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.

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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...

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.

As of February 2025, approximately 45 percent of women in Great Britain thought that more should be done to address equal pay in relation to gender inequality, compared with 25 percent who thought that it had already been achieved.

In 2025, the difference between average hourly earnings for men and women in the United Kingdom for all workers was 12.8 percent, compared with 6.9 percent for full-time workers, and -2.9 percent for part-time workers. During the provided time period, the gender pay gap was at its highest in 1997, when it was 27.5 percent for all workers. Compared with 1997, the gender pay gap has fallen by 13.2 percent for all workers, and 9.7 percent for full-time workers. Gender pay gap higher in older age groups Although the gender pay gap among younger age groups was relatively small in 2024, the double-digit pay gap evident in older age groups served to keep the overall gap high. The gender pay gap for workers aged between 18 and 21 for example was -0.5 percent, compared with 12.1percent for people in their 50s. Additionally, the gender pay gap for people aged over 60 has changed little since 1997, falling by just 1.2 percent between 1997 and 2023, compared with a 14.9 percent reduction among workers in their 40s. Positions of power As of 2024, women are unfortunately still relatively underrepresented in leadership positions at Britain’s top businesses. Among FTSE 100 companies, for example, just 9.4 percent of CEOs were female, falling to just 6.1 percent for FTSE 250 companies. Representation was better when it came to FTSE 100 boardrooms, with 44.7 percent of positions at this level being filled by women, compared with 42.6 percent at FTSE 250 companies. In the corridors of political power, the proportion of female MPs was estimated to have reached its highest ever level after the 2024 election at 41 percent, compared with just three percent in 1979.

A paper outlining how the gender pay gap will be presented in future ONS Statistical Bulletins

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Presentation of the Gender Pay Gap: ONS Position Paper

Results of statistical modelling exploring linear regression and Blinder-Oaxaca decomposition.

Most young Italians are aware that men and women are not paid the same for equal work. In a survey published in 2021, around 87 percent thought that men and women did not receive the same wage for work of equal value, while almost 13 percent believed the opposite.

Annual Survey for Hours and Earnings data to support the 'Understanding the gender pay gap' article.

Last year, government introduced world-leading legislation, which made it statutory for organisations (with 250 or more employees) to report annually on their gender pay gap.

This report sets out where the IPO fulfils the reporting requirements. It analyses the figures in more detail and explains what we are doing to close the gender pay gap in the organisation.

You can also:

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

In a survey carried out in Mexico in 2021, when asked if respondents knew about any case where a woman was being paid less than a man performing the same job and with the same skills, ** percent answered affirmatively. Meanwhile, ** percent of participants stated they did not know any case. Further, Mexican women are ** percent less likely than men to receive an equal wage for similar work according to the gender gap index.

Gender Pay Gap legislation introduced in April 2017 requires all employers of 250 or more employees to report annually on their gender pay gap.

The gender pay gap is the difference between the average earnings of men and women.

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

This statistic presents the public opinion on the existence of gender pay gap in France in 2019. In total, ** percent of respondents actually felt that there was a wage gap between men and women in France. Gender pay gap is a reality in Europe. In 2018, men in France out-earned women by **** percent. It was only *** percent under the average gap in Europe which amounted to **** percent.

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.

About Dataset Context Want to know the base pay for different job roles, then this data set will be useful.

About the data set: The data set has been taken from glassdoor and focuses on income for various job titles based on gender. As there have been many studies showcasing that women are paid less than men for the same job titles, this data set will be helpful in identifying the depth of the gender-based pay gap. The features of the data set are: Job Title Gender Age PerfEval Education Dept Seniority Base Pay, and Bonus

Acknowledgements The data set has been taken from the website of Glassdoor. The license was not mentioned on the source.

Inspiration To find out the pay gap between the gender for the same job title.

Context

"The gender wage gap is defined as the difference between median earnings of men and women relative to median earnings of men. Data refer to full-time employees on the one hand and to self-employed on the other."

OECD (2022), Gender wage gap (indicator). doi: 10.1787/7cee77aa-en (Accessed on 10 March 2022)

Content

Table with Gender wage gap data.

Acknowledgements

OECD (2022), Gender wage gap (indicator). doi: 10.1787/7cee77aa-en (Accessed on 10 March 2022)

https://data.oecd.org/earnwage/gender-wage-gap.htm

Inspiration

The disparity of male/female on Kaggle that will be reproduced on kagglers professional lives.

The statistic presents information on the public opinion on the severity of the problem in the movie industry that female movie stars are generally paid less than their male counterparts in the United States as of January 2017. According to the survey, 28 percent of respondents stated that this gender pay gap was a very serious problem.