| Status | Year | Region_ITL1 | Profession_of_post | Region_london | Region_ITL2 | Headcount | FTE | Mean_salary | Median_salary |
|---|---|---|---|---|---|---|---|---|---|
| In post | 2023 | East Midlands (England) | Commercial | Outside London | Derbyshire and Nottinghamshire | 100 | 100 | 48800 | 43360 |
| In post | 2023 | East Midlands (England) | Commercial | Outside London | Leicestershire, Rutland and Northamptonshire | 30 | 30 | 42050 | 37060 |
| In post | 2023 | East Midlands (England) | Commercial | Outside London | Lincolnshire | 15 | 15 | 40270 | 38460 |
| In post | 2023 | East Midlands (England) | Communications | Outside London | Derbyshire and Nottinghamshire | 100 | 95 | 41240 | 40570 |
| In post | 2023 | East Midlands (England) | Communications | Outside London | Leicestershire, Rutland and Northamptonshire | 5 | 5 | [c] | [c] |
| In post | 2023 | East Midlands (England) | Communications | Outside London | Lincolnshire | 20 | 15 | 33240 | 34300 |
| In post | 2023 | East Midlands (England) | Counter Fraud | Outside London | Derbyshire and Nottinghamshire | 420 | 380 | 31890 | 28130 |
| In post | 2023 | East Midlands (England) | Counter Fraud | Outside London | Leicestershire, Rutland and Northamptonshire | 90 | 80 | 28760 | 28120 |
| In post | 2023 | East Midlands (England) | Counter Fraud | Outside London | Lincolnshire | 40 | 35 | 29090 | 28120 |
| In post | 2023 | East Midlands (England) | Digital, Data and Technology | Outside London | Derbyshire and Nottinghamshire | 280 | 275 | 41170 | 37680 |
| In post | 2023 | East Midlands (England) | Digital, Data and Technology | Outside London | Leicestershire, Rutland and Northamptonshire | 90 | 85 | 38350 | 37060 |
| In post | 2023 | East Midlands (England) | Digital, Data and Technology | Outside London | Lincolnshire | 45 | 45 | 34190 | 30320 |
| In post | 2023 | East Midlands (England) | Economics | Outside London | Derbyshire and Nottinghamshire | 5 | 5 | [c] | [c] |
| In post | 2023 | East Midlands (England) | Finance | Outside London | Derbyshire and Nottinghamshire | 160 | 155 | 40100 | 35000 |
| In post | 2023 | East Midlands (England) | Finance | Outside London | Leicestershire, Rutland and Northamptonshire | 35 | 35 | 36080 | 34170 |
| In post | 2023 | East Midlands (England) | Finance | Outside London | Lincolnshire | 35 | 35 | 27880 | 27060 |
| In post | 2023 | East Midlands (England) | Human Resources | Outside London | Derbyshire and Nottinghamshire | 370 | 350 | 41020 | 37890 |
| In post | 2023 | East Midlands (England) | Human Resources | Outside London | Leicestershire, Rutland and Northamptonshire | 75 | 75 | 36100 | 34170 |
| In post | 2023 | East Midlands (England) | Human Resources | Outside London | Lincolnshire | 115 | 115 | 28960 | 27300 |
| In post | 2023 | East Midlands (England) | Inspector of Education and Training | Outside London | Derbyshire and Nottinghamshire | 70 | 65 | 64470 | 52750 |
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 2023-07-28, 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. | |
| 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. | |
| Profession_of_post | Professions relate to the post occupied by the person and are not dependent on qualifications the individual may have. |
| Of the 20 bodies under the Scottish Government, 16 did not report any professions information for their employees. | |
| 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). |