Official Statistics

Methodology information for Home Office workforce diversity statistics: 2024 to 2025

Published 31 March 2026

1. Introduction

This document is supplementary to the Home Office workforce diversity statistics and provides more detail on the methodology used

2. Data source

The findings presented in this report are based on data from the department’s central human resources reporting system (known as ‘Metis’), as of 31 March 2025. The majority of the data has been self-reported by staff. Self-reporting is voluntary, and therefore the findings relate only to a subset (section) of the Home Office workforce.

In November 2024, changes were introduced to Metis, the central human resources reporting system for the department. These changes were designed to simplify and make the process to report diversity data quicker for staff. Simultaneously, the department started to use automated and targeted notifications to staff to encourage completion of their data declarations. These actions are aimed to improve the declaration rates for all diversity characteristics helping improve the department’s monitoring of diversity within the workforce.

The data provide a snapshot as of 31 March 2025. The data include overseas and non-paid staff, and most of the data has been self-reported by staff. Self-reporting is voluntary and therefore does not provide a complete picture of Home Office workforce diversity, as some staff choose to withhold information through the ’prefer not to say’ response. Others may not have been surveyed, so no data are available for these individuals. This is particularly true of overseas staff, for whom the department only holds information regarding sex. There may also be individuals who misreport, either through accident or design. While considered unlikely to be widespread, where percentages are very low, a few individuals would have noticeable effects on the results.

The department continues to explore how to improve its declaration rates, for example by making improvements to the categories an individual can self-define as, as well as consistent messaging on why data are needed, what they are used for, reassurance on data security, how data are stored and anonymity protection.

2.1 Senior Civil Service (SCS) data

Data for Senior Civil Service (SCS) staff in the department are taken from the Metis system. This is different to the data used in the Civil Service Diversity and Inclusion Dashboard, which uses the Cabinet Office SCS database. Therefore, representation rates calculated here may differ from those calculated using alternative data sources.

3. Other diversity considerations

The Civil Service Diversity and Inclusion Strategy: 2022 to 2025 highlights the importance of considering a broader definition of diversity, such as socio-economic, work experience and geographic backgrounds.

3.1 Socio-economic background

The Home Office collects data on socio-economic background, with the aim of providing richer data on representation within the workforce. This data is currently not of sufficient quality to include in this publication, but this information is planned to be included in future publications when the declartion rate improves. The declaration rate for socio-economic background data as at 31 March 2025 was 38.0%. This has increased from April 2021 when the declaration rate stood at 4.9%.

3.2 Gender identity

The department has recently introduced an option for staff to declare their gender identity on Metis. Currently, the majority of staff have not declared their gender identity. The department will review the reporting of gender identity every year.

4. Methodology

Representation rates have been calculated by excluding individuals with unknown characteristics. These include not being surveyed or responding with ‘prefer not to say’. In March 2025, the percentages of staff with unknown characteristics were 28% for sexual orientation, 33% for disability, 23% for ethnicity, 21% for religion, less than 1% for geography, and less than 1% of staff grades were unknown. Please see separate data tables with every X.2 table (where X is the table number) providing response numbers.

The respective totals of unknown characteristics for March 2024 were 37% for sexual orientation, 44% for disability, 32% for ethnicity, 29% for religion, less than 1% for geography, and less than 1% of staff grades were unknown. Work is ongoing within the department to improve declaration rates. The improved response rates in 2025 are likely a result of this work.

The representation rate is the proportion of people identified as having a specific characteristic (for example, female) as a proportion of all people with an identified characteristic (for example, total of all people identified as male or female).

In the analysis of representation, where the number of individuals with a characteristic was fewer than 10, the value has been removed and marked with a ‘c’ for confidential. Fewer than 10 individuals indicate that either representation is very close to 0%, or that the population of people with a disclosed characteristic (for example, ethnicity, age, sex) is so small that representation rates are heavily influenced by individual people and do not provide a reliable narrative of the bigger picture. Categories with fewer than 10 individuals have also been redacted to protect the anonymity of individuals with characteristics in smaller groups.

Data included in Figures 4, 6 and 8, and Table 7, 9 and 11 include unknown grades; this may differ from the separate accompanying tables by 0.1 percentage points or less, as the accompanying tables exclude unknown grades.

5. Rounding

Data may be rounded to simplify the presentation of the figures. However, all numeric and percentage calculations are based on unrounded data. Where data are rounded, they may not add up to the totals shown or to 100% in the case of percentages, because they have been rounded independently.

Unless otherwise stated, all percentages are rounded to the nearest 0.1%.

Similarly, all percentages in the separate data tables are rounded to the nearest 0.1%.

6. Definitions

6.1 Business areas

For the purposes of these statistics, Home Office teams, units and directorates are grouped into 5 areas according to their broad area of work.

6.1.1 Migration and Borders Operations

Staff in this area include those working in:

• Border Force
• Immigration Enforcement
• His Majesty’s Passport Office
• the operation areas of Asylum Support, Resettlement and Accommodation (ASRA)

This grouping has changed compared with 2022’s Home Office workforce diversity statistics 2021 to 2022. Migration and Borders Operations and Migration and Borders Policy were previously grouped under a Migration and Borders grouping; however, these have now been separated to better reflect the type of work each area undertakes.

6.1.2 Homeland Security

Staff in this area work to counter threats from terrorism.

6.1.3 Public Safety

This includes staff who support work on policing and fire and rescue services.

6.1.4 Corporate and Support

This includes staff that support other functions through a variety of means, including analysis, private office, Human Resources and Inoformation Technology.

6.1.5 Migration and Borders Policy

This includes staff who support on the policy side of migration and borders related work. This was previously grouped with Migration and Borders Operations to make the Migration and Borders business area grouping.

6.2 Disability

The Equality Act 2010 defines disability as a physical or a mental condition which has a substantial and long-term effect on your ability to do normal day-to-day activities. The HR system before 2020 (Adelphi) asked staff the question, “Do you have a disability?” The Equality Act definition was not provided, so there may have been some under-reporting. The current system of Metis defines disability and then asks the question, “Do you have a disability?”

6.3 Ethnicity

The Home Office’s Metis system allows staff to identify as 19 distinct ethnic groups, which are grouped together into the following categories for reporting in this publication:

• Asian or Asian British
• Black or Black British
• Mixed
• Other ethnic group
• White

These groupings have changed compared with Home Office workforce diversity statistics 2022 to 2023. The ‘Other ethnic group’ category was previously referred to as ‘Chinese or other ethnic group’ and included those from the Chinese ethnic group. Staff from the Chinese ethnic group are now grouped into Asian or Asian British.

6.4 Lesbian, gay, bisexual (LGB)

The Home Office’s Adelphi system offered the self-reporting categories of ‘heterosexual/straight’, ‘LGB’ or ‘prefer not to say’. In 2020, the department introduced a new HR record system called Metis to replace Adelphi. Metis provides a broader set of categories for sexual orientation. Currently the disclosure rates of these broader categories are too low to report in this publication. The department will review the reporting of the broader set of categories every year.

The 2023 sexual orientation data was reported using the options of either ‘heterosexual/straight’, ‘LGB’ or ‘other’. From 2024, a new category ‘LGB+’ has been included, which combines the ‘LGB’ and ‘other’ options used in 2023. ‘LGB+’ includes those reporting as LGB (‘gay man’, ‘gay woman/lesbian’ and ‘bisexual’) as well as ‘asexual’, ‘pansexual’ or ‘other’.

Unless otherwise stated, figures and tables show representation rates for the LGB group to keep consistency with previous publications. Representation rates of LGB+ staff are included where relevant to add further context.

The higher target for lesbian, gay and bisexual (LGB) staff set by the Home Office in 2018 was based on estimates created for the passage of civil partnership legislation. Since then, the Office for National Statistics (ONS) has published estimates from the 2021 Census on sexual orientation which is used as the source for table 5. However the LGBT+ population is likely to be larger, please see this ‘2021 census: What do we know about the LGBT+ population? for reasons why.

6.5 Sex

The Home Office Metis system allows staff to identify as either female or male and therefore this terminology is used when referring to the data; however, when referring to targets we use the term women.