Public attitudes towards immigration and ethnic minorities

This project explores beliefs about immigration and its impact on society, as well as people's feelings towards immigrants and minority ethnic groups.
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Border control at UK airport with blurred figures walking past


This report draws on new data collected on the NatCen Panel, in combination with past surveys, to explore how attitudes to immigration and minority ethnic groups have changed over time. The report focuses on findings from questions asked about three groups: Black people, immigrants and Muslims. Respondents were asked how positively or negatively they felt each group, how comfortable they would be with them in different social situations (either appointed as their boss, living on the same street, or married to a close relative), and whether efforts to give them equal opportunities have gone too far or not far enough. 

The report also focuses on public attitudes to immigration - whether the impact of immigration on society is positive or negative and whether they support increased or decreased immigration in different situations (for immigration generally, for illegal immigration and for refugees). 

Having described the landscape of public attitudes and how they have changed over time, the report describes the findings of a segmentation analysis – grouping adults in the UK by their attitudes towards minorities and immigration across a range of different areas. 

Key findings

  • Attitudes towards immigration and ethnic minority groups were mixed.
    • UK adults were more likely to feel positive “in general” towards Black people, immigrants and Muslims than to feel negative.
    • A majority of people reported they would feel comfortable with having people from these groups married to a relative, living next door, or as their boss.
    • However, there were sizeable minorities with more negative attitudes. 6% of people felt negatively “in general” towards Black people, 16% towards Muslims and 20% towards immigrants.
  • People were more favourable towards Black people than immigrants or Muslims. However, attitudes towards Muslims and immigrants have improved over recent years whilst attitudes towards Black people have become less favourable.
  • People were more likely to have either neutral or negative views about levels of immigration. 
    • People were more likely to say that the number of immigrants to the UK should be reduced than that it should be increased (42% compared to 29%). 
    • When asked about illegal immigration, two thirds (65%) felt the government should do more to exclude illegal immigrants, compared to 15% who disagreed.
  • However, beliefs about the impact of immigration on society, after remaining fairly stable in the 2000s, have become more favourable from 2016 onwards.
    • When asked about the impact of immigration on society, just under half of UK adults felt the impact of immigration on society was positive across all of the different areas asked about (ranging from culture to access to public services). A minority (between 15% and 29%) reported that the impact of immigration would be negative.
  • A latent class analysis grouped the population into seven ‘classes’ of people with similar attitudes towards Black people, immigrants and Muslims and the impact of immigration:
    • The first two were typified by broadly positive attitudes. The first, ‘Enthusiastic pluralists’, accounting for 31% of adults, felt positively towards the three groups, was comfortable with them, and thought immigration would be good for the country. The second (16% of the population) group, ‘Comfortable inclusives’ were very similar but were neutral in their general feelings towards the three groups.
    • The next three, containing 32% of the population, were characterised by a combination of positive attitudes in some of the areas asked about and negative views on others. For example, one class ‘Wary of Muslims’ (containing 11% of the population) was likely to be neutral about the impact of immigration, comfortable in social situations with Black people, but also likely to feel negatively towards and be uncomfortable with Muslims.
    • The two remaining classes include one, ‘Concerned ethnocentrists’ (making up 13% of adults) which held consistently negative views of immigration (thinking it would be bad for the UK as a country), felt negatively towards the three groups asked about, and to be uncomfortable with those groups in the different scenarios. Finally, the seventh was a neutral group, which was likely to give neutral answers to all the questions included in this segmentation.


Fieldwork for this study was conducted using the random-probability NatCen Panel, with historical data drawn from a previous wave of the NatCen Panel and the European Social Survey. The NatCen Panel is a panel of people recruited from high-quality, random probability surveys such as the British Social Attitudes survey. Those agreeing to join the Panel are then invited to take part in additional short surveys covering a range of different topics either online or over the phone.

The wave of the NatCen Panel was conducted between 3rd November and 4th December 2022. A total of 2,169 of the 3,000 panel members invited to take part did so, either online or over the phone, giving a 72% survey response rate.

All data presented in this study is weighted to adjust for survey design and non-response, making the weighted estimates representative of the target population – all adults (18+) in the UK. All reported differences between groups were tested for statistical significance and are statistically significant at the 95% level: that is to say that they are unlikely to have occurred due to sampling chance.

The grouping of people into clusters (classes) was conducted using a statistical method called Latent Class Analysis. This technique helps identify groups of respondents that are as similar as possible within each group and as different as possible between different groups based on the survey responses used in this analysis. We tested several potential models with different variables and numbers of classes and selected the model which balanced goodness of fit to our data with substantive interpretability of the resulting groups. 

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