100+ datasets found
  1. F

    Employed full time: Median usual weekly real earnings: Wage and salary...

    • fred.stlouisfed.org
    json
    Updated Jul 22, 2025
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    (2025). Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over [Dataset]. https://fred.stlouisfed.org/series/LEU0252881600Q
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 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 real earnings: Wage and salary workers: 16 years and over (LEU0252881600Q) from Q1 1979 to Q2 2025 about full-time, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

  2. Quarterly Census of Employment and Wages (QCEW)

    • catalog.data.gov
    • data.ca.gov
    • +1more
    Updated Jul 23, 2025
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    California Employment Development Department (2025). Quarterly Census of Employment and Wages (QCEW) [Dataset]. https://catalog.data.gov/dataset/quarterly-census-of-employment-and-wages-qcew-a6fea
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    Dataset updated
    Jul 23, 2025
    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.

  3. F

    Average Hourly Earnings of All Employees, Total Private

    • fred.stlouisfed.org
    json
    Updated Aug 1, 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
    Aug 1, 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 Jul 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

  4. 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

  5. F

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

    • fred.stlouisfed.org
    json
    Updated Jul 31, 2025
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    (2025). Employment Cost Index: Wages and Salaries: Private Industry Workers: Manufacturing [Dataset]. https://fred.stlouisfed.org/series/ECIMANWAG
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    jsonAvailable download formats
    Dataset updated
    Jul 31, 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: Manufacturing (ECIMANWAG) from Q1 2001 to Q2 2025 about ECI, salaries, workers, private industries, wages, private, manufacturing, industry, inflation, and USA.

  6. 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

  7. T

    United States Wages and Salaries Growth

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 14, 2025
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    TRADING ECONOMICS (2025). United States Wages and Salaries Growth [Dataset]. https://tradingeconomics.com/united-states/wage-growth
    Explore at:
    csv, json, xml, excelAvailable download formats
    Dataset updated
    Jun 14, 2025
    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 - Jun 30, 2025
    Area covered
    United States
    Description

    Wages in the United States increased 4.78 percent in June 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.

  8. 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.

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

    • statista.com
    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.

  10. 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: Miscellaneous agricultural workers occupations: 16 years and over [Dataset]. https://fred.stlouisfed.org/series/LEU0254558000A
    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: Miscellaneous agricultural workers occupations: 16 years and over (LEU0254558000A) from 2000 to 2024 about second quartile, miscellaneous, occupation, full-time, agriculture, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

  11. United States Monthly Earnings

    • ceicdata.com
    Updated Mar 21, 2025
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    CEICdata.com (2025). United States Monthly Earnings [Dataset]. https://www.ceicdata.com/en/indicator/united-states/monthly-earnings
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    Dataset updated
    Mar 21, 2025
    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
    Mar 1, 2024 - Feb 1, 2025
    Area covered
    United States
    Description

    Key information about United States Monthly Earnings

    • United States Monthly Earnings stood at 4,901 USD in Feb 2025, compared with the previous figure of 4,887 USD in Jan 2025
    • US Monthly Earnings data is updated monthly, available from Mar 2006 to Feb 2025, with an average number of 3,469 USD
    • The data reached the an all-time high of 4,901 USD in Feb 2025 and a record low of 2,743 USD in Mar 2006

    CEIC calculates Monthly Earnings from Average Weekly Earnings multiplied by 4. U.S. Bureau of Labor Statistics provides Average Weekly Earnings in USD. Monthly Earnings include Private Non Agricultural sector only.


    Further information about United States Monthly Earnings

    • In the latest reports, US Population reached 341 million people in Dec 2024
    • Unemployment Rate of US increased to 4 % in Feb 2025
    • The country's Labour Force Participation Rate remained the same rate at 62 % in Feb 2025

  12. T

    United States Average Hourly Wages in Manufacturing

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Mar 15, 2025
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    TRADING ECONOMICS (2025). 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 updated
    Mar 15, 2025
    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. F

    Employed full time: Median usual weekly real earnings: Wage and salary...

    • fred.stlouisfed.org
    json
    Updated Jan 22, 2025
    + more versions
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    (2025). Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over: Men [Dataset]. https://fred.stlouisfed.org/series/LEU0252881900A
    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 real earnings: Wage and salary workers: 16 years and over: Men (LEU0252881900A) from 1979 to 2024 about full-time, males, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

  14. N

    Great Scott Township, Minnesota annual median income by work experience and...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
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    Neilsberg Research (2025). Great Scott Township, Minnesota 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/great-scott-township-mn-income-by-gender/
    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
    Great Scott Township, Minnesota
    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 Great Scott township. 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 Great Scott township, the median income for all workers aged 15 years and older, regardless of work hours, was $57,250 for males and $22,625 for females.

    These income figures highlight a substantial gender-based income gap in Great Scott township. Women, regardless of work hours, earn 40 cents for each dollar earned by men. This significant gender pay gap, approximately 60%, underscores concerning gender-based income inequality in the township of Great Scott township.

    - Full-time workers, aged 15 years and older: In Great Scott township, among full-time, year-round workers aged 15 years and older, males earned a median income of $90,750, while females earned $47,250, leading to a 48% gender pay gap among full-time workers. This illustrates that women earn 52 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment 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 Great Scott township, 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 Great Scott township median household income by race. You can refer the same here

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

    • statista.com
    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/
    Explore at:
    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.

  16. N

    Denmark, SC annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
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    Neilsberg Research (2025). Denmark, SC 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/denmark-sc-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
    Denmark, South Carolina
    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 Denmark. 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 Denmark, the median income for all workers aged 15 years and older, regardless of work hours, was $15,675 for males and $15,526 for females.

    Based on these incomes, we observe a gender gap percentage of approximately 1%, indicating a significant disparity between the median incomes of males and females in Denmark. Women, regardless of work hours, still earn 99 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

    - Full-time workers, aged 15 years and older: In Denmark, among full-time, year-round workers aged 15 years and older, males earned a median income of $46,691, while females earned $39,911, resulting in a 15% gender pay gap among full-time workers. This illustrates that women earn 85 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 Denmark.

    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 Denmark, 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 Denmark median household income by race. You can refer the same here

  17. F

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

    • fred.stlouisfed.org
    json
    Updated Jul 22, 2025
    + more versions
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    (2025). Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: 16 years and over: Asian [Dataset]. https://fred.stlouisfed.org/series/LEU0254468400Q
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 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: Asian (LEU0254468400Q) from Q1 2000 to Q2 2025 about second quartile, full-time, asian, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

  18. N

    Cutler, Maine annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    Neilsberg Research (2025). Cutler, Maine 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/cutler-me-income-by-gender/
    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
    Maine, Cutler
    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 Cutler town. 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 Cutler town, the median income for all workers aged 15 years and older, regardless of work hours, was $68,750 for males and $29,375 for females.

    These income figures highlight a substantial gender-based income gap in Cutler town. Women, regardless of work hours, earn 43 cents for each dollar earned by men. This significant gender pay gap, approximately 57%, underscores concerning gender-based income inequality in the town of Cutler town.

    - Full-time workers, aged 15 years and older: In Cutler town, among full-time, year-round workers aged 15 years and older, males earned a median income of $72,105, while females earned $41,313, leading to a 43% gender pay gap among full-time workers. This illustrates that women earn 57 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment 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 Cutler town, 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 Cutler town median household income by race. You can refer the same here

  19. N

    Scottdale, PA annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    Close
    Cite
    Neilsberg Research (2025). Scottdale, PA 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/a535d50f-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
    Scottdale, Pennsylvania
    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 Scottdale. 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 Scottdale, the median income for all workers aged 15 years and older, regardless of work hours, was $44,827 for males and $29,694 for females.

    These income figures highlight a substantial gender-based income gap in Scottdale. Women, regardless of work hours, earn 66 cents for each dollar earned by men. This significant gender pay gap, approximately 34%, underscores concerning gender-based income inequality in the borough of Scottdale.

    - Full-time workers, aged 15 years and older: In Scottdale, among full-time, year-round workers aged 15 years and older, males earned a median income of $63,571, while females earned $41,770, leading to a 34% gender pay gap among full-time workers. This illustrates that women earn 66 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment 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 Scottdale, 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 Scottdale median household income by race. You can refer the same here

  20. N

    Fremont, CA annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Neilsberg Research (2025). Fremont, 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/insights/fremont-ca-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
    Fremont, 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 Fremont. 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 Fremont, the median income for all workers aged 15 years and older, regardless of work hours, was $99,996 for males and $55,990 for females.

    These income figures highlight a substantial gender-based income gap in Fremont. Women, regardless of work hours, earn 56 cents for each dollar earned by men. This significant gender pay gap, approximately 44%, underscores concerning gender-based income inequality in the city of Fremont.

    - Full-time workers, aged 15 years and older: In Fremont, among full-time, year-round workers aged 15 years and older, males earned a median income of $136,764, while females earned $101,991, leading to a 25% gender pay gap among full-time workers. This illustrates that women earn 75 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.

    Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Fremont.

    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 Fremont median household income by race. You can refer the same here

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
(2025). Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over [Dataset]. https://fred.stlouisfed.org/series/LEU0252881600Q

Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over

LEU0252881600Q

Explore at:
31 scholarly articles cite this dataset (View in Google Scholar)
jsonAvailable download formats
Dataset updated
Jul 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 real earnings: Wage and salary workers: 16 years and over (LEU0252881600Q) from Q1 1979 to Q2 2025 about full-time, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

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