Civil Service Statistics data browser (2024)

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

Status Year Region_london Profession_of_post Region_ITL1 Sex Headcount FTE Mean_salary Median_salary
In post 2024 London Commercial London Female 755 735 57320 55360
In post 2024 London Commercial London Male 730 720 64700 59150
In post 2024 London Communications London Female 1270 1230 51080 47060
In post 2024 London Communications London Male 835 835 51920 47630
In post 2024 London Corporate Finance London Female 20 20 [c] [c]
In post 2024 London Corporate Finance London Male 30 30 73710 67600
In post 2024 London Counter Fraud London Female 1005 925 37530 34270
In post 2024 London Counter Fraud London Male 985 955 42120 40800
In post 2024 London Digital, Data and Technology London Female 2125 2060 53450 48900
In post 2024 London Digital, Data and Technology London Male 3605 3575 54200 49730
In post 2024 London Economics London Female 625 600 53690 56100
In post 2024 London Economics London Male 1020 1010 54500 56790
In post 2024 London Finance London Female 1330 1280 51520 46980
In post 2024 London Finance London Male 1340 1325 54500 49760
In post 2024 London Geography London Female [c] [c] [c] [c]
In post 2024 London Geography London Male [c] [c] [c] [c]
In post 2024 London Human Resources London Female 2185 2080 51500 46250
In post 2024 London Human Resources London Male 1065 1050 53870 49760
In post 2024 London Inspector of Education and Training London Female 75 75 68390 68920
In post 2024 London Inspector of Education and Training London Male 30 30 [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 2024-07-24

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).