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Data preview: All civil servants / Sex / Profession_of_post / Ethnicity

Status Year Sex Profession_of_post Ethnicity Headcount FTE Mean_salary Median_salary
In post 2021 Female Commercial Asian 120 110 38000 35190
In post 2021 Female Commercial Black 120 120 36990 34900
In post 2021 Female Commercial Chinese [c] [c] [c] [c]
In post 2021 Female Commercial Mixed 60 50 39730 34780
In post 2021 Female Commercial Other ethnicity [c] [c] [c] [c]
In post 2021 Female Commercial Undeclared 100 100 38310 35760
In post 2021 Female Commercial Unknown 310 290 38650 36820
In post 2021 Female Commercial White 2130 2010 38910 36500
In post 2021 Female Communications Asian 110 100 40290 37210
In post 2021 Female Communications Black 80 80 37710 35010
In post 2021 Female Communications Chinese [c] [c] [c] [c]
In post 2021 Female Communications Mixed 70 70 42240 39290
In post 2021 Female Communications Other ethnicity [c] [c] [c] [c]
In post 2021 Female Communications Undeclared 50 40 [c] [c]
In post 2021 Female Communications Unknown 400 390 42090 38650
In post 2021 Female Communications White 1600 1510 41530 38540
In post 2021 Female Corporate Finance Asian [c] [c] [c] [c]
In post 2021 Female Corporate Finance Black [c] [c] [c] [c]
In post 2021 Female Corporate Finance Chinese [c] [c] [c] [c]
In post 2021 Female Corporate Finance Mixed [c] [c] [c] [c]
In post 2021 Female Corporate Finance Other ethnicity [c] [c] [c] [c]
In post 2021 Female Corporate Finance Undeclared [c] [c] [c] [c]
In post 2021 Female Corporate Finance Unknown [c] [c] [c] [c]
In post 2021 Female Corporate Finance White 70 60 42570 37870
In post 2021 Female Counter Fraud Asian [c] [c] [c] [c]
In post 2021 Female Counter Fraud Black [c] [c] [c] [c]
In post 2021 Female Counter Fraud Chinese [c] [c] [c] [c]
In post 2021 Female Counter Fraud Mixed [c] [c] [c] [c]
In post 2021 Female Counter Fraud Other ethnicity [c] [c] [c] [c]
In post 2021 Female Counter Fraud Undeclared [c] [c] [c] [c]
In post 2021 Female Counter Fraud Unknown 70 70 38750 34790
In post 2021 Female Counter Fraud White 270 250 35180 33100
In post 2021 Female Digital, Data and Technology Asian 330 320 42430 38180
In post 2021 Female Digital, Data and Technology Black 160 150 40320 38630
In post 2021 Female Digital, Data and Technology Chinese [c] [c] [c] [c]
In post 2021 Female Digital, Data and Technology Mixed 90 80 41240 39980
In post 2021 Female Digital, Data and Technology Other ethnicity [c] [c] [c] [c]
In post 2021 Female Digital, Data and Technology Undeclared 160 150 41920 38630
In post 2021 Female Digital, Data and Technology Unknown 660 640 40400 36500
In post 2021 Female Digital, Data and Technology White 3190 3000 40430 37450
In post 2021 Female Economics Asian 70 70 46210 52360
In post 2021 Female Economics Black [c] [c] [c] [c]
In post 2021 Female Economics Chinese [c] [c] [c] [c]
In post 2021 Female Economics Mixed 40 40 [c] [c]
In post 2021 Female Economics Other ethnicity [c] [c] [c] [c]
In post 2021 Female Economics Undeclared [c] [c] [c] [c]
In post 2021 Female Economics Unknown 130 130 42980 36820
In post 2021 Female Economics White 490 470 48410 49600
In post 2021 Female Finance Asian 370 350 38240 35200
In post 2021 Female Finance Black 230 220 38420 35200
In post 2021 Female Finance Chinese 30 30 [c] [c]
In post 2021 Female Finance Mixed 80 80 40710 38140
In post 2021 Female Finance Other ethnicity 40 30 [c] [c]
In post 2021 Female Finance Undeclared 160 150 38770 35140
In post 2021 Female Finance Unknown 510 480 36360 32790
In post 2021 Female Finance White 3710 3410 38090 33300
In post 2021 Female Human Resources Asian 300 290 37340 34640
In post 2021 Female Human Resources Black 240 230 37720 34500
In post 2021 Female Human Resources Chinese [c] [c] [c] [c]
In post 2021 Female Human Resources Mixed 110 110 40660 37640
In post 2021 Female Human Resources Other ethnicity 30 30 [c] [c]
In post 2021 Female Human Resources Undeclared 140 130 36310 33300
In post 2021 Female Human Resources Unknown 460 430 35610 31810
In post 2021 Female Human Resources White 4260 3960 38590 33350
In post 2021 Female Inspector of Education and Training Asian [c] [c] [c] [c]
In post 2021 Female Inspector of Education and Training Black [c] [c] [c] [c]
In post 2021 Female Inspector of Education and Training Chinese [c] [c] [c] [c]
In post 2021 Female Inspector of Education and Training Mixed [c] [c] [c] [c]
In post 2021 Female Inspector of Education and Training Other ethnicity [c] [c] [c] [c]
In post 2021 Female Inspector of Education and Training Undeclared [c] [c] [c] [c]
In post 2021 Female Inspector of Education and Training Unknown [c] [c] [c] [c]
In post 2021 Female Inspector of Education and Training White 540 530 63680 64500
In post 2021 Female Intelligence Analysis Asian 110 100 32960 29480
In post 2021 Female Intelligence Analysis Black 40 40 [c] [c]
In post 2021 Female Intelligence Analysis Chinese [c] [c] [c] [c]
In post 2021 Female Intelligence Analysis Mixed 40 40 [c] [c]
In post 2021 Female Intelligence Analysis Other ethnicity [c] [c] [c] [c]
In post 2021 Female Intelligence Analysis Undeclared 60 60 33160 31360
In post 2021 Female Intelligence Analysis Unknown 120 110 44540 44830
In post 2021 Female Intelligence Analysis White 1170 1090 32610 29250
In post 2021 Female Internal Audit Asian 40 40 [c] [c]
In post 2021 Female Internal Audit Black [c] [c] [c] [c]
In post 2021 Female Internal Audit Chinese [c] [c] [c] [c]
In post 2021 Female Internal Audit Mixed [c] [c] [c] [c]
In post 2021 Female Internal Audit Other ethnicity [c] [c] [c] [c]
In post 2021 Female Internal Audit Undeclared [c] [c] [c] [c]
In post 2021 Female Internal Audit Unknown [c] [c] [c] [c]
In post 2021 Female Internal Audit White 290 270 43810 41300
In post 2021 Female International Trade Asian 60 60 43650 38540
In post 2021 Female International Trade Black 30 30 [c] [c]
In post 2021 Female International Trade Chinese [c] [c] [c] [c]
In post 2021 Female International Trade Mixed [c] [c] [c] [c]
In post 2021 Female International Trade Other ethnicity [c] [c] [c] [c]
In post 2021 Female International Trade Undeclared [c] [c] [c] [c]
In post 2021 Female International Trade Unknown 190 190 44120 39370
In post 2021 Female International Trade White 310 300 47880 48220
In post 2021 Female Knowledge and Information Management Asian 70 70 34010 32330
In post 2021 Female Knowledge and Information Management Black 40 40 [c] [c]
In post 2021 Female Knowledge and Information Management Chinese [c] [c] [c] [c]
In post 2021 Female Knowledge and Information Management Mixed [c] [c] [c] [c]
Note: Data has been truncated to 100 rows, please download the data to view the remaining rows

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About: The Civil Service Statistics data browser is a pilot project by Cabinet Office to provide access to more detailed data on the Civil Service workforce from the Annual Civil Service Employment Survey. We welcome feedback or comments on this project, which can be addressed to civilservicestatistics@cabinetoffice.gov.uk

Notes: Summary figures are suppressed when information relates to less than 30 civil servants for FTE or Headcount, and less than 50 civil servants for median and mean salary (shown as [c]). Additionally, zero responses have been suppressed for GPDR protected characteristics. Figures are rounded to the nearest 10, or £10 as appropriate.

Data source: All figures are aggregated from the Cabinet Office Annual Civil Service Employment Survey collection.

Version: Generated on 2022-07-26, with GIT 03ee02c.

Data column Description
Status Employment status of the civil servants.
In post - includes staff that were in post on the reference date (31 March).
New entrant CS - includes new entrants to the Civil Service over the year (1 April to 31 March).
Leaver CS - includes leavers from the Civil Service over the year (1 April to 31 March). This includes employees who have an Unknown leaving cause.
Leaver Dept. - includes leavers from the department over the year (1 April to 31 March), who did not leave the Civil Service.
Organisation specific notes on status: In late June 2021 around 7,000 employees from Community Rehabilitation Companies were transferred in from the private sector to HM Prison and Probation Service, counting as entrants. The Defence Electronics and Components Agency does not record the date in which their employees first enter the Civil Service and so entrants data is not available. HM Land Registry do not record where their departing employees transfer to and so are unable to identify those that transfer to another Civil Service department.
Year Year of data collection (as at March 31st).
Profession_of_post Professions relate to the post occupied by the person and are not dependent on qualifications the individual may have.
Cabinet Office (excl agencies) reported profession information for only 60 out of a total 9,930 employees.
Of the 20 bodies under the Scottish Government, 16 did not report any professions information for their employees.
Sex Self reported sex.
“Unknown” accounts for employees who were recorded with an unknown sex.
Ethnicity Self reported ethnicity. For consistency with Civil Service Statistics, ‘Chinese’ is shown separately in the 2021 figures, but counted within ‘Asian’ for 2022.
“Undeclared” accounts for employees who have actively declared that they do not want to disclose their ethnicity and “Unknown” accounts for employees who have not made an active declaration about their ethnicity.
Headcount Total number of civil servants (rounded to nearest 10).
FTE Total full-time equivalent employment numbers (rounded to nearest 10).
Mean_salary Average salary (mean, rounded to nearest £10). For part-time employees, salaries represent the full-time equivalent earnings, while for full-time employees they are the actual annual gross salaries.
Median_salary Median salary (rounded to nearest £10). For part-time employees, salaries represent the full-time equivalent earnings, while for full-time employees they are the actual annual gross salaries.