa: 1990-2004, 2013-2021 - San Francisco: 2019-2020 - San Mateo: 2013

    Information:
    (aggregation level: 73, NAICS code: 51) - Solano: 2001

    Financial Activities:
    (aggregation level: 73, NAICS codes: 52, 53) - Solano: 2001

    Unclassified:
    (aggregation level: 73, NAICS code: 99) - All nine Bay Area counties: 1990-2000 - Marin, Napa, San Mateo, and Solano: 2020 - Napa: 2019 - Solano: 2001

  17. Data from: Local Area Unemployment Statistics

    • catalog.data.gov
    • data.ct.gov
    • +2more
    Updated May 16, 2022
    + more versions
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    Bureau of Labor Statistics (2022). Local Area Unemployment Statistics [Dataset]. https://catalog.data.gov/dataset/local-area-unemployment-statistics-47d02
    Explore at:
    Dataset updated
    May 16, 2022
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Description

    The Local Area Unemployment Statistics (LAUS) program is a federal-state cooperative effort which produces monthly estimates of produces monthly and annual employment, unemployment, and labor force data for approximately 7,000 areas including Census regions and divisions, States, counties, metropolitan areas, and many cities. For more information and data visit: https://www.bls.gov/lau/

  18. F

    Employment Cost Index: Wages and Salaries: Private Industry Workers: Service...

    • fred.stlouisfed.org
    json
    Updated Apr 30, 2025
    + more versions
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    Employment Cost Index: Wages and Salaries: Private Industry Workers: Service Occupations [Dataset]. https://fred.stlouisfed.org/series/ECISRVWAG
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Apr 30, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Employment Cost Index: Wages and Salaries: Private Industry Workers: Service Occupations (ECISRVWAG) from Q1 2001 to Q1 2025 about ECI, occupation, salaries, workers, private industries, wages, services, private, industry, inflation, and USA.

  19. F

    Employed full time: Wage and salary workers: Millwrights occupations: 16...

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
    + more versions
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    (2025). Employed full time: Wage and salary workers: Millwrights occupations: 16 years and over [Dataset]. https://fred.stlouisfed.org/series/LEU0254511600A
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 22, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Employed full time: Wage and salary workers: Millwrights occupations: 16 years and over (LEU0254511600A) from 2000 to 2024 about occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

  20. F

    Employed full time: Wage and salary workers: Physician assistants...

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
    + more versions
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    (2025). Employed full time: Wage and salary workers: Physician assistants occupations: 16 years and over [Dataset]. https://fred.stlouisfed.org/series/LEU0254487700A
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 22, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Employed full time: Wage and salary workers: Physician assistants occupations: 16 years and over (LEU0254487700A) from 2000 to 2024 about physicians, assistance, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Share
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Cite
Bureau of Labor Statistics (2022). Occupational Employment and Wage Statistics (OES) [Dataset]. https://catalog.data.gov/dataset/occupational-employment-and-wage-statistics-oes
Organization logo

Occupational Employment and Wage Statistics (OES)

Explore at:
16 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
May 16, 2022
Dataset provided by
Bureau of Labor Statisticshttp://www.bls.gov/
Description

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

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