Civil Service Statistics data browser (2025)

Data preview: All civil servants / Region_ITL1 / Region_london / Sex / Ethnicity

Status Year Region_ITL1 Region_london Sex Ethnicity Headcount FTE Mean_salary Median_salary
In post 2025 East (England) Outside London Female Asian 875 780 34810 30980
In post 2025 East (England) Outside London Female Black 580 545 33990 32490
In post 2025 East (England) Outside London Female Mixed 325 300 36210 34330
In post 2025 East (England) Outside London Female Other ethnicity 75 65 36180 34330
In post 2025 East (England) Outside London Female Undeclared 390 345 35600 33180
In post 2025 East (England) Outside London Female Unknown 1150 1040 34580 30980
In post 2025 East (England) Outside London Female White 9435 8530 36320 32910
In post 2025 East (England) Outside London Male Asian 630 600 37490 34160
In post 2025 East (England) Outside London Male Black 505 495 36460 34330
In post 2025 East (England) Outside London Male Mixed 195 190 38270 34330
In post 2025 East (England) Outside London Male Other ethnicity 60 55 36430 34250
In post 2025 East (England) Outside London Male Undeclared 620 590 37580 34790
In post 2025 East (England) Outside London Male Unknown 1140 1100 35080 31500
In post 2025 East (England) Outside London Male White 7230 6935 38680 35680
In post 2025 East (England) Outside London Unknown Asian [c] [c] [c] [c]
In post 2025 East (England) Outside London Unknown Black [c] [c] [c] [c]
In post 2025 East (England) Outside London Unknown Mixed [c] [c] [c] [c]
In post 2025 East (England) Outside London Unknown Other ethnicity [c] [c] [c] [c]
In post 2025 East (England) Outside London Unknown White [c] [c] [c] [c]
In post 2025 East Midlands (England) Outside London Female Asian 1430 1260 32630 30980
Note: Data has been truncated to 20 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 5 civil servants for FTE or Headcount, and less than 10 civil servants for median and mean salary (shown as [c]). Zero responses and salaries for less than 30 civil servants have been suppressed for GPDR special category data. FTE figures are not shown for entrants or leavers due to data quality concerns for these groups. Figures are rounded to the nearest 5, or £10 as appropriate.

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

Version: Generated on 2025-07-18

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.
Five organisations do not report when their employees first entered the Civil Service and so entrants data for these organisations is not available . These are as follows: Foreign Commonwealth and Development Office (excl. agencies), Foreign Commonwealth and Development Office Services, United Kingdom Statistics Authority, Scottish Forestry and Forest and Land Scotland.
Year Year of data collection (as at 31 March).
Region_london Workplace postcode data are used to derive geographical information using the International Territorial Level (ITL) classification standard.
Region_london groups the ITL classifications into "London", "Outside London": all UK regions excluding London, "Overseas", and "Unknown".
Region_ITL1 Workplace postcode data are used to derive geographical information using the International Territorial Level (ITL) classification standard.
Following the UK’s withdrawal from the EU, a new UK-managed international statistical geography - International Territorial Levels (ITL) - was introduced from 1st January 2021, replacing the former NUTS classification. They align with international standards, enabling comparability both over time and internationally. To ensure continued alignment, the ITLs mirror the NUTS system. They also follow a similar review timetable - every three years.
ITL 1 divides into Wales, Scotland, Northern Ireland, and the 9 statistical regions of England.
Sex Self reported sex.
"Unknown" accounts for employees who were recorded with an unknown sex.
Ethnicity Self reported ethnicity. "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 5).
FTE Total full-time equivalent (FTE) employment numbers (rounded to nearest 5).
FTE figures are not shown for entrants or leavers due to data quality concerns for these groups.
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.
These figures should be interpreted with caution when the total number of employees in a group is small, as they will tend to show more variability than larger groups (i.e. may be much higher or lower than can be explained by the data shown).
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.
These figures should be interpreted with caution when the total number of employees in a group is small, as they will tend to show more variability than larger groups (i.e. may be much higher or lower than can be explained by the data shown).