Ethnicity pay gap estimates for 2018 across different ethnicity breakdowns using the Annual Population Survey.

Median pay and raw ethnicity pay gap estimates for 2012 to 2022 across different ethnicity breakdowns using the Annual Population Survey, UK.

CYC's other annual pay gap reports, including all CYC staff but excluding all schools staff and councillors. For further pay gap reports please visit CYC Gender Pay Gap Reports

Since 2021 these data tables have been produced to supplement the Pay Gap reports for the Greater London Authority (GLA). They provide extra details of the median and mean hourly rates of particular groups of staff at the GLA, as well as their corresponding pay gaps, as of 31 March 2024, 2023, 2022 and 2021. The GLA first published its ethnicity pay gap in March 2018, one of the first organisations to do so. These data tables bring together all of the ethnicity pay gap data published by the GLA since then. This reporting year (2024) is the fourth time that the GLA is publishing its disability pay gap, ahead of any statutory responsibility to do so. Therefore, there is only data from 2021 to compare against. The Equality Act 2010 (Gender Pay Gap Information) regulations came into force in 2017. This required public bodies with 250 or more employees to report on their gender pay gap. These data tables bring together all of the gender pay gap data published by the GLA since then. Pay gaps are included in the GLA's measures of Economic Fairness. Click here to find out more.

In 2019 white people in England and Wales had an average hourly income that was *** percent larger than that of ethnic minority groups. This was the lowest percentage difference recorded during this time period, with the highest difference recorded in 2014, when the average difference in hourly earnings was *** percent.

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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...

Data on this page shows the gender and ethnicity pay gap reporting and (from 2021) disability pay gap reporting for staff employed by the London Fire Brigade. The London Fire Commissioner (LFC) is the fire and rescue authority for London, and runs the London Fire Brigade (LFB). As a functional body reporting to the Mayor of London, LFB has a commitment to undertake work to narrow any gaps. LFB agree with the Mayor of London that this is necessary in order to address any inequalities and reduce gaps where these are evident. The Equality Act 2010 (Specific Duties and Public Authorities) Regulations 2017 requires that the London Fire Brigade publish the difference between the rate of pay of male and female full-pay employees to show any gender pay gap. Annual data is available from March 2016. And, although it is not a statutory requirement, the LFB also publish ethnicity and disability (from 2021) pay gap data in line with good practice. Annual data for ethnicity is available annually from March 2017. With effect from 1 April 2018, the individual holding the office of London Fire Commissioner (LFC) is appointed by the Mayor of London and is not an LFB employee. However, in the interests of transparency, the London Fire Commissioner has been taken into account for the purposes of reporting this pay gap data.

During a 2023 survey carried out among more than ***** marketers from the United Kingdom, it was found that marketers who identified as white had the highest average full-time earnings, with ***** thousand British pounds annually. Black/African Caribbean/Black British was the lowest-paid group, with ***** thousand pounds annually.

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.

Analysis of employee characteristics by ethnicity for 2022 using the Annual Population Survey, UK

Ethnicity pay gap regression results for 2018 across different ethnicity breakdowns using the Annual Population Survey.

A Pay Gap Analysis of Camden's workforce by gender,disability and ethnicity. The data is a breakdown of the Pay Gap as of 31 March 2016.

The median hourly pay and percentage difference in 2019 between hourly earnings with white British employees was greater between men than between women. White Irish male employees earned a median of ***** pounds per hour, while Bangladeshi male employees earned a median of ***** pounds per hour. In comparison, white Irish female employees earned a median of ***** pounds per hour, while Bangladeshi female employees earned a median of ***** pounds per hour. The highest median hourly pay was seen for Chinese male employees, at ***** pounds per hour. Chinese female employees also earned a high hourly pay, at ***** pounds per hour. Female white and black Caribbean female employees experienced the lowest median hourly pay in 2019, at just **** pounds per hour.

39.8% of workers from the Indian ethnic group were in 'professional' jobs in 2021 – the highest percentage out of all ethnic groups in this role.

The median hourly pay in 2022 for White British employees was ***** pounds per hour, while people belonging to the Asian Other category received a median of ***** pounds per hour, which was the highest hourly pay in the hourly earnings shown. White and Black Caribbean employees had the lowest median hourly earnings in 2022, at ***** pounds per hour, followed by Bangladeshi employees, at just over ** pounds per hour

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

Civil servants from Asian and black ethnic backgrounds had the lowest median annual pay, and those from the ‘other’ ethnic group had the highest.