100+ datasets found
  1. U.S. wage and salary accruals 2023, by industry

    • statista.com
    Updated Oct 15, 2024
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    U.S. wage and salary accruals 2023, by industry [Dataset]. https://www.statista.com/statistics/243834/annual-mean-wages-and-salary-per-employee-in-the-us-by-industry/
    Explore at:
    Dataset updated
    Oct 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, the average wage and salary per full-time equivalent employee in the mining industry in the United States was at 126,707 U.S. dollars. The highest wage and salary per FTE was found in the information industry, at 164,400 U.S. dollars.

  2. F

    Median Personal Income in the United States

    • fred.stlouisfed.org
    json
    Updated Sep 10, 2024
    + more versions
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    (2024). Median Personal Income in the United States [Dataset]. https://fred.stlouisfed.org/series/MEPAINUSA646N
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 10, 2024
    License

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

    Area covered
    United States
    Description

    Graph and download economic data for Median Personal Income in the United States (MEPAINUSA646N) from 1974 to 2023 about personal income, personal, median, income, and USA.

  3. U.S. median household income 1990-2023

    • statista.com
    Updated Sep 16, 2024
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    Statista (2024). U.S. median household income 1990-2023 [Dataset]. https://www.statista.com/statistics/200838/median-household-income-in-the-united-states/
    Explore at:
    Dataset updated
    Sep 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

  4. d

    Average Salary by Job Classification

    • catalog.data.gov
    • data.montgomerycountymd.gov
    Updated Sep 15, 2023
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    data.montgomerycountymd.gov (2023). Average Salary by Job Classification [Dataset]. https://catalog.data.gov/dataset/average-salary-by-job-classification
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    Dataset updated
    Sep 15, 2023
    Dataset provided by
    data.montgomerycountymd.gov
    Description

    This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

  5. Average salary of an employee in urban China 2013-2023

    • statista.com
    • flwrdeptvarieties.store
    Updated May 29, 2024
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    Statista (2024). Average salary of an employee in urban China 2013-2023 [Dataset]. https://www.statista.com/statistics/278349/average-annual-salary-of-an-employee-in-china/
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    Dataset updated
    May 29, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    China
    Description

    In 2023, an employee working for a not privately held company or organization in urban areas of China earned around 120,698 yuan annually on average. That year, the year-on-year growth rate of the average salary ranged at 5.8 percent nominally and 5.5 percent real.

    Regional differences in salary levels

    Salary levels in urban China have seen a significant rise between 2013 and 2023. During that period, average annual salaries of employees in non-private urban units have grown from around 51,500 to 120,700 yuan per year. Yet as of 2022, large income disparities still existed between different regions in China. While employees in Shanghai enjoyed the highest annual salaries on average, Henan province and Jilin province in central and northeastern China displayed the lowest average annual salaries. Regions with lower income levels are mainly located in central China or in the former centers of steel and heavy industry in Northeast China, whereas the coastal regions and municipalities in general still provide comparatively higher salaries.

    Occupational salary differences

    Moreover, a considerable salary discrepancy exists between different occupational groups in China. As of 2023, people working in IT services were atop the list earning about 231,800 yuan per year on average, whereas people in the financial service sector reached approximately 197,700 yuan. The hotels and catering sector ranked at the lower end with an average annual salary of around 58,100 yuan.

  6. U.S. annual mean wages and salary per employee 2000-2023

    • statista.com
    Updated Oct 15, 2024
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    Statista (2024). U.S. annual mean wages and salary per employee 2000-2023 [Dataset]. https://www.statista.com/statistics/243842/annual-mean-wages-and-salary-per-employee-in-the-us/
    Explore at:
    Dataset updated
    Oct 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, the average wage and salary per full-time equivalent employee in the United States was at 81,359 U.S. dollars. Wage and salary accruals include executives' compensation, bonuses, tips, and payments-in-kind. Wage and salary accruals by industry data may be accessed here.

  7. U

    United States Monthly Earnings

    • ceicdata.com
    Updated Feb 15, 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
    Feb 15, 2025
    Dataset provided by
    CEICdata.com
    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

  8. F

    Income Before Taxes: Wages and Salaries by Age: from Age 25 to 34

    • fred.stlouisfed.org
    json
    Updated Sep 25, 2024
    + more versions
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    (2024). Income Before Taxes: Wages and Salaries by Age: from Age 25 to 34 [Dataset]. https://fred.stlouisfed.org/series/CXU900000LB0403M
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 25, 2024
    License

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

    Description

    Graph and download economic data for Income Before Taxes: Wages and Salaries by Age: from Age 25 to 34 (CXU900000LB0403M) from 1984 to 2023 about age, 25 years +, salaries, tax, wages, income, and USA.

  9. T

    Vital Signs: Jobs by Wage Level - Subregion

    • data.bayareametro.gov
    application/rdfxml +5
    Updated Jan 18, 2019
    + more versions
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    (2019). Vital Signs: Jobs by Wage Level - Subregion [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Jobs-by-Wage-Level-Subregion/yc3r-a4rh
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    json, xml, csv, application/rdfxml, tsv, application/rssxmlAvailable download formats
    Dataset updated
    Jan 18, 2019
    Description

    VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

    FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

    LAST UPDATED January 2019

    DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

    DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

    American Community Survey (2001-2017) http://api.census.gov

    CONTACT INFORMATION vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

    Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

    Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

    Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

    In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

  10. T

    China Average Yearly Wages

    • tradingeconomics.com
    • sv.tradingeconomics.com
    • +17more
    csv, excel, json, xml
    Updated Feb 17, 2025
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    TRADING ECONOMICS (2025). China Average Yearly Wages [Dataset]. https://tradingeconomics.com/china/wages
    Explore at:
    json, xml, csv, excelAvailable download formats
    Dataset updated
    Feb 17, 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
    Dec 31, 1952 - Dec 31, 2023
    Area covered
    China
    Description

    Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  11. B

    Belgium Average Monthly Salary: Age: 30 to 34

    • ceicdata.com
    Updated Feb 18, 2018
    + more versions
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    CEICdata.com (2018). Belgium Average Monthly Salary: Age: 30 to 34 [Dataset]. https://www.ceicdata.com/en/belgium/average-monthly-salary/average-monthly-salary-age-30-to-34
    Explore at:
    Dataset updated
    Feb 18, 2018
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Sep 1, 2010 - Sep 1, 2021
    Area covered
    Belgium
    Variables measured
    Wage/Earnings
    Description

    Belgium Average Monthly Salary: Age: 30 to 34 data was reported at 3,467.000 EUR in 2021. This records an increase from the previous number of 3,422.000 EUR for 2020. Belgium Average Monthly Salary: Age: 30 to 34 data is updated yearly, averaging 2,843.000 EUR from Sep 1999 (Median) to 2021, with 23 observations. The data reached an all-time high of 3,467.000 EUR in 2021 and a record low of 2,111.000 EUR in 1999. Belgium Average Monthly Salary: Age: 30 to 34 data remains active status in CEIC and is reported by Directorate-General Statistics - Statistics Belgium. The data is categorized under Global Database’s Belgium – Table BE.G036: Average Monthly Salary.

  12. N

    Riverside, CA 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). Riverside, 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/riverside-ca-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
    Riverside, 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 Riverside. 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 Riverside, the median income for all workers aged 15 years and older, regardless of work hours, was $42,921 for males and $30,743 for females.

    These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 28% between the median incomes of males and females in Riverside. With women, regardless of work hours, earning 72 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Riverside.

    - Full-time workers, aged 15 years and older: In Riverside, among full-time, year-round workers aged 15 years and older, males earned a median income of $57,855, while females earned $50,953, resulting in a 12% gender pay gap among full-time workers. This illustrates that women earn 88 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 Riverside.

    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 Riverside.

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

  13. F

    Mean Personal Income in the United States

    • fred.stlouisfed.org
    json
    Updated Sep 10, 2024
    + more versions
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    (2024). Mean Personal Income in the United States [Dataset]. https://fred.stlouisfed.org/series/MAPAINUSA646N
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 10, 2024
    License

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

    Area covered
    United States
    Description

    Graph and download economic data for Mean Personal Income in the United States (MAPAINUSA646N) from 1974 to 2023 about average, personal income, personal, income, and USA.

  14. N

    Rochester, NY 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). Rochester, NY 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/rochester-ny-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
    Rochester, New York
    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 Rochester. 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 Rochester, the median income for all workers aged 15 years and older, regardless of work hours, was $32,132 for males and $28,571 for females.

    Based on these incomes, we observe a gender gap percentage of approximately 11%, indicating a significant disparity between the median incomes of males and females in Rochester. Women, regardless of work hours, still earn 89 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

    - Full-time workers, aged 15 years and older: In Rochester, among full-time, year-round workers aged 15 years and older, males earned a median income of $52,701, while females earned $47,224, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 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 Rochester.

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

  15. T

    United States Wages and Salaries Growth

    • tradingeconomics.com
    • pl.tradingeconomics.com
    • +17more
    csv, excel, json, xml
    Updated May 6, 2023
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    TRADING ECONOMICS (2023). United States Wages and Salaries Growth [Dataset]. https://tradingeconomics.com/united-states/wage-growth
    Explore at:
    csv, json, xml, excelAvailable download formats
    Dataset updated
    May 6, 2023
    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 - Jan 31, 2025
    Area covered
    United States
    Description

    Wages in the United States increased 4.46 percent in January 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.

  16. U.S. median annual wage 2023, by major occupational group

    • statista.com
    Updated Aug 27, 2024
    + more versions
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    Statista (2024). U.S. median annual wage 2023, by major occupational group [Dataset]. https://www.statista.com/statistics/218235/median-annual-wage-in-the-us-by-major-occupational-groups/
    Explore at:
    Dataset updated
    Aug 27, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 2023
    Area covered
    United States
    Description

    As of 2023, the median wage for employees in healthcare support occupations was about 36,140 U.S. dollars. The occupational group with the highest annual median wage was management occupations. Mean wages for the same occupational groups can be accessed here.

  17. N

    Cuba, KS annual median income by work experience and sex dataset: Aged 15+,...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
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    Neilsberg Research (2025). Cuba, KS 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/cuba-ks-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
    Kansas, Cuba
    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 Cuba. 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 Cuba, the median income for all workers aged 15 years and older, regardless of work hours, was $35,542 for males and $39,583 for females.

    Contrary to expectations, women in Cuba, women, regardless of work hours, earn a higher income than men, earning 1.11 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

    - Full-time workers, aged 15 years and older: In Cuba, among full-time, year-round workers aged 15 years and older, males earned a median income of $35,972, while females earned $65,259

    Contrary to expectations, in Cuba, women, earn a higher income than men, earning 1.81 dollars for every dollar earned by men. This analysis showcase a consistent trend of women outearning men, when working full-time or part-time in the city of Cuba.

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

  18. T

    Russia Average Monthly Wages

    • tradingeconomics.com
    • sv.tradingeconomics.com
    • +16more
    csv, excel, json, xml
    Updated Nov 6, 2022
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    TRADING ECONOMICS (2022). Russia Average Monthly Wages [Dataset]. https://tradingeconomics.com/russia/wages
    Explore at:
    excel, csv, xml, jsonAvailable download formats
    Dataset updated
    Nov 6, 2022
    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, 1990 - Dec 31, 2024
    Area covered
    Russia
    Description

    Wages in Russia increased to 128665 RUB/Month in December from 86399 RUB/Month in November of 2024. This dataset provides the latest reported value for - Russia Average Monthly Wages - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  19. f

    Data from: Average salary

    • f1hire.com
    Updated Sep 29, 2024
    + more versions
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    F1 Hire (2024). Average salary [Dataset]. https://www.f1hire.com/major/Computer%20Science%20Also%20Has%20A%204%20Year%20Bachelors%20Degree%20In%20Computer%20Engineering
    Explore at:
    Dataset updated
    Sep 29, 2024
    Dataset authored and provided by
    F1 Hire
    Description

    Explore the progression of average salaries for graduates in Computer Science Also Has A 4 Year Bachelors Degree In Computer Engineering from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Computer Science Also Has A 4 Year Bachelors Degree In Computer Engineering relative to other fields. This data is essential for students assessing the return on investment of their education in Computer Science Also Has A 4 Year Bachelors Degree In Computer Engineering, providing a clear picture of financial prospects post-graduation.

  20. N

    Jordan, MT Median Income by Age Groups Dataset: A Comprehensive Breakdown of...

    • neilsberg.com
    csv, json
    Updated Aug 7, 2024
    + more versions
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    Neilsberg Research (2024). Jordan, MT Median Income by Age Groups Dataset: A Comprehensive Breakdown of Jordan Annual Median Income Across 4 Key Age Groups // 2024 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a3defaeb-54ae-11ef-a42e-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Aug 7, 2024
    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
    Jordan
    Variables measured
    Income for householder under 25 years, Income for householder 65 years and over, Income for householder between 25 and 44 years, Income for householder between 45 and 64 years
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It delineates income distributions across four age groups (Under 25 years, 25 to 44 years, 45 to 64 years, and 65 years and over) following an initial analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series via current methods (R-CPI-U-RS). 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 the distribution of median household income among distinct age brackets of householders in Jordan. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Jordan. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

    Key observations: Insights from 2022

    In terms of income distribution across age cohorts, Jordan only reports a median household income of $56,401 among householders in the 45 to 64 years age group.

    Content

    When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

    Age groups classifications include:

    • Under 25 years
    • 25 to 44 years
    • 45 to 64 years
    • 65 years and over

    Variables / Data Columns

    • Age Of The Head Of Household: This column presents the age of the head of household
    • Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

    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 Jordan median household income by age. You can refer the same here

Share
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Email
Click to copy link
Link copied
Close
Cite
U.S. wage and salary accruals 2023, by industry [Dataset]. https://www.statista.com/statistics/243834/annual-mean-wages-and-salary-per-employee-in-the-us-by-industry/
Organization logo

U.S. wage and salary accruals 2023, by industry

Explore at:
6 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Oct 15, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2023
Area covered
United States
Description

In 2023, the average wage and salary per full-time equivalent employee in the mining industry in the United States was at 126,707 U.S. dollars. The highest wage and salary per FTE was found in the information industry, at 164,400 U.S. dollars.

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