Civil Service Statistics data browser (2023)

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

Status Year Age Region_london Region_ITL1 Sex Headcount FTE Mean_salary Median_salary
In post 2023 16-19 London London Female 65 65 27030 26920
In post 2023 16-19 London London Male 60 55 27260 26400
In post 2023 16-19 Outside London East Midlands (England) Female 40 40 24720 23230
In post 2023 16-19 Outside London East Midlands (England) Male 45 45 24510 23230
In post 2023 16-19 Outside London East of England Female 40 35 24030 23230
In post 2023 16-19 Outside London East of England Male 35 35 24510 23230
In post 2023 16-19 Outside London North East (England) Female 45 40 23520 22520
In post 2023 16-19 Outside London North East (England) Male 40 35 24070 22520
In post 2023 16-19 Outside London North West (England) Female 95 85 23370 22520
In post 2023 16-19 Outside London North West (England) Male 50 50 24100 22520
In post 2023 16-19 Outside London Northern Ireland Female [c] [c] [c] [c]
In post 2023 16-19 Outside London Northern Ireland Male [c] [c] [c] [c]
In post 2023 16-19 Outside London Scotland Female 65 60 22940 22520
In post 2023 16-19 Outside London Scotland Male 70 65 22460 22460
In post 2023 16-19 Outside London South East (England) Female 85 85 23970 22750
In post 2023 16-19 Outside London South East (England) Male 100 95 23520 23230
In post 2023 16-19 Outside London South West (England) Female 65 65 21840 21690
In post 2023 16-19 Outside London South West (England) Male 145 145 20560 19460
In post 2023 16-19 Outside London Wales Female 90 85 21840 22500
In post 2023 16-19 Outside London Wales Male 70 65 21320 22150
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).
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.
Sex Self reported sex.
"Unknown" accounts for employees who were recorded with an unknown sex.
Age Age in 10 year bands. Age is calculated as at the reference date in each year (31st March), so entrants or leavers may have been up to one year younger at the date of exit or entry.
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).