Civil Service Statistics data browser (2023)

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

Status Year Sex Region_london Region_ITL1 Profession_of_post Headcount FTE Mean_salary Median_salary
In post 2023 Female London London Commercial 840 820 54430 49450
In post 2023 Female London London Communications 1200 1160 48800 45170
In post 2023 Female London London Corporate Finance 25 25 [c] [c]
In post 2023 Female London London Counter Fraud 1010 925 35160 32520
In post 2023 Female London London Digital, Data and Technology 1980 1925 50810 46790
In post 2023 Female London London Economics 595 570 51130 54090
In post 2023 Female London London Finance 1285 1230 49650 44770
In post 2023 Female London London Human Resources 2030 1950 48310 43650
In post 2023 Female London London Inspector of Education and Training 80 80 65790 66170
In post 2023 Female London London Intelligence Analysis 500 470 38460 35960
In post 2023 Female London London Internal Audit 85 80 52220 50220
In post 2023 Female London London International Trade 675 665 49670 46080
In post 2023 Female London London Knowledge and Information Management 440 415 40630 36910
In post 2023 Female London London Legal 2885 2665 57940 56950
In post 2023 Female London London Medicine 245 225 69490 55240
In post 2023 Female London London Operational Delivery 20245 18720 34830 32520
In post 2023 Female London London Operational Research 185 175 50120 46600
In post 2023 Female London London Other 1365 1300 42840 36180
In post 2023 Female London London Planning 50 45 48980 45750
In post 2023 Female London London Planning Inspectors 5 5 [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).
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.
Of the 20 bodies under the Scottish Government, 16 did not report any professions 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).