The difference between the earnings of women and men shrank slightly over the past years. Considering the controlled gender pay gap, which measures the median salary for men and women with the same job and qualifications, women earned one U.S. cent less. By comparison, the uncontrolled gender pay gap measures the median salary for all men and all women across all sectors and industries and regardless of location and qualification. In 2025, the uncontrolled gender pay gap in the world stood at 0.83, meaning that women earned 0.83 dollars for every dollar earned by men.

In 2021, female employee earnings were outpaced by male earnings across nearly all industries, with sharp disparities in the professional and technical services industry, as well as the finance and insurance industry. In that year, there were no industries in which women earned more than men.

The paper assesses gender differences in pre-labor market specialization among the college-educated and highlights how those differences have evolved over time. Women choose majors with lower potential earnings (based on male wages associated with those majors) and subsequently sort into occupations with lower potential earnings given their major choice. These differences have narrowed over time, but recent cohorts of women still choose majors and occupations with lower potential earnings. Differences in undergraduate major choice explain a substantive portion of gender wage gaps for the college-educated above and beyond simply controlling for occupation. Collectively, our results highlight the importance of understanding gender differences in the mapping between college major and occupational sorting when studying the evolution of gender differences in labor market outcomes over time.

The gender wage gap indicator compares the median earnings between male and female workers in Champaign County.

Two worker populations are analyzed: all workers, including part-time and seasonal workers and those that were not employed for the full survey year; and full-time, year-round workers. The gender wage gap is included because it blends economics and equity, and illustrates that a major economic talking point on the national level is just as relevant at the local scale.

For all four populations (male full-time, year-round workers; female full-time, year-round workers; all male workers; and all female workers), the estimated median earnings were higher in 2023 than in 2005. The greatest increase in a population’s estimated median earnings between 2005 and 2023 was for female full-time, year-round workers; the smallest increase between 2005 and 2023 was for all female workers. In both categories (all and full-time, year-round), the estimated median annual earnings for male workers was consistently higher than for female workers.

The gender gap between the two estimates in 2023 was larger for full-time, year-round workers than all workers. For full-time, year-round workers, the difference was $11,863; for all workers, it was approaching $9,700.

The Associated Press wrote this article in October 2024 about how Census Bureau data shows that in 2023 in the United States, the gender wage gap between men and women working full-time widened year-over-year for the first time in 20 years.

Income data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Median Earnings in the Past 12 Months (in 2020 Inflation-Adjusted Dollars) by Sex by Work Experience in the Past 12 Months for the Population 16 Years and Over with Earnings in the Past 12 Months.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (20 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (21 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S2001; generated by CCRPC staff; using American FactFinder; (13 September 2018).

As of 2023, South Korea is the country with the highest gender pay gap among OECD countries, with a **** percent difference between the genders. The gender pay gap displays the difference between the median wages of full-time employed men and full-time employed women.

This table describes gender pay gap and is defined as the ratio of the gross earnings between women and men. The disaggregation variables are subject to data availability and where the numbers are lesser than 6, the disaggregation will be dropped.

Find more Pacific data on PDH.stat.

In Italy, the percentage of the gender salary gap was the lowest for middle managers, while it was the largest for blue-collar workers. According to data provided by JobPricing, in 2024, male middle managers earned on average *** percent more than women, while for blue-collar workers, salaries were almost *** percent higher for men than for women.

Annual gender pay gap estimates for UK employees by age, occupation, industry, full-time and part-time, region and other geographies, and public and private sector. Compiled from the Annual Survey of Hours and Earnings.

In 2021, female elementary and middle school teachers earned on average 1,138 U.S. dollars per week, while their male counterparts earned 1,301 U.S. dollars. Male office supervisors made an average of 1,184 U.S. dollars per week, while female supervisors earned an average of 913 U.S. dollars.

This dataset provides valuable insights into the wage gap between Hispanic and White workers in the United States.

The wage gap is expressed as a percentage by which hourly wages of Hispanic workers are less than those of White workers.

It is an essential measure for understanding income disparities and examining trends over time.

Interesting Task Ideas:

1. Analyze the Hispanic-White wage gap trends over the years.
2. Explore the variations in median and average wages between Hispanic and White workers.
3. Investigate the gender-specific wage gap and identify any disparities between Hispanic and White men and women.
4. Conduct regression analysis to analyze the wage gap while controlling for relevant factors such as education, age, and geographic location.
5. Visualize the wage gap data to emphasize the magnitude of disparities and highlight any patterns over time.
6. Discover the potential effects of policy changes or economic transformations on the wage gap.

If you find this dataset insightful, don't forget to upvote it! 😊💝

Checkout my other datasets

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Productivity and Hourly Compensation

Photo by Clay Banks on Unsplash

Context

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

Content

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

Acknowledgements

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

Inspiration

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

Selection correction methods usually make assumptions about selection itself. In the case of gender wage gap estimation, those assumptions are especially tenuous because of high female nonparticipation and because selection could be different in different parts of the labor market. This paper proposes an estimator for the wage gap that allows for arbitrary and unobserved heterogeneity in selection. It applies to the subpopulation of always employed women, which is similar to men in labor force characteristics. Using CPS data from 1976 to 2005, I show that the gap has narrowed substantially from a ?0.521 to a ?0.263 log wage point differential for this population.

Using Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, we provide new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during this time. By 2010, conventional human capital variables taken together explained little of the gender wage gap, while gender differences in occupation and industry continued to be important. Moreover, the gender pay gap declined much more slowly at the top of the wage distribution than at the middle or bottom and by 2010 was noticeably higher at the top. We then survey the literature to identify what has been learned about the explanations for the gap. We conclude that many of the traditional explanations continue to have salience. Although human-capital factors are now relatively unimportant in the aggregate, women's work force interruptions and shorter hours remain significant in high-skilled occupations, possibly due to compensating differentials. Gender differences in occupations and industries, as well as differences in gender roles and the gender division of labor remain important, and research based on experimental evidence strongly suggests that discrimination cannot be discounted. Psychological attributes or noncognitive skills comprise one of the newer explanations for gender differences in outcomes. Our effort to assess the quantitative evidence on the importance of these factors suggests that they account for a small to moderate portion of the gender pay gap, considerably smaller than, say, occupation and industry effects, though they appear to modestly contribute to these differences.

Based on data from the Bureau of Labor Statistics in 2018, women working full-time as registered nurses earned 91 percent of what their male counterparts earned. This statistic shows women's median weekly earnings as a percentage of men's for select full-time health care positions in the U.S. for full-time wage earners during 2018.

This data article describes the data and merging procedure used by Nikulin and Wolszczak-Derlacz (Nikulin and Wolszczak-Derlacz, 2022).

Median pay and raw disability pay gap estimates across different characteristic breakdowns, using Annual Population Survey (APS) data.

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 $22,924 for males and $20,885 for females.

Based on these incomes, we observe a gender gap percentage of approximately 9%, indicating a significant disparity between the median incomes of males and females in Washington. Women, regardless of work hours, still earn 91 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Washington, for full-time, year-round workers aged 15 years and older, the Census reported a median income of $40,060 for females, while data for males was unavailable due to an insufficient number of sample observations.

As there was no available median income data for males, conducting a comprehensive assessment of gender-based pay disparity in Washington was not feasible.

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

In 2023, the Rhode Island had the highest earnings ratio for women, as female workers earned ***** percent of their male counterparts on average. The state of Louisiana had the lowest earnings ratio for female workers, who earned ***** percent of what their male counterparts earn.

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 West York. 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 West York, the median income for all workers aged 15 years and older, regardless of work hours, was $33,635 for males and $33,297 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 West York. 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 West York, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,169, while females earned $44,861, leading to a 24% gender pay gap among full-time workers. This illustrates that women earn 76 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that West York offers better opportunities for women in non-full-time positions.

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

I conduct a survey experiment to study the relationship between people's beliefs about the size of the gender wage gap and their demand for policies aimed at mitigating it. Beliefs causally affect support for equal pay legislation and affirmative action programs, but cannot account for the polarization in policy views by partisanship and gender. Changes in policy demand seem to be driven by changes in beliefs about discrimination in labor markets and fairness concerns, while self-interest appears less important. I provide evidence that pessimism about the effectiveness of government intervention limits the elasticity of policy demand to perceived wage differentials.