25 datasets found
  1. 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
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    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.

  2. U.S. highest paying occupations 2023

    • statista.com
    Updated Jul 3, 2024
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    Abigail Tierney (2024). U.S. highest paying occupations 2023 [Dataset]. https://www.statista.com/topics/789/wages-and-salary/
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    Dataset updated
    Jul 3, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Abigail Tierney
    Area covered
    United States
    Description

    In 2023, many of the highest paying jobs were those in the medical field. The mean annual pay for psychiatrists was at 256,930 U.S. dollars in the United States. The highest paying occupation was pediatric surgeon, with a mean annual wage of 449,320 U.S. dollars.

  3. 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
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    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

  4. 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!

  5. F

    Average Hourly Earnings of All Employees, Manufacturing

    • fred.stlouisfed.org
    json
    Updated Jul 3, 2025
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    (2025). Average Hourly Earnings of All Employees, Manufacturing [Dataset]. https://fred.stlouisfed.org/series/CES3000000003
    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, Manufacturing (CES3000000003) from Mar 2006 to Jun 2025 about earnings, establishment survey, hours, wages, manufacturing, employment, and USA.

  6. U.S. monthly average hourly earnings nonfarm payroll employees 2022-2024

    • statista.com
    Updated Jul 3, 2024
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    Abigail Tierney (2024). U.S. monthly average hourly earnings nonfarm payroll employees 2022-2024 [Dataset]. https://www.statista.com/topics/789/wages-and-salary/
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    Dataset updated
    Jul 3, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Abigail Tierney
    Area covered
    United States
    Description

    In October 2024, the average hourly earnings for all employees on private nonfarm payrolls in the United States stood at 35.46 U.S. dollars. The data have been seasonally adjusted. Employed persons are employees on nonfarm payrolls and consist of: persons who did any work for pay or profit during the survey reference week; persons who did at least 15 hours of unpaid work in a family-operated enterprise; and persons who were temporarily absent from their regular jobs because of illness, vacation, bad weather, industrial dispute, or various personal reasons.

  7. 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
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    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

  8. U.S. minimum wage 2024, by state

    • statista.com
    • ai-chatbox.pro
    Updated Apr 3, 2025
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    Statista (2025). U.S. minimum wage 2024, by state [Dataset]. https://www.statista.com/statistics/238997/minimum-wage-by-us-state/
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    Dataset updated
    Apr 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 1, 2025
    Area covered
    United States
    Description

    The federally mandated minimum wage in the United States is 7.25 U.S. dollars per hour, although the minimum wage varies from state to state. As of January 1, 2025, the District of Columbia had the highest minimum wage in the U.S., at 17.5 U.S. dollars per hour. This was followed by Washington, which had 16.66 U.S. dollars per hour as the state minimum wage. Minimum wage workers Minimum wage jobs are traditionally seen as “starter jobs” in the U.S., or first jobs for teenagers and young adults, and the number of people working minimum wage jobs has decreased from almost four million in 1979 to about 247,000 in 2020. However, the number of workers earning less than minimum wage in 2020 was significantly higher, at about 865,000. Minimum wage jobs Minimum wage jobs are primarily found in food preparation and serving occupations, as well as sales jobs (primarily in retail). Because the minimum wage has not kept up with inflation, nor has it been increased since 2009, it is becoming harder and harder live off of a minimum wage wage job, and for those workers to afford essential things like rent.

  9. U.S. wage and salary workers median hourly earnings 1979-2023

    • statista.com
    Updated Jan 14, 2025
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    Statista (2025). U.S. wage and salary workers median hourly earnings 1979-2023 [Dataset]. https://www.statista.com/statistics/185335/median-hourly-earnings-of-wage-and-salary-workers/
    Explore at:
    Dataset updated
    Jan 14, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, the median hourly earnings of wage and salary workers in the United States was 19.24 U.S. dollars. This is an increase from 1979, when median hourly earnings were at 4.44 U.S. dollars. Hourly Workers The United States national minimum wage is 7.25 U.S. dollars per hour, which has been the minimum wage since 2009. However, each state has the agency to set their state minimum wage. Furthermore, some cities are able to create their minimum wage. Many argue that the minimum wage is too low and should be raised, because it is not considered a living wage. There has been a movement to raise the minimum wage to 15 U.S. dollars per hour, called “Fight for 15” which began in the early 2010s. While there has been no movement at the federal level, some states have moved to increase their minimum wages, with at least three states and the District of Columbia setting minimum wage rates at or above 15 dollars per hour. More recently, some proponents of increasing the minimum wage say that 15 dollars is too low, and lawmakers should strive toward a higher goal, especially given that a 2021 analysis found that the minimum wage in the U.S. should be 22.88 U.S. dollars if it grew at the same rate as economic productivity. Salary Workers On the other hand, salary workers in the United States do not get paid on an hourly basis. The median weekly earnings of salary workers have significantly increased since 1979. Asian salary workers had the highest hourly earnings in the U.S. in 2021. Among female salary workers, those ages 45 to 54 years old had the highest median hourly earnings in 2021, likewise for male salary workers.

  10. Data from: Quarterly Census of Employment and Wages

    • icpsr.umich.edu
    Updated Oct 22, 2015
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    United States Department of Labor. Bureau of Labor Statistics (2015). Quarterly Census of Employment and Wages [Dataset]. https://www.icpsr.umich.edu/web/NADAC/studies/36312
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    Dataset updated
    Oct 22, 2015
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    United States Department of Labor. Bureau of Labor Statistics
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/36312/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/36312/terms

    Area covered
    United States
    Description

    The Quarterly Census of Employment and Wages (QCEW) program is a cooperative program involving the Bureau of Labor Statistics (BLS) of the United States Department of Labor and the State Employment Security Agencies (SESAs). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Publicly available data files include information on the number of establishments, monthly employment, and quarterly wages, by NAICS industry, by county, by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (national, State, and Metropolitan Statistical Area (MSA)). To download and analyze QCEW data, users can begin on the QCEW Databases page. Downloadable data are available in formats such as text and CSV. Data for the QCEW program that are classified using the North American Industry Classification System (NAICS) are available from 1990 forward, and on a more limited basis from 1975 to 1989. These data provide employment and wage information for arts-related NAICS industries, such as: Arts, entertainment, and recreation (NAICS Code 71) Performing arts and spectator sports Museums, historical sites, zoos, and parks Amusements, gambling, and recreation Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic design services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, book and music stores Book, periodical, and music stores Art dealers For years 1975-2000, data for the QCEW program provide employment and wage information for arts-related industries are based on the Standard Industrial Classification (SIC) system. These arts-related SIC industries include the following: Book stores (SIC 5942) Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums, Botanical, Zoological Gardens (SIC Code 84) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Services (SIC Code 78) The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit NAICS industry at the national, state, and county levels. At the national level, the QCEW program provides employment and wage data for almost every NAICS industry. At the State and area level, the QCEW program provides employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Wages represent total compensation paid during the calendar quarter, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). The QCEW program does provide partial information on agricultural industries and employees in private households. Data from the QCEW program serve as an important source for many BLS programs. The QCEW data are used as the benchmark source for employment by the Current Employment Statistics program and the Occupational Employment Statistics program. The UI administrative records collected under the QCEW program serve as a sampling frame for BLS establishment surveys. In addition, data from the QCEW program serve as a source to other Federal and State programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses QCEW data as the base for developing the wage and salary component of personal income. The Employment and Training Administration (ETA) of the Department of Labor and the SESAs use QCEW data to administer the employment security program. The QCEW data accurately reflect the ex

  11. T

    United States Wages and Salaries Growth

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States Wages and Salaries Growth [Dataset]. https://tradingeconomics.com/united-states/wage-growth
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    csv, json, xml, excelAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 31, 1960 - May 31, 2025
    Area covered
    United States
    Description

    Wages in the United States increased 4.72 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  12. T

    United States Average Hourly Wages in Manufacturing

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, United States Average Hourly Wages in Manufacturing [Dataset]. https://tradingeconomics.com/united-states/wages-in-manufacturing
    Explore at:
    csv, xml, excel, jsonAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 31, 1939 - Jun 30, 2025
    Area covered
    United States
    Description

    Wages in Manufacturing in the United States remained unchanged at 28.87 USD/Hour in June. This dataset provides - United States Average Hourly Wages in Manufacturing - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  13. d

    Income and Employment in the United States, 1983-1987 - Direct Download

    • datadiscoverystudio.org
    shapefile
    Updated Mar 1, 2006
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    National Atlas of the United States (2006). Income and Employment in the United States, 1983-1987 - Direct Download [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/ebaf6897ef0249678db650037e36c799/html
    Explore at:
    shapefileAvailable download formats
    Dataset updated
    Mar 1, 2006
    Dataset authored and provided by
    National Atlas of the United States
    Area covered
    United States,
    Description

    This map layer portrays 1983 to 1987 estimates for total personal income, per capita personal income, annual number of full-time and part- time jobs, average wage per job in dollars, population, and per capita number of jobs, for counties in the United States. Total personal income is all the income that is received by, or on behalf of, the residents of a particular area. It is calculated as the sum of wage and salary disbursements, other labor income, proprietors' income with inventory valuation and capital consumption adjustments, rental income of persons with capital consumption adjustment, personal dividend income, personal interest income, and transfer payments to persons, minus personal contributions for social insurance. Per capita personal income is calculated as the total personal income of the residents of a county divided by the resident population of the county. The Census Bureau's annual midyear population estimates were used in the computation. The average annual number of full-time and part-time jobs includes all jobs for which wages and salaries are paid, except jury and witness service and paid employment of prisoners. The jobs are counted at equal weight, and employees, sole proprietors, and active partners are all included. Unpaid family workers and volunteers are not included. Average wage per job is the wage and salary disbursements divided by the number of wage and salary jobs in the county. Wage and salary disbursements consist of the monetary remuneration of employees, including the compensation of corporate officers; commissions, tips, and bonuses; and receipts in kind, or pay-in-kind, such as the meals furnished to the employees of restaurants. It reflects the amount of payments disbursed, but not necessarily earned during the year. Per capita number of jobs is calculated as the average annual number of full-time and part-time jobs in a county divided by the resident population of the county. The Census Bureau's annual midyear population estimates were used in the computation. All dollar estimates are in current dollars, not adjusted for inflation. The information in this map layer comes from the Regional Economic Information System (REIS) that is distributed by the Bureau of Economic Analysis, http://www.bea.gov/. This is an updated version of the November 2004 map layer.

  14. N

    National City, CA annual median income by work experience and sex dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
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    Neilsberg Research (2025). National City, CA annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a52ac7a5-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    National City, California
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in National City. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

    Key observations: Insights from 2023

    Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In National City, the median income for all workers aged 15 years and older, regardless of work hours, was $35,194 for males and $24,782 for females.

    These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 30% between the median incomes of males and females in National City. With women, regardless of work hours, earning 70 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of National City.

    - Full-time workers, aged 15 years and older: In National City, among full-time, year-round workers aged 15 years and older, males earned a median income of $47,336, while females earned $40,843, resulting in a 14% gender pay gap among full-time workers. This illustrates that women earn 86 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of National City.

    Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in National City.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

    Gender classifications include:

    • Male
    • Female

    Employment type classifications include:

    • Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
    • Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

    Variables / Data Columns

    • Year: This column presents the data year. Expected values are 2010 to 2023
    • Male Total Income: Annual median income, for males regardless of work hours
    • Male FT Income: Annual median income, for males working full time, year-round
    • Male PT Income: Annual median income, for males working part time
    • Female Total Income: Annual median income, for females regardless of work hours
    • Female FT Income: Annual median income, for females working full time, year-round
    • Female PT Income: Annual median income, for females working part time

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for National City median household income by race. You can refer the same here

  15. N

    National Park, NJ annual median income by work experience and sex dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
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    Neilsberg Research (2025). National Park, NJ annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/national-park-nj-income-by-gender/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

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

    Area covered
    National Park, New Jersey
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in National Park. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

    Key observations: Insights from 2023

    Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In National Park, the median income for all workers aged 15 years and older, regardless of work hours, was $46,250 for males and $35,217 for females.

    These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 24% between the median incomes of males and females in National Park. With women, regardless of work hours, earning 76 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in theborough of National Park.

    - Full-time workers, aged 15 years and older: In National Park, among full-time, year-round workers aged 15 years and older, males earned a median income of $66,094, while females earned $50,375, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

    Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in National Park, showcasing a consistent income pattern irrespective of employment status.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

    Gender classifications include:

    • Male
    • Female

    Employment type classifications include:

    • Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
    • Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

    Variables / Data Columns

    • Year: This column presents the data year. Expected values are 2010 to 2023
    • Male Total Income: Annual median income, for males regardless of work hours
    • Male FT Income: Annual median income, for males working full time, year-round
    • Male PT Income: Annual median income, for males working part time
    • Female Total Income: Annual median income, for females regardless of work hours
    • Female FT Income: Annual median income, for females working full time, year-round
    • Female PT Income: Annual median income, for females working part time

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for National Park median household income by race. You can refer the same here

  16. d

    Median Earnings of Creative Sector Occupations, CLL.B.1

    • datasets.ai
    • data.austintexas.gov
    • +3more
    23, 40, 55, 8
    Updated Aug 29, 2024
    + more versions
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    City of Austin (2024). Median Earnings of Creative Sector Occupations, CLL.B.1 [Dataset]. https://datasets.ai/datasets/median-earnings-of-creative-sector-occupations-cll-b-1
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    55, 23, 8, 40Available download formats
    Dataset updated
    Aug 29, 2024
    Dataset authored and provided by
    City of Austin
    Description

    Median earnings of creative sector occupations tells us how much Austin's artists, musicians, and related creative sector workers are earning per hour. It shows us whether our city's artists and creative sector workers can afford to live and work in Austin.

    This data reflects metric CLL.B1 within the City of Austin's Strategic Direction 2023. This metric is meant to inform City efforts in pursuing the Culture and Lifelong Learning Strategic Outcome. The complete metric title is "Median earnings of metro-area creative sector occupations (as defined by specific Bureau of Labor Statistics Standard Occupational Classifications System [SOC] codes)"

    Data was gathered directly from the Creative Vitality Suite (cvsuite.org), a subscription-based online research-based, economic development tool that provides creative economy data and reporting. Data extracted from CVSuite software's data analysis from the following sources: Economic Modeling Specialists International (EMSI), National Assembly of State Arts Agencies, National Center for Charitable Statistics.

  17. Low-paying sectors review

    • gov.uk
    • s3.amazonaws.com
    Updated Sep 11, 2023
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    Low Pay Commission (2023). Low-paying sectors review [Dataset]. https://www.gov.uk/government/publications/low-paying-sectors-review
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    Dataset updated
    Sep 11, 2023
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Low Pay Commission
    Description

    At the Low Pay Commission, we analyse the low-paid labour market to monitor the impact of the National Minimum Wage. To this end, we want to identify the businesses and workers who are most affected by the minimum wage.

    To help us identify these workers and businesses, we use two definitions: low-paying occupations relate to job roles that are often low-paid – for example, ‘sales assistants’; low-paying industries are based on the main activity of the employer – for example, ‘retail trade’.

    The definitions were last updated in 2017, shortly after the introduction of the National Living Wage (NLW). A lot has changed since then: the level of the minimum wage has increased rapidly, potentially changing the types of workers and businesses affected by it. The ONS has also updated how it classifies occupations, moving to a new set of standard occupational codes (SOC 2020) in the datasets we use. This move was completed for the Annual Survey of Hours and Earnings (ASHE) – our main data source for hourly pay – in autumn 2022.

    To make sure our work keeps up with these changes – and remains relevant once the NLW meets its target in 2024 – we have reviewed and updated our definitions of low-paying occupations and industries. This page publishes tables with full details of the new occupation and industry groups. It also contains data tables related to https://minimumwage.blog.gov.uk/2023/09/11/the-lpc-has-updated-its-definitions-of-low-paying-sectors/" class="govuk-link">a blog we have recently published explaining these changes.

  18. Employee wages by industry, annual

    • www150.statcan.gc.ca
    • open.canada.ca
    • +2more
    Updated Jan 24, 2025
    + more versions
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    Government of Canada, Statistics Canada (2025). Employee wages by industry, annual [Dataset]. http://doi.org/10.25318/1410006401-eng
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    Dataset updated
    Jan 24, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

  19. U.S. inflation rate versus wage growth 2020-2025

    • statista.com
    • svd-g.ru
    Updated May 8, 2025
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    Statista (2025). U.S. inflation rate versus wage growth 2020-2025 [Dataset]. https://www.statista.com/statistics/1351276/wage-growth-vs-inflation-us/
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    Dataset updated
    May 8, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2020 - Mar 2025
    Area covered
    United States
    Description

    In March 2025, inflation amounted to 2.4 percent, while wages grew by 4.3 percent. The inflation rate has not exceeded the rate of wage growth since January 2023. Inflation in 2022 The high rates of inflation in 2022 meant that the real terms value of American wages took a hit. Many Americans report feelings of concern over the economy and a worsening of their financial situation. The inflation situation in the United States is one that was experienced globally in 2022, mainly due to COVID-19 related supply chain constraints and disruption due to the Russian invasion of Ukraine. The monthly inflation rate for the U.S. reached a 40-year high in June 2022 at 9.1 percent, and annual inflation for 2022 reached eight percent. Without appropriate wage increases, Americans will continue to see a decline in their purchasing power. Wages in the U.S. Despite the level of wage growth reaching 6.7 percent in the summer of 2022, it has not been enough to curb the impact of even higher inflation rates. The federally mandated minimum wage in the United States has not increased since 2009, meaning that individuals working minimum wage jobs have taken a real terms pay cut for the last twelve years. There are discrepancies between states - the minimum wage in California can be as high as 15.50 U.S. dollars per hour, while a business in Oklahoma may be as low as two U.S. dollars per hour. However, even the higher wage rates in states like California and Washington may be lacking - one analysis found that if minimum wage had kept up with productivity, the minimum hourly wage in the U.S. should have been 22.88 dollars per hour in 2021. Additionally, the impact of decreased purchasing power due to inflation will impact different parts of society in different ways with stark contrast in average wages due to both gender and race.

  20. Employment-to-population ratio worldwide 2025, by region

    • statista.com
    • ai-chatbox.pro
    Updated May 30, 2025
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    Statista (2025). Employment-to-population ratio worldwide 2025, by region [Dataset]. https://www.statista.com/statistics/1258882/global-employment-rate-by-region/
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    Dataset updated
    May 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Worldwide
    Description

    In 2024, the employment-to-population ratio worldwide was estimated to be approximately 58 percent, indicating that nearly 60 percent of the global population above 15 years was employed. Among the provided regions, Africa had the highest employment-to-population ratio, at 60 percent, with Europe and Central Asia having the lowest at 55 percent. Global income growth As greater portions of the population hold stable employment over time, income has also grown globally. From 1970 until today, North America has seen the largest increase in net national incomes per capita, but this increase has occurred in other regions as well. In terms of real wages, while they have grown over time, they have experienced a slight decrease in light of the high global inflation rates. Decrease in child labor Even though greater proportions of the population are employed, child labor has decreased over time. In 2000, there were 245 million children working, which has decreased to 160 million by 2020. The majority of working children are in the agricultural sector, especially younger children within the 5-11 and 12-14 age groups.

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(2025). Average Hourly Earnings of All Employees, Total Private [Dataset]. https://fred.stlouisfed.org/series/CES0500000003

Average Hourly Earnings of All Employees, Total Private

CES0500000003

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45 scholarly articles cite this dataset (View in Google Scholar)
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.

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