Civil Service Statistics data browser (2025)

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

Status Year Region_london Parent_department Ethnicity Region_ITL1 Headcount FTE Mean_salary Median_salary
In post 2025 London Attorney General’s Departments Asian London 735 695 51800 59660
In post 2025 London Attorney General’s Departments Black London 485 460 47880 38630
In post 2025 London Attorney General’s Departments Mixed London 180 170 56420 61100
In post 2025 London Attorney General’s Departments Other ethnicity London 70 65 52040 59070
In post 2025 London Attorney General’s Departments Undeclared London 105 100 58440 61200
In post 2025 London Attorney General’s Departments Unknown London 1070 1025 52720 50750
In post 2025 London Attorney General’s Departments White London 2375 2225 62240 65250
In post 2025 London Cabinet Office Asian London 640 630 50340 42630
In post 2025 London Cabinet Office Black London 280 275 49350 42820
In post 2025 London Cabinet Office Mixed London 235 230 52540 46170
In post 2025 London Cabinet Office Other ethnicity London 70 70 46140 40160
In post 2025 London Cabinet Office Undeclared London 170 170 59850 61740
In post 2025 London Cabinet Office Unknown London 935 925 56840 61010
In post 2025 London Cabinet Office White London 3465 3405 59140 61740
In post 2025 London Chancellor’s other departments Asian London 60 55 56090 52110
In post 2025 London Chancellor’s other departments Black London 15 15 [c] [c]
In post 2025 London Chancellor’s other departments Mixed London 20 20 [c] [c]
In post 2025 London Chancellor’s other departments Other ethnicity London 5 5 [c] [c]
In post 2025 London Chancellor’s other departments Undeclared London 25 25 [c] [c]
In post 2025 London Chancellor’s other departments Unknown London 10 10 [c] [c]
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 2025-07-17

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).
Parent_department Government Department, total figures for both Ministerial and Non-Ministerial Departments include all of their Executive Agencies.
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.
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).