Civil Service Statistics data browser (2023)

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

Explore further: Organisation, Responsibility_level_grouped, Responsibility_level_ungrouped, Region_ITL1, Region_ITL2, Region_ITL3, Profession_of_post, Sex, Ethnicity, Disability, Sexual_orientation, Age

Status Year Parent_department Function_of_post Region_london Headcount FTE Mean_salary Median_salary
In post 2023 Attorney General’s Departments Analysis London 10 10 [c] [c]
In post 2023 Attorney General’s Departments Analysis Outside London [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Commercial London 15 15 36980 35190
In post 2023 Attorney General’s Departments Commercial Outside London 30 30 42580 38940
In post 2023 Attorney General’s Departments Communications London 65 60 44020 40540
In post 2023 Attorney General’s Departments Communications Outside London 20 20 39690 41320
In post 2023 Attorney General’s Departments Counter Fraud London 235 230 47760 41400
In post 2023 Attorney General’s Departments Digital, Data & Technology London 135 135 47600 41580
In post 2023 Attorney General’s Departments Digital, Data & Technology Outside London 160 155 39460 38940
In post 2023 Attorney General’s Departments Finance London 85 80 44300 38860
In post 2023 Attorney General’s Departments Finance Outside London 85 80 33450 31180
In post 2023 Attorney General’s Departments Human Resources London 140 135 42770 36050
In post 2023 Attorney General’s Departments Human Resources Outside London 135 130 36950 32500
In post 2023 Attorney General’s Departments Legal London 4015 3770 52090 53390
In post 2023 Attorney General’s Departments Legal Outside London 5430 5045 41620 38760
In post 2023 Attorney General’s Departments Legal Overseas [c] [c] [c] [c]
In post 2023 Attorney General’s Departments No function London 50 50 45210 38070
In post 2023 Attorney General’s Departments Project Delivery London 25 20 51290 52050
In post 2023 Attorney General’s Departments Project Delivery Outside London [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Property London 10 10 41760 31640
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 2023-07-26, 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.
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).