Civil Service Statistics data browser (2023)

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

Status Year Sex Sexual_orientation Disability Region_london Headcount FTE Mean_salary Median_salary
In post 2023 Female Heterosexual / straight Declared disabled London 4665 4370 42260 36180
In post 2023 Female Heterosexual / straight Declared disabled Outside London 23340 20690 31290 28120
In post 2023 Female Heterosexual / straight Declared disabled Overseas 55 55 46800 42620
In post 2023 Female Heterosexual / straight Declared disabled Unknown 50 45 33880 28120
In post 2023 Female Heterosexual / straight Declared non-disabled London 28235 26625 45470 40800
In post 2023 Female Heterosexual / straight Declared non-disabled Outside London 117290 106385 33190 28440
In post 2023 Female Heterosexual / straight Declared non-disabled Overseas 620 615 51390 45060
In post 2023 Female Heterosexual / straight Declared non-disabled Unknown 235 220 35220 28880
In post 2023 Female Heterosexual / straight Undeclared London 1410 1335 44210 39150
In post 2023 Female Heterosexual / straight Undeclared Outside London 5160 4655 32060 28120
In post 2023 Female Heterosexual / straight Undeclared Overseas 20 20 [c] [c]
In post 2023 Female Heterosexual / straight Undeclared Unknown 15 10 [c] [c]
In post 2023 Female Heterosexual / straight Unknown London 3600 3485 45570 41880
In post 2023 Female Heterosexual / straight Unknown Outside London 10225 9520 35090 31280
In post 2023 Female Heterosexual / straight Unknown Overseas 220 220 56990 57290
In post 2023 Female Heterosexual / straight Unknown Unknown 25 20 [c] [c]
In post 2023 Female LGBO Declared disabled London 630 615 43690 39150
In post 2023 Female LGBO Declared disabled Outside London 2550 2425 31640 28130
In post 2023 Female LGBO Declared disabled Overseas [c] [c] [c] [c]
In post 2023 Female LGBO Declared disabled Unknown 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 2023-07-26, 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).
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.
Sexual_orientation Self reported sexual orientation.
"Undeclared" accounts for employees who have actively declared that they do not want to disclose their sexual orientation and "Unknown" accounts for employees who have not made an active declaration about their sexual orientation.
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).