Civil Service Statistics data browser (2026)

Data preview: All civil servants / Region_ITL2 / Region_ITL3

Explore further: Parent_department, Organisation, Responsibility_level_grouped, Responsibility_level_ungrouped, Region_london, Region_ITL1, Profession_of_post, Function_of_post, Sex, Ethnicity, Disability, Sexual_orientation, Age

Status Year Region_ITL2 Region_ITL3 Headcount FTE Mean_salary Median_salary
In post 2026 Bedfordshire and Hertfordshire Bedford 935 875 38030 36740
In post 2026 Bedfordshire and Hertfordshire Central Bedfordshire 440 415 39210 33640
In post 2026 Bedfordshire and Hertfordshire Luton 655 575 35510 32140
In post 2026 Bedfordshire and Hertfordshire North and East Hertfordshire 645 595 41400 35620
In post 2026 Bedfordshire and Hertfordshire South West Hertfordshire 2190 2095 44780 39460
In post 2026 Berkshire, Buckinghamshire and Oxfordshire Berkshire East 800 755 40060 35620
In post 2026 Berkshire, Buckinghamshire and Oxfordshire Berkshire West 1825 1720 43910 39890
In post 2026 Berkshire, Buckinghamshire and Oxfordshire Buckinghamshire 1865 1765 40830 38770
In post 2026 Berkshire, Buckinghamshire and Oxfordshire Milton Keynes 2305 2215 42140 39030
In post 2026 Berkshire, Buckinghamshire and Oxfordshire Oxfordshire CC 2790 2625 41000 37820
In post 2026 Cambridgeshire and Peterborough Cambridgeshire CC 3615 3425 41970 37950
In post 2026 Cambridgeshire and Peterborough Peterborough 1745 1580 36290 32120
In post 2026 Cheshire Cheshire East 1000 935 38790 34930
In post 2026 Cheshire Cheshire West and Chester 545 485 37310 32140
In post 2026 Cheshire Warrington 2840 2690 43740 42270
In post 2026 Cornwall and Isles of Scilly Cornwall and Isles of Scilly 1965 1785 35200 32140
In post 2026 Cumbria Cumberland 1840 1685 33890 29990
In post 2026 Cumbria Westmorland and Furness 585 550 44760 40450
In post 2026 Derbyshire and Nottinghamshire Derby 1190 1065 34920 32140
In post 2026 Derbyshire and Nottinghamshire East Derbyshire 320 290 38100 32140
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 2026-07-16

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.
Year Year of data collection (as at 31 March).
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).