Civil Service Statistics data browser (2023)

Data preview: All civil servants / Parent_department / Region_london / Function_of_post / Sex

Status Year Parent_department Region_london Function_of_post Sex Headcount FTE Mean_salary Median_salary
In post 2023 Attorney General’s Departments London Analysis Female [c] [c] [c] [c]
In post 2023 Attorney General’s Departments London Analysis Male [c] [c] [c] [c]
In post 2023 Attorney General’s Departments London Commercial Female 5 5 [c] [c]
In post 2023 Attorney General’s Departments London Commercial Male 10 10 [c] [c]
In post 2023 Attorney General’s Departments London Communications Female 35 35 45770 40540
In post 2023 Attorney General’s Departments London Communications Male 25 25 [c] [c]
In post 2023 Attorney General’s Departments London Counter Fraud Female 95 95 43790 40000
In post 2023 Attorney General’s Departments London Counter Fraud Male 140 135 50550 43480
In post 2023 Attorney General’s Departments London Digital, Data & Technology Female 50 50 42640 36530
In post 2023 Attorney General’s Departments London Digital, Data & Technology Male 85 85 50670 44310
In post 2023 Attorney General’s Departments London Finance Female 45 40 42890 38070
In post 2023 Attorney General’s Departments London Finance Male 45 40 45700 38860
In post 2023 Attorney General’s Departments London Human Resources Female 105 100 41670 35680
In post 2023 Attorney General’s Departments London Human Resources Male 35 35 45910 37550
In post 2023 Attorney General’s Departments London Legal Female 2690 2475 51540 53390
In post 2023 Attorney General’s Departments London Legal Male 1325 1295 53200 53990
In post 2023 Attorney General’s Departments London No function Female 30 30 47260 41400
In post 2023 Attorney General’s Departments London No function Male 20 20 [c] [c]
In post 2023 Attorney General’s Departments London Project Delivery Female 15 15 [c] [c]
In post 2023 Attorney General’s Departments London Project Delivery Male 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 2023-07-28, with GIT d545f65.

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.
Four 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, Scottish Forestry and Forest and Land Scotland. A further three organisations also could not provide entrants data in 2021. These are as follows: Department for International Development, Foreign and Commonwealth Office (excl. agencies) and Royal Fleet Auxiliary.
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".
Function_of_post Functions relate to the post occupied by the person and are not dependent on qualifications the individual may have.
Welsh Government and Royal Fleet Auxiliary did not report any functions information for their employees.
Of the 20 bodies under the Scottish Government, 16 did not report any functions information for their employees.
Sex Self reported sex.
"Unknown" accounts for employees who were recorded with an unknown sex.
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).