Civil Service Statistics data browser (2025)

Data preview: All civil servants / Region_london / Profession_of_post / Disability / Region_ITL1

Status Year Region_london Profession_of_post Disability Region_ITL1 Headcount FTE Mean_salary Median_salary
In post 2025 London Actuary Declared disabled London 10 10 [c] [c]
In post 2025 London Actuary Declared non-disabled London 95 90 94530 90390
In post 2025 London Actuary Undeclared London [c] [c] [c] [c]
In post 2025 London Actuary Unknown London 10 10 [c] [c]
In post 2025 London Clinical Declared disabled London 25 25 [c] [c]
In post 2025 London Clinical Declared non-disabled London 190 175 78020 73510
In post 2025 London Clinical Undeclared London 25 25 [c] [c]
In post 2025 London Clinical Unknown London 105 95 67890 59970
In post 2025 London Commercial Declared disabled London 175 170 59870 61010
In post 2025 London Commercial Declared non-disabled London 870 845 68860 63000
In post 2025 London Commercial Undeclared London 100 95 67290 66180
In post 2025 London Commercial Unknown London 375 370 55640 49240
In post 2025 London Communications Declared disabled London 180 175 51300 48410
In post 2025 London Communications Declared non-disabled London 1205 1170 56090 51950
In post 2025 London Communications Undeclared London 95 95 55890 52360
In post 2025 London Communications Unknown London 760 750 53280 48330
In post 2025 London Corporate Finance Declared disabled London [c] [c] [c] [c]
In post 2025 London Corporate Finance Declared non-disabled London 30 30 60400 59990
In post 2025 London Corporate Finance Undeclared London 5 5 [c] [c]
In post 2025 London Corporate Finance Unknown London 50 45 61090 50110
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).
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.
Profession_of_post Professions relate to the post occupied by the person and are not dependent on qualifications the individual may have.
Two bodies did not report any professions information for their employees. These are as follows: Scottish Forestry and Forestry and Land Scotland.
Disability Self reported disability.
"Undeclared" accounts for employees who have actively declared that they do not want to disclose their disability status and "Unknown" accounts for employees who have not made an active declaration about their disability status.
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).