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.

The global gender gap index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2025, the country offering the most gender equal conditions was Iceland, with a score of 0.93. Overall, the Nordic countries make up 3 of the 5 most gender equal countries worldwide. The Nordic countries are known for their high levels of gender equality, including high female employment rates and evenly divided parental leave. Sudan is the second-least gender equal country Pakistan is found on the other end of the scale, ranked as the least gender equal country in the world. Conditions for civilians in the North African country have worsened significantly after a civil war broke out in April 2023. Especially girls and women are suffering and have become victims of sexual violence. Moreover, nearly 9 million people are estimated to be at acute risk of famine. The Middle East and North Africa have the largest gender gap Looking at the different world regions, the Middle East and North Africa have the largest gender gap as of 2023, just ahead of South Asia. Moreover, it is estimated that it will take another 152 years before the gender gap in the Middle East and North Africa is closed. On the other hand, Europe has the lowest gender gap in the world.

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.

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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...

Women and Men in Spain: Gender salary gap (not adjusted to individual characteristics) by hourly salary by sectors of economic activity and period in the EU. Annual. National.

The Global Gender Gap Index aims to measure the parity between men and women in four key areas: health, education, economics, and politics. At the European Union level, ******* led the ranking in the 2025 edition, with a score of **** points, followed by another Nordic country, ******, at ****.

Gender pay gap data, with year on year change and extended information (such as part-time mean and median, bonus & BIK info, etc. for Carelon Global Solutions. Data is available for 2022-2025 for most companies.

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.

Men in the European Union earned approximately 12 percent more than women in 2023, with Latvia having the biggest gender pay gap of 19 percent and Luxembourg having the lowest at minus 0.9 percent, meaning that on average women actually earned more than men in Luxembourg during that year.

The focus of this study is the implications of structural transformation for gender equality, specifically equal pay, in Sub-Saharan Africa. While structural transformation affects key development outcomes, including growth, poverty, and access to decent work, its effect on the gender pay gap is not clear ex-ante. Evidence on the gender pay gap in sub-Saharan Africa is limited, and often excludes rural areas and informal (self-)employment. This paper provides evidence on the extent and drivers of the gender pay gap in non-farm wage- and self-employment activities across three countries at different stages of structural transformation (Malawi, Tanzania and Nigeria). The analysis leverages nationally-representative survey data and decomposition methods, and is conducted separately among individuals residing in rural versus urban areas in each country. The results show that women earn 40 to 46 percent less than men in urban areas, which is substantially less than in high-income countries. The gender pay gap in rural areas ranges from (a statistically insignificant) 12 percent in Tanzania to 77 percent in Nigeria. In all rural areas, a major share of the gender pay gap (81 percent in Malawi, 83 percent in Tanzania and 70 percent in Nigeria) is explained by differences in workers’ characteristics, including education, occupation and sector. This suggests that if rural men and women had similar characteristics, most of the gender pay gap would disappear. Country-differences are larger across urban areas, where differences in characteristics account for only 32 percent of the pay gap in Tanzania, 50 percent in Malawi and 81 percent in Nigeria. Our detailed decomposition results suggest that structural transformation does not consistently help bridge the gender pay gap. Gender-sensitive policies are required to ensure equal pay for men and women.

Around **** of the people in the world believe that concerns about the gender pay gap are a response to a real problem. This was stated by ** percent of the female respondents and ** percent of the male respondents in a 2021 survey. At the same time, however, ** percent of the male respondents saw these concerns as an example of political correctness going too far, which was around ** percent more than the female respondents. Overall, ** percent believe that closing the gender pay gap is important and should be one of the world's top priorities right now.

Gender pay gap data, with year on year change and extended information (such as part-time mean and median, bonus & BIK info, etc. for Global Payments. Data is available for 2022-2025 for most companies.

Gender pay gap data, with year on year change and extended information (such as part-time mean and median, bonus & BIK info, etc. for International Financial Data Services. Data is available for 2022-2025 for most companies.

Gender pay gap data, with year on year change and extended information (such as part-time mean and median, bonus & BIK info, etc. for Mediolanum International Funds. Data is available for 2022-2025 for most companies.

Gender pay gap data, with year on year change and extended information (such as part-time mean and median, bonus & BIK info, etc. for Mondelez International. Data is available for 2022-2025 for most companies.

The unadjusted Gender Pay Gap (GPG) represents the difference between average gross hourly earnings of male paid employees and of female paid employees as a percentage of average gross hourly earnings of male paid employees. The population consists of all paid employees in enterprises with 10 employees or more in NACE Rev. 2 aggregate B to S (excluding O). The GPG indicator is calculated within the framework of the data collected according to the methodology of the Structure of Earnings Survey.

Gender pay gap data, with year on year change and extended information (such as part-time mean and median, bonus & BIK info, etc. for Apple Distribution International. Data is available for 2022-2025 for most companies.

Gender Pay Gap legislation introduced in April 2017 requires all employers of 250 or more employees to publish their gender pay gap annually. The gender pay gap is the difference between the average earnings of men and women, expressed relative to men’s earnings.

You can also:

• https://gender-pay-gap.service.gov.uk/Viewing/search-results">explore this data on a dashboard
• https://data.gov.uk/dataset/gender-pay-gap">export all national gender pay gap data

Gender Pay Gap legislation introduced in April 2017 requires all employers of 250 or more employees to publish their gender pay gap annually. The gender pay gap is the difference between the average earnings of men and women, expressed relative to men’s earnings.

You can also:

• https://gender-pay-gap.service.gov.uk/Viewing/search-results?t=1&search=">explore this data on a dashboard
• https://data.gov.uk/dataset/54219db1-dd98-49d9-a383-a5978bb0aeb9/gender-pay-gap">export all national gender pay gap data