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. F

    Employed full time: Wage and salary workers: 65 years and over: Native born

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
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    (2025). Employed full time: Wage and salary workers: 65 years and over: Native born [Dataset]. https://fred.stlouisfed.org/series/LEU0257373100A
    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: 65 years and over: Native born (LEU0257373100A) from 2005 to 2024 about 65 years +, native born, full-time, salaries, workers, wages, employment, and USA.

  3. 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?

  4. 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

  5. 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

  6. Occupational Employment and Wage Statistics (OEWS)

    • catalog.data.gov
    • s.cnmilf.com
    Updated Nov 27, 2024
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    California Employment Development Department (2024). Occupational Employment and Wage Statistics (OEWS) [Dataset]. https://catalog.data.gov/dataset/occupational-employment-and-wage-statistics-oews-4b4c4
    Explore at:
    Dataset updated
    Nov 27, 2024
    Dataset provided by
    Employment Development Departmenthttp://www.edd.ca.gov/
    Description

    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.

  7. F

    Average Hourly Earnings of All Employees, Total Private

    • fred.stlouisfed.org
    json
    Updated Jul 3, 2025
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    (2025). Average Hourly Earnings of All Employees, Total Private [Dataset]. https://fred.stlouisfed.org/series/CES0500000003
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 3, 2025
    License

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

    Description

    Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jun 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

  8. Current Population Survey (CPS) - Weekly and Hourly Earnings

    • catalog.data.gov
    • s.cnmilf.com
    Updated May 16, 2022
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    U.S. Department of Labor Bureau of Labor Statistics (2022). Current Population Survey (CPS) - Weekly and Hourly Earnings [Dataset]. https://catalog.data.gov/dataset/current-population-survey-cps-weekly-and-hourly-earnings-8d283
    Explore at:
    Dataset updated
    May 16, 2022
    Dataset provided by
    United States Department of Laborhttp://www.dol.gov/
    Bureau of Labor Statisticshttp://www.bls.gov/
    Description

    The Current Population Survey (CPS) is a monthly survey of households conducted by the Bureau of Census for the Bureau of Labor Statistics. The earnings data are collected from one-fourth of the CPS total sample of approximately 60,000 households. Data measures usual hourly and weekly earnings of wage and salary workers. All self-employed persons are excluded, regardless of whether their businesses are incorporated. Data represent earnings before taxes and other deductions and include any overtime pay, commissions, or tips usually received. Earnings data are available for all workers, by age, race, Hispanic or Latino ethnicity, sex, occupation, usual full- or part-time status, educational attainment, and other characteristics. Data are published quarterly. More information and details about the data provided can be found at http://www.bls.gov/cps/earnings.htm

  9. 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/

  10. Quarterly Census of Employment and Wages (QCEW)

    • catalog.data.gov
    • data.ca.gov
    • +1more
    Updated Nov 27, 2024
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    California Employment Development Department (2024). Quarterly Census of Employment and Wages (QCEW) [Dataset]. https://catalog.data.gov/dataset/quarterly-census-of-employment-and-wages-qcew-a6fea
    Explore at:
    Dataset updated
    Nov 27, 2024
    Dataset provided by
    Employment Development Departmenthttp://www.edd.ca.gov/
    Description

    The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.

  11. Employment, Hours, and Earnings - National

    • db.nomics.world
    Updated Jul 4, 2025
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    DBnomics (2025). Employment, Hours, and Earnings - National [Dataset]. https://db.nomics.world/BLS/ce
    Explore at:
    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Authors
    DBnomics
    Description

    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.

  12. Employer Cost for Employee Compensation

    • catalog.data.gov
    • datadiscoverystudio.org
    • +3more
    Updated May 16, 2022
    + more versions
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    Bureau of Labor Statistics (2022). Employer Cost for Employee Compensation [Dataset]. https://catalog.data.gov/dataset/employer-cost-for-employee-compensation-e7a39
    Explore at:
    Dataset updated
    May 16, 2022
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Description

    The Employer Costs for Employee Compensation (ECEC) is a measure of the cost of labor. The compensation series includes wages and salaries plus employer costs for individual employee benefits. Employee benefit costs are calculated as cents-per-hour-worked for individual benefits ranging from employer payments for Social Security to paid time off for holidays. The survey covers all occupations in the civilian economy, which includes the total private economy (excluding farms and households), and the public sector (excluding the Federal government). Statistics are published for the private and public sectors separately, and the data are combined in a measure for the civilian economy. For information and data, visit: https://www.bls.gov/ncs/ect/

  13. Current Employment Statistics (CES)

    • catalog.data.gov
    • data.ca.gov
    Updated Nov 27, 2024
    + more versions
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    California Employment Development Department (2024). Current Employment Statistics (CES) [Dataset]. https://catalog.data.gov/dataset/current-employment-statistics-ces-5540b
    Explore at:
    Dataset updated
    Nov 27, 2024
    Dataset provided by
    Employment Development Departmenthttp://www.edd.ca.gov/
    Description

    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.

  14. F

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

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
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    (2025). Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: 16 years and over [Dataset]. https://fred.stlouisfed.org/series/LEU0252881500A
    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: 16 years and over (LEU0252881500A) from 1979 to 2024 about second quartile, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

  15. Wage Estimates

    • kaggle.com
    zip
    Updated Jun 29, 2017
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    US Bureau of Labor Statistics (2017). Wage Estimates [Dataset]. https://www.kaggle.com/bls/wage-estimates
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    zip(4529907 bytes)Available download formats
    Dataset updated
    Jun 29, 2017
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Authors
    US Bureau of Labor Statistics
    License

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

    Description

    Context:

    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. 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.

    Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

    The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

    The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

    Content:

    Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

    Frequency of Observations: Data are available on an annual basis, typically in May.

    Data Characteristics: All hourly wages are published to the nearest cent.

    Acknowledgements:

    This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

    Inspiration:

    This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

  16. A

    ‘Earnings of females and males employees.’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Mar 27, 2015
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2015). ‘Earnings of females and males employees.’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-earnings-of-females-and-males-employees-cd17/3f7d5184/?iid=002-643&v=presentation
    Explore at:
    Dataset updated
    Mar 27, 2015
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Description

    Analysis of ‘Earnings of females and males employees.’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mpwolke/cusersmarildownloadsearningcsv on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    The Bureau of Labor Statistics reported that, in 2013, female full-time workers had median weekly earnings of $706, compared to men's median weekly earnings of $860. Women aged 35 years and older earned 74% to 80% of the earnings of their male counterparts. https://en.wikipedia.org › wiki › Gender_pay_gap_in_the_United_States

    Content

    What is the gender pay gap 2019? Study after study has identified a persistent gender pay gap. A PayScale report found that women still make only $0.79 for each dollar men make in 2019. A Bureau of Labor Statistics (BLS) analysis discovered that in 2018, median weekly earnings for female full-time wage and salary workers was 81% of men's earnings.Jul 11, 2019 https://www.forbes.com/sites/shaharziv/2019/07/11/gender-pay-gap-bigger-than-you-thnk/#36ca335f7d8a.

    Acknowledgements

    Linked through data.gov.au for discoverability and availability. This dataset was originally found on data.gov.au https://data.gov.au/data/dataset/a5776c56-bdde-4643-a3fd-dcc2775d7d7a ***Photo by Samantha Sophia on Unsplash.

    Inspiration

    Great females scientists: Mileva Maric', Frances "Poppy" Northcut, Hedy Lamarr, Marie Sklodowska Curie and Ada Lovelace. If you don't know them yet, just search on Google.

    --- Original source retains full ownership of the source dataset ---

  17. 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.

  18. F

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

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
    + more versions
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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.

  19. United States AHE: sa: PW: FA: Consumer Lending

    • ceicdata.com
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    CEICdata.com, United States AHE: sa: PW: FA: Consumer Lending [Dataset]. https://www.ceicdata.com/en/united-states/current-employment-statistics-survey-average-hourly-earnings-production-workers-seasonally-adjusted
    Explore at:
    Dataset provided by
    CEIC Data
    License

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

    Time period covered
    Feb 1, 2024 - Jan 1, 2025
    Area covered
    United States
    Description

    AHE: sa: PW: FA: Consumer Lending data was reported at 28.890 USD in Mar 2025. This records an increase from the previous number of 28.730 USD for Feb 2025. AHE: sa: PW: FA: Consumer Lending data is updated monthly, averaging 13.320 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 28.890 USD in Mar 2025 and a record low of 8.580 USD in Feb 1990. AHE: sa: PW: FA: Consumer Lending 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.

  20. International Labor Comparisons (ILC)

    • catalog.data.gov
    Updated May 16, 2022
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    Bureau of Labor Statistics (2022). International Labor Comparisons (ILC) [Dataset]. https://catalog.data.gov/dataset/international-labor-comparisons-ilc-f307b
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    Dataset updated
    May 16, 2022
    Dataset provided by
    Bureau of Labor Statisticshttp://www.bls.gov/
    Description

    The International Labor Comparisons (ILC) program provides international comparisons of hourly compensation costs; productivity and unit labor costs; labor force, employment and consumer prices. The comparisons relate primarily to the major industrial countries, but other countries are included in certain measures. The ILC program adjusts data to a common conceptual framework because direct comparisons of national statistics across countries can be misleading due to differing concepts and methods. ILC data are used to assess U.S. economic and labor market performance relative to that of other countries and to evaluate the competitive position of the United States in increasingly global markets. The ILC program was eliminated in 2013 under sequestration. For more information and data visit: https://www.bls.gov/ilc/

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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
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Occupational Employment and Wage Statistics (OES)

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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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