Civil Service Statistics data browser (2023)

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

Status Year Parent_department Region_london Sex Disability Headcount FTE Mean_salary Median_salary
In post 2023 Attorney General’s Departments London Female Declared disabled 310 285 49660 52020
In post 2023 Attorney General’s Departments London Female Declared non-disabled 2160 1995 50730 52790
In post 2023 Attorney General’s Departments London Female Undeclared 100 95 53130 54270
In post 2023 Attorney General’s Departments London Female Unknown 540 505 50020 52790
In post 2023 Attorney General’s Departments London Male Declared disabled 165 160 50400 52400
In post 2023 Attorney General’s Departments London Male Declared non-disabled 1220 1200 52130 52020
In post 2023 Attorney General’s Departments London Male Undeclared 50 50 58970 67650
In post 2023 Attorney General’s Departments London Male Unknown 275 270 51270 54100
In post 2023 Attorney General’s Departments London Unknown Declared disabled [c] [c] [c] [c]
In post 2023 Attorney General’s Departments London Unknown Declared non-disabled [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Outside London Female Declared disabled 510 460 39590 32660
In post 2023 Attorney General’s Departments Outside London Female Declared non-disabled 2620 2370 40040 33300
In post 2023 Attorney General’s Departments Outside London Female Undeclared 30 30 34310 26900
In post 2023 Attorney General’s Departments Outside London Female Unknown 835 785 38890 33810
In post 2023 Attorney General’s Departments Outside London Male Declared disabled 225 215 43050 40120
In post 2023 Attorney General’s Departments Outside London Male Declared non-disabled 1310 1275 45630 49050
In post 2023 Attorney General’s Departments Outside London Male Undeclared 15 15 [c] [c]
In post 2023 Attorney General’s Departments Outside London Male Unknown 355 355 43550 49050
In post 2023 Attorney General’s Departments Outside London Unknown Declared disabled [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Outside London Unknown Declared non-disabled [c] [c] [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 2023-07-29, 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".
Sex Self reported sex.
"Unknown" accounts for employees who were recorded with an unknown sex.
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).