Civil Service Statistics data browser (2023)

Data preview: All civil servants / Sex / Region_london / Region_ITL3 / Ethnicity

Status Year Sex Region_london Region_ITL3 Ethnicity Headcount FTE Mean_salary Median_salary
In post 2023 Female London Barking & Dagenham and Havering Asian 105 90 32720 32520
In post 2023 Female London Barking & Dagenham and Havering Black 85 80 33620 32520
In post 2023 Female London Barking & Dagenham and Havering Mixed 10 10 [c] [c]
In post 2023 Female London Barking & Dagenham and Havering Other ethnicity 10 5 [c] [c]
In post 2023 Female London Barking & Dagenham and Havering Undeclared 20 20 [c] [c]
In post 2023 Female London Barking & Dagenham and Havering Unknown 20 20 [c] [c]
In post 2023 Female London Barking & Dagenham and Havering White 150 130 32420 32520
In post 2023 Female London Barnet Asian 280 260 39240 32750
In post 2023 Female London Barnet Black 130 125 35510 32520
In post 2023 Female London Barnet Mixed 50 45 36530 32520
In post 2023 Female London Barnet Other ethnicity 20 20 [c] [c]
In post 2023 Female London Barnet Undeclared 65 60 41490 35410
In post 2023 Female London Barnet Unknown 95 85 34720 32520
In post 2023 Female London Barnet White 395 375 44340 40880
In post 2023 Female London Bexley and Greenwich Asian 60 50 31780 30930
In post 2023 Female London Bexley and Greenwich Black 160 155 33550 32520
In post 2023 Female London Bexley and Greenwich Mixed 30 25 [c] [c]
In post 2023 Female London Bexley and Greenwich Other ethnicity 5 5 [c] [c]
In post 2023 Female London Bexley and Greenwich Undeclared 30 25 31980 32520
In post 2023 Female London Bexley and Greenwich Unknown 140 135 31760 32110
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 2023-07-27, 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_ITL3 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 3 divides into counties, unitary authorities, or districts in England (some grouped), groups of unitary authorities in Wales, groups of council areas in Scotland, groups of districts in Northern Ireland.
Sex Self reported sex.
"Unknown" accounts for employees who were recorded with an unknown sex.
Ethnicity Self reported ethnicity. "Undeclared" accounts for employees who have actively declared that they do not want to disclose their ethnicity and "Unknown" accounts for employees who have not made an active declaration about their ethnicity.
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).