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Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from...

In 2023, the median annual earnings of full-time male workers in the United States stood at ****** U.S. dollars after being adjusted for inflation, which was significantly higher than the median earnings of full-time women at ******. For further reading, see the female to male earnings ratio.

Since 2000, the share of the world's total labor income before tax earned by women fluctuated between ***** percent to ***** percent. This is significantly less than their male counterparts. There are also differences between the world regions.

Graph and download economic data for Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over: Women (LES1252882800Q) from Q1 1979 to Q2 2025 about full-time, females, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

Graph and download economic data for Employed part time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: 16 years and over: Black or African American: Women (LEU0262885100Q) from Q1 2000 to Q2 2025 about part-time, second quartile, African-American, females, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

In Sweden, men earn slightly more than women. From 2016 to 2021, women's average earnings in the country were 96 percent of that of their male counterparts, but dropped to 95 percent in 2022. This is despite the fact that women have a higher educational level than men.Unequal pay, a frequently observed problem Even though women in Sweden earn less on average than men, the gender pay gap is smaller in Sweden than in several other European countries. However, in a survey about attitudes towards gender equality, equal pay for equal work was considered the second most important issue facing women and girls in Sweden today. Gender difference in the distribution among occupations and sectors The main reason for the lack of equal pay for men and women is that the latter group tends to work in occupations where average salaries are lower than in those dominated by men. For instance, the average salary in the human health sector in Sweden is 35,000 Swedish kronor, compared to 57,200 in financial institutions and insurance companies. The health care sector in the country has a high share of female employees.

In 2025, the average annual full-time salary for men in the United Kingdom was 41,832 British pounds, compared with 35,670 pounds for women, a difference of 6,162 pounds. In the previous year, men earned an average annual salary of 40,053, compared with women who earned 34,001.

Average female and male earnings and female-to-male earnings ratio, Canada.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Alabama. The dataset can be utilized to gain insights into gender-based income distribution within the Alabama population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Alabama, among individuals aged 15 years and older with income, there were 1.76 million men and 1.81 million women in the workforce. Among them, 943.71 thousand men were engaged in full-time, year-round employment, while 736.67 thousand women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 9.92% fell within the income range of under $24,999, while 16.27% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 23.67% of men in full-time roles earned incomes exceeding $100,000, while 10.51% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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

Graph and download economic data for Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 to 24 years: Women (LEU0252883100Q) from Q1 2000 to Q2 2025 about 16 to 24 years, full-time, females, salaries, workers, earnings, wages, median, real, employment, and USA.

In 2023, the median hourly rate of a female wageworker in the United States between 20 and 24 years old was 15.88 current U.S. dollars. Women between the ages of 35 and 44 years old made the most in that year, with a median hourly wage of 19.84 current U.S. dollars.

This comprehensive indicator offers detailed insights into the average hourly earnings derived from paid employment across various dimensions, including sex, occupation, age, and disability status. By examining the interplay of these factors, the indicator provides a nuanced understanding of wage differentials within the workforce. This information is invaluable for assessing patterns of income inequality, identifying potential areas for policy intervention, and fostering a more inclusive and equitable employment environment. Through its multifaceted approach, the indicator enables a thorough analysis of how various demographic variables intersect with earnings, thereby contributing to a more holistic comprehension of labor market dynamics and the socioeconomic landscape.

PS I hope this dataset will answer many of your questions and will be trigger to many new ones. I will read every comment and notebooks as I do it every time and hope to see your mind blowing conclusions. Good luck and thank you for being here

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: 16 to 24 years: Women (LEU0252883000Q) from Q1 2000 to Q2 2025 about 16 to 24 years, second quartile, full-time, females, salaries, workers, earnings, wages, median, employment, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Bachelor's degree and higher: 25 years and over: Black or African American: Women (LEU0252943800A) from 2000 to 2024 about African-American, 25 years +, tertiary schooling, full-time, females, salaries, workers, education, wages, employment, and USA.

Median pay for women employed by the region as a percentage of median pay for men employed by the region. All salary reported is calculated as full-time salary, SEK/month. Full-time salary includes basic salary plus variable allowances and benefits. For employees aged 18-66 (until 2013 18-64 years) who are monthly or hourly paid. Employees of municipal-owned companies are not included.

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 Washington. 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 Washington, the median income for all workers aged 15 years and older, regardless of work hours, was $77,818 for males and $64,201 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 17% between the median incomes of males and females in Washington. With women, regardless of work hours, earning 83 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Washington.

- Full-time workers, aged 15 years and older: In Washington, among full-time, year-round workers aged 15 years and older, males earned a median income of $110,582, while females earned $95,422, resulting in a 14% gender pay gap among full-time workers. This illustrates that women earn 86 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Washington.

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 Washington.

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

Women and Men in Spain: Percentages of annual wages Woman's salary with respect to man's salary by period. Annual. National.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Hennepin County. The dataset can be utilized to gain insights into gender-based income distribution within the Hennepin County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Hennepin County, among individuals aged 15 years and older with income, there were 482.13 thousand men and 476.72 thousand women in the workforce. Among them, 274.00 thousand men were engaged in full-time, year-round employment, while 216,290 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 4.29% fell within the income range of under $24,999, while 5% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 40.47% of men in full-time roles earned incomes exceeding $100,000, while 28.57% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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

United States - Employed full time: Wage and salary workers: 25 to 34 years: Women was 13366.00000 Thous. of Persons in April of 2025, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: 25 to 34 years: Women reached a record high of 13524.00000 in April of 2023 and a record low of 10215.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: 25 to 34 years: Women - last updated from the United States Federal Reserve on November of 2025.

Indonesia Monthly Average Wage: Female data was reported at 2,178,134.000 IDR in 2018. This records an increase from the previous number of 2,070,274.000 IDR for 2017. Indonesia Monthly Average Wage: Female data is updated yearly, averaging 720,632.000 IDR from Aug 1994 (Median) to 2018, with 24 observations. The data reached an all-time high of 2,178,134.000 IDR in 2018 and a record low of 113,497.000 IDR in 1994. Indonesia Monthly Average Wage: Female data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GBB002: Monthly Average Wage: by Industry.