Guidance

Standards for ethnicity data

Published 17 April 2023

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This publication is available at https://www.gov.uk/government/publications/standards-for-ethnicity-data/standards-for-ethnicity-data

1. Introduction

The government’s Equality Hub has produced these standards for ethnicity data (from now on, ‘the standards’). They describe best practice when collecting, analysing and reporting ethnicity data.

1.1 The importance of ethnicity data

These standards contribute to action 6 of Inclusive Britain, the government’s action plan for racial equality:

To ensure more responsible and accurate reporting on race and ethnicity, the Race Disparity Unit (RDU) will, by the end of 2022, consult on new standards for government departments and other public bodies on how to record, understand and communicate ethnicity data.

Ethnicity data has become important in recent years. A person’s ethnicity is often collected in government datasets. The 2017 Race Disparity Audit identified many of these datasets and showed disparities that impacted different aspects of people’s lives. This led to the creation of Ethnicity facts and figures. This website contains over 180 government datasets about people from different ethnic groups and has proven to be a valuable resource for users of ethnicity data.

As well as the amount of ethnicity data, the quality of ethnicity data is also important.

The following publications have reiterated the importance of data quality in recent years:

Ethnicity data needs to be fit for purpose. This is so the government and other organisations can use the data to properly understand disparities and their causes, and to develop policies that reduce any unjustified disparities between ethnic groups.

1.2 The standards and the Code of Practice for Statistics

Producers of official statistics should commit to the standards in the Code of Practice for Statistics. This gives confidence that government statistics:

  • have public value
  • are high quality
  • are produced by people and organisations that are trustworthy

The code formally applies to official statistics. It also sets good practice for everyone who works with statistics.

RDU has based these ethnicity standards around the 3 ‘pillars’ of the code:

  • trustworthiness
  • quality
  • value

They give guidance on how to improve the quality of collection, analysis and reporting of ethnicity data. They also give more general guidance on trustworthiness and value.

The Code of Practice, as well as the Government Functional Standard, have more basic data considerations, such as why and when to collect data or conduct analysis, as well as expectations for the planning and undertaking of analysis to support well-informed decision making. Both the Government Functional Standard web page and the Code of Practice website also have supporting guidance and case studies.

1.3 Who the standards are for

The standards apply to people in government departments or public bodies who are:

  • collecting data about people’s ethnicity – for example, in surveys
  • analysing differences between ethnic groups
  • publishing ethnicity data – for example, in statistical releases

RDU would also encourage other organisations commissioned by government departments and other public bodies to undertake ethnicity data collection, analysis and reporting to also use the standards.

The standards have been written with the expectation that individuals in government departments or other public bodies collecting, analysing or reporting on ethnicity data would most often be analysts.

1.4 Using the standards

It can be difficult to collect ethnicity data. Asking for someone’s ethnicity can be a sensitive issue. Sometimes any perceived or actual sensitivity might arise from the reason for collecting the data. For example, whether you have properly explained why you are  collecting the data and how it might be used.

Asking for someone’s ethnicity can also be more complicated than asking them about their age or country of birth, for example. This is often because ethnicity is based on a combination of factors. These can include someone’s country of birth, nationality, language, skin colour and religion. It is a self-defined and subjective concept – everyone has their own view about their own ethnicity. Sometimes a person might give an ethnicity that they think a questioner is most likely to accept.

Drawing conclusions from ethnicity data might also be difficult when it is based on a small number of people. For example, survey data based on smaller numbers of people might have relatively wide confidence intervals. This can make it harder to understand real differences between different groups. It might not be comparable with other datasets you are interested in.

So, there are good reasons to use different ways to collect and analyse ethnicity data. These can depend on:

  • why the data is being collected
  • the questions you want to answer using the statistics
  • the budget or time you have available

However, using these standards as often as possible can help you collect, analyse and report ethnicity data more responsibly. It will help increase the comparability of data across government and wider society.

1.5 Reviewing use and impact of the standards

The Equality Hub will review the use and impact of the standards, drawing on evidence from the Office for Statistics Regulation (OSR).

OSR can review how data producers and users of ethnicity data are following the standards. OSR can also provide guidance on areas where collection, analysis and reporting of ethnicity data might be improved.

2. Key considerations: Trustworthiness

Read about trustworthiness in the Code of Practice for Statistics.

2.1 Data collection, reporting and analysis

Collect ethnicity data in a respectful way – it should support public interest

Your collection, analysis and reporting of ethnicity data should support a legitimate public interest. You should do this in the least intrusive way.

You should collect ethnicity data in a respectful way and acknowledge cultural sensitivities when you do, for example in the language that you use when collecting data. Understand the risks to data quality or survey response when asking for sensitive information. These might include the burden on survey respondents, or emotional impact, for example, in the case of children.

Supporting evidence and guidance

Understand what data can be legally collected about ethnicity, and comply with relevant legislation

If you are collecting ethnicity data, you should understand what data you can legally collect about an individual’s ethnicity. You should follow relevant legislation.

Ethnicity data is classed as special category data under the General Data Protection Regulation (GDPR). Special category data is personal data that needs more protection because it is sensitive.

In order to lawfully process special category ethnicity data, you must identify both a lawful basis under Article 6 of the UK GDPR and a separate condition for processing under Article 9. These do not have to be linked.

There are 10 conditions for processing special category data in Article 9 of the UK GDPR.

5 of these require you to meet additional conditions and safeguards set out in UK law, in Schedule 1 of the DPA 2018.

You must determine your condition for processing special category data before you begin this processing under the UK GDPR, and you should document it.

In many cases you also need an ‘appropriate policy document’ in place in order to meet a UK Schedule 1 condition for processing in the DPA 2018.

You need to complete a data protection impact assessment (DPIA) for any type of processing which is likely to be high risk. You must therefore be aware of the potential risks to individuals’ rights of processing the special category data.

More information on special category data is available through the link in the supporting evidence and guidance below.

Build capability

To improve ethnicity data quality, you should dedicate resources to building capability in assessing, improving and communicating ethnicity statistics.

You might do this through training and sharing best practice. The Government Analysis Function supports government analysts in this way through its training courses. It can support users in the wider community through its guidance hub.

Protect the privacy and identity of individuals in your data at all times

You must protect the privacy and identity of individuals in your ethnicity data at all times. This means during data:

  • collection
  • storage
  • analysis
  • reporting

Be clear and open with people about how you will protect their ethnicity information.

You should apply relevant security standards to keep data secure. If necessary, use disclosure control methods when releasing statistics such as not disclosing data based on a small number of people, or by rounding numbers.

Supporting evidence and guidance

Regularly review your ethnicity data to ensure that it remains relevant

You should understand the public debate on data about ethnicity. This will help ensure your statistics stay relevant to a changing society. You might sign up to newsletters, blogs and social media about ethnicity data and policy development for example, from:

Your ethnicity analyses and reports should be regularly reviewed with users and other stakeholders. Stakeholders could include people in relevant ethnic minority groups. This will help you prioritise any development of the data.

You might identify user needs that are not met or only partially met by how your ethnicity data is collected. You should consider how you can meet those needs in your work programme. This will involve working with stakeholders, relevant ethnic minority groups, and subject experts.

3. Key considerations: Quality

Read about quality in the Code of Practice for Statistics.

This section lists some of the important things for determining quality that you should consider when collecting, analysing, and reporting on ethnicity data.

It is not always possible to be definitive in some parts of the standards. For example, the size of a survey sample needed to produce reliable results will vary. This is because data quality is dependent on what you are analysing and the data you have available.

A dataset that is good quality for one analysis might not be good quality for another.

3.1 Data collection

Be clear about the importance of collecting ethnicity data

The introduction to the standards noted the importance of ethnicity data. For example, the data can be used to develop policies that reduce unfair disparities between ethnic groups. Sometimes collecting (and reporting) ethnicity data might be an obligation for you.[footnote 1] Following the standards might help support compliance with some obligations.

You should ensure you have complied with the relevant data protection laws and principles to ensure that you have a lawful basis for processing the ethnicity data.

You should understand the importance, purpose and value of collecting ethnicity data. Collecting ethnicity data for your specified purpose and communicating this to respondents in surveys and other data collections could lead to higher engagement, higher response rates and higher quality responses.

You might do this through an accompanying public statement on why ethnicity data is needed.

Feedback from users can help you decide:

  • the level of detail you need to collect (described in more detail below)
  • how often you need to collect the data

At the start, think about the purposes for which you will use the ethnicity data you will collect

You might think about some of the following questions when designing a new ethnicity data collection or adding ethnicity to an existing one:

  • what are the reasons for your data collection?
  • what survey sample sizes do you need for the analysis, for example, to be able to detect any significant differences between ethnic groups?
  • how will you design your survey, and how will you sample people?
  • do you need to collect data on specific groups, for example, ethnic groups and geographies? – this will have an impact the classifications you will use
  • how many people in different groups will you want to sample?
  • how will you group ethnicities together and how will this affect comparability over time or with other datasets you are interested in?
  • how often will you collect the data?
  • how much change do you think is likely over time?

Some of this is likely to be easier with a sample survey than with administrative data collection.

If you are collecting administrative data, you should use your stakeholder relationships to change data collections to collect ethnicity data, especially if there is a strong user need. Stakeholders could also be consulted on the level of detail and approaches to grouping ethnicity categories if that is required.

Decisions about sampling methods are complex. These standards cannot provide definitive instructions for each situation but you will need to consider:

  • the sample frames, which list the businesses and household addresses you might select for a survey – sometimes you might only want to sample people in certain ethnic groups
  • which types of sampling (for example, stratified and clustered designs), which specify how you group businesses and households when sampling that can help make surveying more efficient
  • sample size specification for ethnic groups, with implications for cost, respondent burden, and output quality
  • sample selection mechanisms, which are used for choosing, with a random element, the particular units (households, people, businesses, schools) that will make up any given survey sample

Using qualitative evidence to improve your data collection

When designing data collection that is specific to ethnic minority groups you should understand the cultural and personal experiences of the population being studied.

Co-design and co-production of research (design and production being led by those being studied) is the gold standard to help design questionnaires and surveys. This is not always feasible but using a mixed methods approach is a helpful step towards this gold standard. To inform the design of questionnaires and surveys you can use, for example:

  • focus groups
  • depth interviews
  • ethnographic studies

This will allow you to ask the most relevant questions appropriately, taking into account ethnic minority experiences and using culturally sensitive and inclusive language.

Collect ethnicity data using the GSS harmonised standards, or more detailed groups that you can align with the harmonised standards

The Government Statistical Service (GSS) develops and maintains the harmonised standards for ethnicity. The RDU’s preferred approach is to use these standards to collect ethnicity data. You can collect data using more detailed categories, as long as you can align them to the harmonised groups. If you are collecting data for more detailed categories that are not part of the harmonised standard, you should provide information on how they map to aggregated categories in the harmonised standards. This will help users compare with other datasets.

Harmonising ethnicity data will allow you and other analysts to gain deeper insight and value from the data. This delivers more meaningful statistics that give users a greater level of understanding and better meet user needs. Cost savings can also be achieved by avoiding duplication.

You could also give additional options for respondents to provide their ethnicity. In the 2021 Census for England and Wales  ‘search-as-you-type’ boxes were used for the ‘Any other…’ write-in option under each high-level ethnicity category.

In addition, the ‘Any other mixed or multiple background’ had a write-in box and not ‘search-as-you-type’.

Options like these can give people a chance to respond if they don’t identify with any of the categories in the harmonised list although different options can impact on the quality of data, particularly over time. Analysts in the GSS or the Cabinet Office Equality Hub can provide guidance on how write-in responses can be grouped into categories in the harmonised standards.

Your data collection should also have a ‘prefer not to say’ option for people who do not want to give their ethnicity.

If you are collecting data for the whole of the UK, you should use the UK harmonised standard. If this is not possible, then you should follow guidance from the GSS on which standard to use for the different countries of the United Kingdom for collecting ethnicity data.

A Government Digital Service (GDS) set of design patterns exists for collecting equality information, including ethnicity. Using these patterns to collect equality information in a consistent way across the public sector makes data more coherent.

If you are commissioning data collection to other organisations, ensure that they also use harmonised standards during data collection.

RDU supports the use of harmonised standards. In some instances using the harmonised standards might not be possible because of:

  • cost
  • time
  • a need to maintain a consistent time series
  • a need to collect data for groups of interest

You should provide reasons for not using the harmonised standards and explain any implications for use. This is required by the Code of Practice for Statistics.

Supporting evidence and guidance

Collect data on religion and national identity

Consider collecting data on national identity and religion.

It is recommended that you use the harmonised standards and GDS design patterns for these questions, and in the recommended order:

  • national identity
  • ethnic group
  • religion

Including the national identity and religion questions helps people to give details about their full cultural identity.

As with ethnicity data you should ensure you have complied with the relevant data protection laws and principles to ensure that you have a lawful basis for processing the national identity and religion data.

Supporting evidence and guidance

Ask people to report their own ethnicity

The best and most practical way of collecting data about people’s ethnicity is to ask them – for them to ‘self-report’ their ethnicity. How people respond can depend on the context and who is collecting the data though.

Someone’s ethnicity can change over time. You should be mindful of this when considering how often to ask people for their ethnicity in data collections that are repeated over time.

Ethnicity data that has been reported by someone else – ‘third party’ or ‘proxy’ reporting – will be of lower quality than when someone reports their own ethnicity. It might not necessarily reflect the ethnicity the person themselves would respond with.

Supporting evidence and guidance

Design data collections to increase response rates for different ethnic groups

You should follow established best practice in conducting surveys to increase response rates and reduce the amount of missing ethnicity data. You should address barriers to disclosure that could disproportionately affect some ethnic groups in the sampling, survey and questionnaire design. For example, you might:

  • choose an appropriate survey mode
  • help respondents understand the uses of their data
  • reduce the burden on respondents by considering the length, difficulty and whether the data collection is emotionally stressful
  • provide translated materials and multilingual phone lines,
  • offer incentives (or higher incentives) for participation
  • send follow-up emails or make follow up calls to people who haven’t responded
  • include wording to help respondents be clear on the benefits of giving their ethnicity and other personal information.

One aim of data collection might be to reduce the amount of missing ethnicity data. However, lower rates of missing data does not always mean better data quality. A person responding with ‘prefer not to say’ is preferable to them answering with an ethnic group they don’t identify with.

Supporting evidence and guidance

Design data collections to increase the representativeness of ethnic groups

People from ethnic minority groups are under-represented compared to the population in some data collections. This can reduce the quality of your data and the validity of any conclusions you can draw from it.

Underrepresentation of a group in a survey might be related to response rates or the sample that is being used.

For administrative datasets, we recommend you follow best practice to ensure the distribution in your data collection reflects the population from the latest census data, for example. This will be dependent on the scope of your administrative data collection – you might need to look at working age adults, or people in employment to compare. If census data does not have appropriate data for you to compare, you could use a large survey dataset like the Annual Population Survey.

For some surveys it might be better to have different (non-proportional) distributions of ethnic groups. This might allow you to analyse differences between ethnic groups.

You can increase the number and proportion of people from different ethnic groups in surveys through a ‘survey boost’ or by ‘oversampling’.

The number of respondents in a survey are sometimes boosted (increased):

  • for the whole country
  • in smaller areas such as local authorities
  • in different types of areas such as rural areas
  • for specific groups of people – for example, people with different ethnicities
  • for a combination of all these factors

A survey boost can happen proportionately across groups or areas. Sometimes groups or areas are boosted disproportionately (or oversampled) relative to their proportion in the population. For example, in 2021, 3.1% of people in England and Wales are from the Indian ethnic group. If you are interested in improving the reliability of data for that population in a survey, you might try to ensure that 5% of your survey sample was from the Indian ethnic group.

To increase representation in this way, it is important to consider how and where you will sample people for your survey. Other techniques such as screening can help increase representation – that is, finding out the ethnicity of a potential respondent before they are able to answer a survey.

Bespoke or local surveys can also help you collect data for specific ethnic groups.

Supporting evidence and guidance

Use data linkage to improve ethnicity data quality

You can use data linkage to fill in incomplete records, or improve the quality of ethnicity classification in a dataset. If ethnicity records are used from a linked dataset that are known to be more accurate, this might improve the quality of the data.

You might consider whether the linked dataset:

  • uses harmonised categories
  • has a relatively low proportion of missing or unknown ethnicity
  • has any other known issues such as whether the ethnicity data is self-reported or has a relatively high proportion of proxy reported records

Linking data needs to be done in a consistent, reliable, and ethical way, whilst safeguarding privacy and complying with GDPR.

3.2 Data analysis

Consider the importance of analysing ethnicity data

The introduction to the standards noted the importance of ethnicity data. Understanding the data needs of your users will help you decide on the importance of analysing ethnicity data. In some cases, it might also be an obligation.

Weight survey data to correct for bias. You might include ethnicity as one of the weighting factors

You should weight your data to correct for bias in the collection or analysis of data. Bias can be due to different rates of non-response between different groups in the population.

These weights often include age, sex and geography but you might also include ethnicity as one of the weighting factors.

Survey weighting can be complex and often requires the input of an analyst.

Supporting evidence and guidance

Use harmonised categories for analysing ethnicity data

It is preferable to use the GSS harmonised categories when analysing ethnicity data. This will enable comparability with other government ethnicity statistics. When reliable data for the full harmonised set of classifications is not available, then you should use aggregated groups.[footnote 2]

However, if your data allows for the analysis and reporting of more detailed groups, then you should do this. Every additional disaggregation adds value to your analysis and allows a greater understanding of differences between ethnic groups.

If you are analysing more detailed categories that are not part of the harmonised standard, you should provide information on how they map to aggregated categories in the harmonised standards. This will help users compare with other datasets.

With more detailed information, the risk of disclosing information about individual people in your analysis and reports increases. You should take appropriate steps to ensure that data for ethnic groups is protected such as suppressing data or using rounding.

If you aggregate ethnic groups, you should note the limitations. For example, one limitation is that data for an aggregated ethnic group can hide differences between the constituent  groups.

You should try to avoid using binary categories in your analysis. An example of this is using white and ‘other than white’ (or ‘BAME’, a term the RDU avoids using). Binary classifications have little analytical value as they can mask significant differences in outcomes between different groups.

Avoid aggregating data for ethnic groups together in a non-harmonised way. This is because it reduces the comparability of your analysis with other datasets.

If you are analysing data for the whole of the UK, you should use the UK harmonised standard. If this is not possible, then you should follow guidance from the GSS on which standard to use.

If you are commissioning data analysis to other organisations, ensure that they also use harmonised standards in their analysis.

Supporting evidence and guidance

A person’s ethnicity can change over time and your analysis should take account of that

You will usually want to have a single ethnicity record for each person. Sometimes multiple ethnicity records are collected for each person in a dataset (such as in hospital data for each patient visit) and a person might give a different ethnicity on different visits. You should use appropriate rules to select one ethnicity per person, such as using the most recent ethnicity provided.

It is important to describe what rules you have used in your analysis.

Supporting evidence and guidance

Use appropriate comparators in your analysis

You should use a range of comparators in your analysis. This is to ensure that your selected comparator does not risk being misleading.

You can use any ethnic group as the comparator. A larger comparator group makes some comparisons more reliable. In practice, the availability of data is often a main consideration.

RDU has used the white British group as a comparator. This is preferable to comparing to the white group as a whole because it can show any disparities associated with white minority groups, such as Gypsy, Roma and Irish Travellers.

Comparing with the white British group does require you to disaggregate data for the white group. This might not always be possible. If this is the case then you might use the aggregate white group instead.

Using the total population as your comparator is the most ‘neutral’ approach. This avoids the perception that the white or white British groups are some sort of ‘ideal’. This approach does include an element of comparing an ethnic group against itself, as the group will be in the comparator.

If you are analysing an ethnic group with a small population, such as the Gypsy and Irish Traveller group, this might be an acceptable compromise as the impact on the total will be small.

Supporting evidence and guidance

Find out whether the geographic clustering of some ethnic groups has produced unusual or unexpected results

You might consider whether the geographic clustering of some ethnic groups has produced unusual or unexpected results. An example is the effect of geographical differences in stop and search data.

You should document issues like these in metadata associated with the analysis.

Supporting evidence and guidance

Take other socio-economic and demographic factors in your data into account

If you have other socio-economic and demographic factors available in your data, you should try to take them into account to better understand differences between ethnic groups.

For example, people in ethnic minority groups tend to be younger on average than white British people and are more likely to live in urban areas. This can impact on your comparisons if the data is not adjusted for age and geography. Other factors you might consider could be sex, disability and socioeconomic background.

What factors you use will be informed by data availability and the analysis you are undertaking.

You might present these adjusted differences alongside the unadjusted analysis of the raw data.

You should use correct techniques to address analytical questions. Different types of analytical adjustment can answer different questions. Taking the advice of an analyst, when appropriate, can help you understand different types of techniques.

You can use other methods to improve the reliability of ethnicity data, such as adding together more than one time period. Note any limitations of using these methods.

Take account of people who do not provide their ethnicity in your analysis

It is usually appropriate to remove records of people who ‘prefer not to say’ or who do not respond at all to the ethnicity question from an analysis. For example, percentage distributions should be based only on people who gave their ethnicity.

Supporting evidence and guidance

3.3 Data reporting

Consider the importance of reporting ethnicity data

You should also consider the importance of reporting ethnicity data. Having a good understanding of user needs can inform your decision about whether to report ethnicity data and what to report. Sometimes reporting it is an obligation.

You should refer to ethnicity and not race

RDU refers to ethnicity and not race. This is because:

  • data collections usually ask people for their ethnicity and not their race
  • using consistent terms helps people to understand your data

Use GSS harmonised categories for reporting on ethnicity data

The same considerations apply here as in the data analysis section around:

  • using harmonised standards
  • aggregating ethnic groups appropriately
  • showing additional ethnic groups if the data supports it
  • noting the limitations in the way you have aggregated ethnic groups
  • making sure your reporting does not reveal information about individual people
  • if you have commissioned reporting to another organisation, ensure that they use harmonised standards

Where you have analysed and reported more detailed categories, you should provide information on how they map to aggregated categories in the harmonised standards. This will help users compare with other datasets.

Exercise caution if you are making international comparisons of ethnicity data

Comparing ethnicity data across different countries can be problematic because of:

  • differences in population make-up that determine ethnicity classifications
  • the subjective nature of ethnicity
  • differences in the terms used to describe ethnic groups

There are 2 main issues in comparing ethnicity data for different countries:

  • ethnicity data is not collected in some countries
  • ethnicity is classified differently in different countries

Any comparisons must be made very carefully, and with appropriate explanations in place so that users can understand how and why data might not be completely comparable.

Report potential biases to allow users to understand limitations in the ethnicity data, and how this impacts on the interpretation of your analysis

You should report any biases in the metadata accompanying your analysis. These biases might be due to data collection, analysis or reporting. Reference to these should be included in any commentary. This allows users to understand any limitations of the data and the impact on the interpretation of your analysis.

In particular, you should report any biases that may arise from the way administrative systems collect and categorise data.

You might report some of the following issues:

  • the proportion of ethnicity records that have been proxy reported and by whom
  • how you have selected one ethnicity for a person, if more than one is available
  • the proportion of imputed ethnicity records
  • the proportion of records with missing ethnicity
  • the response rates
  • the impact on ethnic groups of weighting data
  • the impact of any sample boosts on the reliability of estimates for different ethnic groups
  • the impact of data linkage, including reporting data linkage rates and accuracy for different ethnic groups
  • the consistency of ethnicity data over time
  • design factors for complex surveys

You should keep metadata up to date.

Supporting evidence and guidance

Report measures of reliability so users can correctly understand and interpret the data

You should report appropriate measures of reliability. This allows your users to make informed decisions about how to use your ethnicity analysis.

You might report the following:

  • a measure of the quality of the ethnicity coding, for example the proportion of proxy ethnicity reporting, and what rules have been to choose someone’s ethnicity if you have multiple ethnicity records for them
  • confidence intervals
  • standard errors
  • coefficients of variation
  • sample sizes – both weighted and unweighted numerators and denominators
  • measures of relative likelihood
  • the use of overlapping confidence intervals or appropriate statistical tests to detect significant differences in data
  • how aggregating time periods has impacted on the reliability and timeliness of estimates

Some of these measures might require the advice and assistance of an analyst.

Supporting evidence and guidance

You should try to report differences between ethnic groups using data that has been adjusted to take into account other socio-economic and demographic factors

You should try to report differences between ethnic groups that have been adjusted to take into account other socio-economic and demographic factors. You might also report differences between ethnic groups of analysis of the raw (unadjusted) data.

You should understand any issues reporting either of these ethnicity analyses, or reporting both.

You should consider ethnicity data along with data on other personal characteristics

Having a broad cultural awareness can help understand what your ethnicity data is showing. Patterns that may apparently be attributable to ethnicity might be more reflective of other socioeconomic or personal characteristics.

Analysing ethnicity along with other characteristics such as age, sex and geography might help show those patterns.

Supporting evidence and guidance

Be transparent in your reasons for using specific comparators

Whichever comparators you use (for example, another ethnic group or a time period) you should report the reasons for using them.

Report your reasons for comparison with specific distributions, such as the census of population.

Follow best practice when writing about ethnic groups – for example, the writing principles developed by RDU

RDU has developed guidance for writing about ethnicity that you use in your reporting. The guidance shows:

  • words and phrases RDU uses
  • words and phrases RDU avoids, such as ‘BAME’
  • how RDU describe different ethnic groups
  • capitalisation of the names of ethnic groups

Supporting evidence and guidance

4. Key considerations: Value

Read about value in the Code of Practice for Statistics.

4.1 Data collection, reporting and analysis

Your ethnicity statistics should meet their intended uses and inform public debate

Your ethnicity statistics should meet their intended uses.

The statistics should inform public debate.

You should seek to understand your user base and the questions that users want to answer with your ethnicity data.

You should maintain and refresh your understanding of the use and potential use of the ethnicity statistics and data. You should consider the ways in which the statistics might be used and the nature of the decisions that are or could be informed by them.

Your supporting commentary should provide clarity and insight. It should describe any assumptions. This will enable your users to draw the correct conclusions from your data.

You can enhance your insight into your ethnicity data by consultation with subject experts.

Users of ethnicity data are diverse and have different data needs. You should understand whether certain users have other specific requirements. This might include the availability of information in different languages.

Enhance ethnicity statistics to meet new or evolving user needs

You should identify any evolving or new user needs for ethnicity statistics. You should try and enhance the data that inform these statistics to meet the user’s needs.

Where you cannot meet users’ needs, you should report why this is the case. You should also report anything in the existing data that will help these users.

Report new ethnicity datasets to the ONS Equalities Data Audit

You can increase the user value of data by adding new ethnicity data collections to the ONS Equalities Data Audit.

Supporting evidence and guidance

Make decisions about whether to continue, discontinue or adapt ethnicity data and statistics in discussion with users

You should make decisions about whether to continue, discontinue or change ethnicity data and statistics in discussion with users.

You should publish explanations of changes to ethnicity data collections. The explanations should include evidence of the rationale for the change. You should also publish any analysis that informs the change.

Your decision-making processes should be transparent and open

There may be times when you are unable to meet the requests of everyone who has an interest in your ethnicity statistics. In these cases, it is important to be open about your decision-making process. You should document evidence used to inform these decisions, particularly in relation to areas of contention.

Supporting evidence and guidance

5. Annex A: areas to consider for collecting, analysing and reporting ethnicity data

This Annex sets out the areas of the standards under the headings of collecting, analysing and reporting ethnicity data.

5.1 Collecting data

  • Collect ethnicity data in a respectful way – it should support public interest
  • Understand what data can be legally collected about ethnicity, and comply with relevant legislation
  • Build capability
  • Protect the privacy and identity of individuals in your data at all times
  • Regularly review your ethnicity data to ensure that it remains relevant
  • Be clear about the importance of collecting ethnicity data
  • At the start, think about the purposes for which you will use the ethnicity data you will collect
  • Use qualitative evidence to improve your data collection
  • Collect ethnicity data using the GSS harmonised standards, or more detailed groups that you can align with the harmonised standards
  • Collect data on religion and national identity
  • Ask people to report their own ethnicity
  • Design data collections to increase response rates for different ethnic groups
  • Design data collections to increase the representativeness of ethnic groups
  • Use data linkage to improve ethnicity data quality
  • Your ethnicity statistics should meet their intended uses and inform public debate
  • Enhance ethnicity statistics to meet new or evolving user needs
  • Report new ethnicity datasets to the ONS Equalities Data Audit
  • Make decisions about whether to continue, discontinue or adapt ethnicity data and statistics in discussion with users
  • Your decision-making processes should be transparent and open

5.2 Analysing data

  • Build capability
  • Protect the privacy and identity of individuals in your data at all times
  • Regularly review your ethnicity data to ensure that it remains relevant
  • Consider the importance of analysing ethnicity data
  • Weight survey data to correct for bias. You might include ethnicity as one of the weighting factors
  • Use harmonised categories for analysing ethnicity data
  • A person’s ethnicity can change over time and your analysis should take account of that
  • Use appropriate comparators in your analysis
  • Find out whether the geographic clustering of some ethnic groups has produced unusual or unexpected results
  • Take other socio-economic and demographic factors in your data into account
  • Take account of people who do not provide their ethnicity in your analysis
  • Your ethnicity statistics should meet their intended uses and inform public debate
  • Enhance ethnicity statistics to meet new or evolving user needs
  • Make decisions about whether to continue, discontinue or adapt ethnicity data and statistics in discussion with users
  • Your decision-making processes should be transparent and open

5.3 Reporting data

  • Build capability
  • Protect the privacy and identity of individuals in your data at all times
  • Regularly review your ethnicity data to ensure that it remains relevant
  • Use data linkage to improve ethnicity data quality
  • Consider the importance of reporting ethnicity data
  • You should refer to ethnicity and not race
  • Use GSS harmonised categories for reporting on ethnicity data
  • Exercise caution if you are making international comparisons of ethnicity data
  • Report potential biases to allow users to understand limitations in the ethnicity data, and how this impacts on the interpretation of your analysis
  • Report measures of reliability so users can correctly understand and interpret the data
  • You should try to report differences between ethnic groups that have been adjusted to take into account other socio-economic and demographic factors.
  • You should consider ethnicity data along with data on other personal characteristics
  • Be transparent in your reasons for using specific comparators
  • Follow best practice when writing about ethnic groups – for example, the writing principles developed by RDU
  • Your ethnicity statistics should meet their intended uses and inform public debate
  • Enhance ethnicity statistics to meet new or evolving user needs
  • Make decisions about whether to continue, discontinue or adapt ethnicity data and statistics in discussion with users
  • Your decision-making processes should be transparent and open

  1. Public Sector Equality Duty. ↩

  2. In the 2021 England and Wales Census, the aggregated groups were Asian, black, mixed, white, and other. ↩

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