Civil Service Statistics data browser (2023)

Data preview: All civil servants / Parent_department / Region_ITL2 / Responsibility_level_ungrouped / Region_ITL3

Status Year Parent_department Region_ITL2 Responsibility_level_ungrouped Region_ITL3 Headcount FTE Mean_salary Median_salary
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire AO Hertfordshire CC 40 40 23990 24480
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire EO Hertfordshire CC 40 35 29300 28870
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire G6 Hertfordshire CC [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire G7 Bedford [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire G7 Hertfordshire CC 85 75 60770 62890
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire G7 Luton [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire HEO Hertfordshire CC 10 10 34250 32660
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire SCS level Hertfordshire CC [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Bedfordshire and Hertfordshire SEO Hertfordshire CC 10 10 41940 41340
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire AO Berkshire 30 25 23920 24480
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire AO Buckinghamshire [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire EO Berkshire 30 25 28990 28200
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire G6 Berkshire 5 5 [c] [c]
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire G6 Oxfordshire CC [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire G7 Berkshire 45 40 59510 57590
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire G7 Oxfordshire CC [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire HEO Berkshire 15 15 33710 32820
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire SCS level Berkshire [c] [c] [c] [c]
In post 2023 Attorney General’s Departments Berkshire, Buckinghamshire and Oxfordshire SEO Berkshire 10 10 40780 40800
In post 2023 Attorney General’s Departments Cheshire EO Warrington 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 2023-07-27, 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).
Parent_department Government Department, total figures for both Ministerial and Non-Ministerial Departments include all of their Executive Agencies.
Responsibility_level_ungrouped With the exception of the centrally managed Senior Civil Service, government departments have delegated pay and grading. For statistical purposes departments are asked to map their grades to a common framework by responsibility level.
This table shows staff in their substantive responsibility level unless on temporary promotion in which case staff are recorded at the higher responsibility level.
Responsibility_level_ungrouped shows the mapped grades at a higher level of detail, however not all Government Departments / bodies have distinct grades that map exactly to these levels so figures should be considered as approximations and treated with caution.
Region_ITL2 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 2 divides into Northern Ireland, counties in England (most grouped), groups of districts in Greater London, groups of unitary authorities in Wales, groups of council areas in Scotland.
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.
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).