The societal context of perceived racial discrimination

Rahsaan Maxwell Associate Professor

Department of Political Science University of North Carolina, Chapel Hill

Contact information: The Department of Political Science

322 Hamilton Hall • CB#3265 Chapel Hill, NC 27599-3265

rahsaan@email.unc.edu The research for this paper was funded by the University of Massachusetts, Amherst Faculty Research Grant/Healey Endowment Grant. Previous versions were presented at the 2012 Council for European Studies Conference of Europeanists, the 2012 Comparative Approaches to Immigration and Religious and Ethnic Diversity workshop (Princeton University), and “Identity Politics: The New World versus New (and Old) Europe” at the University of Texas, Austin (2013). The author would like to thank Rodolfo Espino, Marc Helbling, Daniel Hopkins, Tatishe Nteta, Brian Schaffner, and Alex Street for helpful comments on earlier drafts.

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ABSTRACT Existing research has developed a wide range of evidence about predictors of

perceived discrimination among minorities, but often generates conflicting evidence across studies. I claim this may be due to the measurement strategy. Existing research often uses cross-sectional surveys that ask minorities about their experiences with discrimination, but these questions cannot determine whether subjects with higher levels of perceived discrimination are more likely to experience discrimination or are just more likely to interpret events through the lens of discrimination. In this article, I propose a new research strategy designed to measure when minorities use discrimination as a heuristic for understanding society. I use an original online survey of non-whites in the United Kingdom and provide vignettes that describe racial inequality in education, employment, politics, the arts, and business. I then ask respondents how they would explain each form of inequality. This ensures that all minority respondents are reacting to the same stimulus. In addition to the new measurement strategy, I make a theoretical contribution to the standard analysis of variation across minority individuals by emphasizing variation in perceived discrimination across societal contexts. This contextual approach has implications for debates about racial minorities in the UK and perceived discrimination in general.

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Perceived discrimination is a key indicator of social cohesion. When racial and

ethnic minorities believe that society is biased against them they are likely to become

alienated (Safi 2010). Alienated minorities may then retreat from the political system,

weakening representation of their interests and the likelihood of improving their social

status (Schildkraut 2005). When they do act, dissatisfied minorities may choose tactics

of disruptive protest or violent revolt and in doing so contribute to societal disorder

(Lijphart 2012). Moreover, individuals who consider themselves fundamentally

disadvantaged due to their minority status may develop impermeable social and cultural

barriers with the majority population (Tajfel 1982: 23-4; Wimmer 2013: 174-203).

Despite widespread agreement that perceived discrimination is an important

measure of social cohesion, there is no consensus about which minorities are more likely

to have high levels of perceived discrimination. In fact, different studies often reach

contradictory conclusions. For example, some researchers find higher levels of perceived

discrimination among immigrant-origin minorities who recently arrived in their host

society while other studies find the opposite (Aguirre et al. 1989; Portes et al. 1980). One

explanation for these conflicting results is the way existing literature measures perceived

discrimination. Most research uses cross-sectional surveys that ask about experiences

with discrimination or opinions on the prevalence of discrimination in society. That

approach is vulnerable to measurement bias from differential item functioning.

Questions that explicitly ask about perceived discrimination cannot determine whether

subjects with higher levels of perceived discrimination are more likely to experience

actual discrimination or are just sensitive to the issue and more likely to use

discrimination as a heuristic for interpreting events. In addition, experiences with

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discrimination and sensitivity to discrimination may influence survey responses in ways

that vary across subjects and are unobservable to the researcher. As a result, analysis of

the overall sample may yield unreliable relationships between the individual-level

characteristics of interest and perceived discrimination. In short, measurement error may

help explain the conflicting results across studies.

I propose that different aspects of discrimination be approached through distinct

research strategies. The most reliable way of studying when minorities are more likely to

experience discrimination is by using experiments that manipulate minority

characteristics (e.g. skin color, national origin, or religion) in a given situation (e.g. job

application or social interaction) and then observe majority individuals’ responses (Sen

and Wasow 2013). In contrast, studies of minorities are more appropriate for

determining which minorities are more likely to view life through the lens of racial

discrimination. In this article, I focus on the latter task with an online survey of non-

whites in the United Kingdom (UK).

I build on previous studies by proposing a new strategy to measure when

minorities use discrimination as a heuristic for understanding society. Instead of posing

questions that explicitly ask respondents to reflect on discrimination, I provide vignettes

that describe racial inequality in education, employment, politics, the arts, and business,

and then ask respondents to generate their own explanation for each form of inequality.

Previous research has asked minorities about their views on general racial inequality and

it has allowed minorities to express their views on discrimination with open-ended

questions. The key innovation in my design is the combination of open-ended questions

and no explicit prompt on racial discrimination. Research on survey methodology

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suggests that open-ended questions are especially good at capturing how respondents

frame issues on their own terms (Achen 1975; Saris and Sniderman 2004). This may be

especially important for sensitive topics like discrimination, where respondents may be

reluctant to reveal their true attitudes to closed-ended questions that impose an external

framework on the issue (Tourangeau and Smith 1996). Moreover, the use of open-ended

questions that do not prompt respondents to think about discrimination provides a

standard stimulus and common benchmark for all respondents (Esses and Maio 2002;

Geer 1991). It is possible that previous experiences with discrimination may color the

ways in which minorities interpret future events (Kaiser, Vick and Major 2006), but tests

in the robustness checks section suggest that is not a concern for my analyses. In short,

the strategy of combining open-ended questions with no explicit prompt about

discrimination allows me to draw cleaner inferences about the conditions under which

minorities are more likely to view life through the lens of discrimination.

This article also makes a theoretical contribution to the study of perceived

discrimination. Existing literature primarily highlights variation in perceived

discrimination across minority individuals.1 In comparison, I emphasize variation across

societal contexts. I claim that the strength of specific individual-level predictors of

perceived discrimination will vary according to context. I also propose that there is more

variation in the likelihood of citing discrimination across context than across minority

individuals. Finally, I argue that contexts where minorities have less control over the

outcome will be more likely to elicit perceptions of discrimination.

1In part this focus reflects the ongoing debate mentioned earlier about the relationship between assimilation and perceived discrimination. There have also been a series of counterintuitive findings in the United States about the positive relationship between socio-economic attainment and perceived discrimination, which have sparked ongoing research into individual-level predictors of perceived discrimination (Chong and Kim 2006; Cose 1995).

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My approach has implications for debates about racial minorities in the UK and

perceived discrimination in general. Most notably, my focus on context implies that all

minorities have the capacity to view life through racialized or non-racialized heuristics.

This complicates the search for which type of minority is more or less likely to view

society in racialized terms and has both optimistic and pessimistic implications for social

cohesion.

Generating hypotheses about perceived discrimination in the UK from existing

literature

Racial discrimination is highly salient in the UK. The UK has a long history of

racial diversity (and racial inequality) dating back to the transatlantic slave trade. More

recently, non-whites from around the world immigrated to the UK in the decades after

World War II and often suffered integration difficulties due to their status as racial

minorities (Gilroy 1993; Phillips and Phillips 1998). There is also a long tradition in the

UK of using the concept of racial discrimination as a framework for understanding the

inequalities suffered by non-whites (as opposed to other European countries where

similar inequalities are framed as a function of culture, religion or national origin) (Bleich

2003; Givens and Case 2014). Yet, despite the salience of racial discrimination, non-

whites in the UK do not always view their lives through the lens of race. In fact, existing

literature generates three different predictions for why some minorities in the UK will be

more likely than others to use racial discrimination as a heuristic for interpreting events.

The first hypothesis focuses on social class. In some respects, this is an alternative

framework for understanding inequality because it is possible that some non-whites in the

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UK will interpret inequalities as the result of differences in socio-economic resources

(Miles 1982). In addition, longstanding debates about the interaction between race and

class suggest that poorly-educated and working class minorities are the most likely to

suffer from racial discrimination (Miles 1993; Rex and Tomlinson 1979; Sivanandan

1976). This imbalance in disadvantage should shape how different minorities view the

world (Chong and Kim 2006).

H1: Non-whites with lower socio-economic status will be more likely to cite

racial discrimination as a cause of inequality.

A second argument focuses on generational differences. Most racial minorities in

the UK have immigrant origins and when immigrants arrive they tend to interpret their

place in society through the lens of their foreign origins. In contrast, second-generation

non-whites who are born in the UK will inevitably be socialized into British norms and

social categories. As a result, second-generation non-whites will be more likely to

interpret life through the racial categories commonly used in the UK (Goulbourne 2009;

Paul 1997). This is consistent with research from across Europe and North America,

which finds that first-generation immigrants are often more positive and optimistic about

life in the new country, compared to the second generation which is more likely to be

disappointed, alienated, and sensitive to their vulnerability to discrimination (Maxwell

2010a, 2010b; Waters 1999).

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H2: Non-whites born in the UK will be more likely to cite racial discrimination as

a cause of inequality.

The third argument highlights differences among non-whites. In contemporary

Britain, there are many established non-white communities, the largest of which are

Bangladeshis, Caribbeans, Indians, Pakistanis and Sub-Saharan Africans.2 Some argue

that racial discrimination is of particular concern to black minorities (e.g. minorities with

origins in the Caribbean and Sub-Saharan Africa).3 Black minorities were the ones most

likely to be defined by ‘racial’ differences, because the concept of race dated back to the

transatlantic slave trade and the distinction between people of African and European

descent (Gilroy 1993). Moreover, the legislation and institutions that had been developed

to fight racial discrimination in Britain during the 1960s and 70s were focused on unfair

treatment of minorities due to their phenotype (Bleich 2003). Other non-whites faced

different integration challenges. Most notably, for South Asians the primary barrier has

been whether their religious needs would be respected by a British society constructed

around Christian norms (Modood 2010; Modood and Berthoud 1997).

H3: Black non-whites should be the most likely to cite racial discrimination as a

cause of inequality.

2These groups accounted for 66% of the non-white population in England and Wales according to the 2011 Census. There are also smaller non-white groups from across Asia and the Middle East (Office for National Statistics 2012). 3There was a brief period during the 1960s and 70s when British activists tried to unite all non-whites under the same ‘black’ label to maximize their political power, but by the 1990s that unified classification had failed (Modood 1988; Modood and Berthoud 1997).

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The societal context of perceived discrimination

The main theoretical proposition in this article is that individual-level attributes

matter less than context for predicting perceptions of discrimination. The main way

context has been used in previous studies of discrimination is to analyze how majority

individuals’ likelihood of discriminating against minorities varies according to the

demographic composition of their geographic environment (Oliver and Mendelberg

2000; Taylor 1998). Recent work has attempted to extend that approach and analyze

spatial variation in minorities’ perceptions of discrimination, but found no differences

across geographic contexts (Hopkins et al. 2013; McDermott 2011). In this article, I take

an alternate approach and analyze ‘societal contexts’, which are social institutions and

structures with different behavioral norms and ideological principles. For example, one

would expect different types of interpersonal interactions in a secondary school, on the

labor market, and in a political campaign. Therefore, one may also have different ways of

evaluating racial discrimination across societal contexts.

My approach builds on a range of work that examines how social spaces are rich

with symbolic meaning that can shape our attitudes and behavior (Blumer 1969;

Bourdieu 1989; Ross and Nisbett 1991), and that the meaning of these social spaces

depends on subjective interpretations (Crocker and Quinn 2000). In addition, my

approach is informed by work on the microfoundations of racism, which analyzes how

minorities receive subtle cues in different social spaces which must constantly be

evaluated for evidence of potential racism (Essed 1991). These insights suggest that

minorities may be more likely to perceive discrimination in some contexts as opposed to

others.

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H4: The relative importance of individual-level factors for predicting perceived

discrimination will vary according to the context.

H5: There is more variation in perceived discrimination across contexts than

across individual-level factors.

To predict the type of context in which minorities will be most likely to perceive

discrimination, I draw on the finding that when minorities are confronted with negative

outcomes and evidence of potential discrimination, they adopt numerous strategies to

preserve their self-esteem (Crocker and Major 1989; Ruggiero and Taylor 1997). When

minorities have less control over the outcome, they are more likely to use discrimination

to explain negative results and protect their self-esteem by not blaming themselves. But

when minorities have more control over the outcome they will avoid using discrimination

to explain negative results. This strategy protects minorities’ agency and allows them to

avoid being categorized in a lower social status (Crosby 1984; Kaiser and Miller 2001).

H6: Non-whites will be more likely to cite discrimination as a cause of racial

inequality in societal contexts where they have less control over the outcome.

Data

The data in this article are from a survey conducted February 24 – 27, 2012 by the

market research agency YouGovUK. At the time of the study, YouGov maintained a

panel of approximately 350,000 potential survey respondents in the UK. YouGov recruits

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panel members through standard (online and offline) advertising campaigns and

partnerships with a broad set of websites. Upon completion of a survey, respondents are

compensated with points that are redeemable for cash or prizes. As with any public

opinion survey, YouGov respondents have consciously decided to participate and

therefore may be more socially and civically engaged than the broader population or may

have views about discrimination that are not representative. However, research suggests

that internet samples drawn from large pools of potential respondents are at least as (if

not more) likely to represent the general population as standard telephone or face-to-face

surveys (Stephenson and Crête 2011).

For this article, YouGov drew a sample of 2,008 non-white respondents.4 The

sample was then weighted to be representative of the national non-white population of

adults 18 years old and older (including those without internet access) according to sex,

social class, region, party identity and newspaper readership.5 Appendix table 1 provides

descriptive statistics for weighted and unweighted data, which are similar on many basic

demographic variables. This suggests that the results in this article are unlikely to be

biased by underrepresented demographic sub-groups.

Measures

To measure perceived discrimination I provide a series of vignettes about racial

inequality and then ask for subjects’ opinions on the most important explanation for the

inequality. I do not prompt them in any way, and respondents are free to provide any

4YouGovUK uses a ‘turbo sampling’ method in which members of their panel are invited to participate in a survey and then assigned to a specific survey once they have accepted the invitation. The overall panel response rate is approximately 20%. Of those who started taking the survey, 10.5% did not complete the survey and were not included in the final sample. 5For more details on the polling methodology: http://yougov.co.uk/publicopinion/methodology/.

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explanation that they feel is relevant. The vignettes were presented one at a time, on

separate screens, and the full text is as follows:6

Recent studies suggest that many BME7 groups are less likely than whites in Britain to finish secondary school. In your opinion, what is the most important explanation for this finding?

Over the past few decades the unemployment rate for many BME groups has consistently been higher than for whites in Britain. In your opinion, what is the most important explanation for this dynamic? After the 2010 General Election, BMEs now represent 4% of the Members of Parliament but are roughly 11% of the United Kingdom population. In your opinion, what is the most important explanation for this underrepresentation?

In recent years, many BME artists have complained that they do not receive the same funding or publicity opportunities as white artists. In your opinion, what is the most important explanation for this dynamic?

In your opinion, what is the most important explanation for why there are few BME CEOs of large corporations in Britain?

These contexts were selected to test H6 by providing variation in the extent of

minority control over the outcomes. Minorities have the most control (and therefore

should have the least perceived discrimination) in secondary education where the

outcomes are largely driven by meritocratic testing and there is no limit on the number of

people who can succeed. Minorities have the least control (and therefore should have the

most perceived discrimination) in politics, arts and business because while all three

outcomes involve elements of merit they are also highly competitive and dependent on

networks and luck. Unemployment is in-between. It is not as tightly linked to merit and

performance as secondary school graduation, but it is also not randomly distributed

across the population and one can exert some control over the likelihood of finding a job.

6The vignettes were consistently presented in this order. 7‘BME’ stands for black minority ethnic, a common British term for non-whites.

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I code responses to the open-ended questions into three general categories: racial

discrimination, blaming minorities, and socio-economic status. I also code whether

responses are a mixture of these categories, whether they pose an ‘other’ explanation, or

whether they should be classified as ‘don’t know’. I limit the coding to three main

substantive categories because they were the most common responses and the most

general way of organizing the responses.8 The coding scheme is summarized in table 1

and produces twelve response categories. Full details on coding for the independent

variables can be found in the appendix.

‘Table 1 about here’

Perceived discrimination across individuals and societal contexts

To evaluate H1-H5, table 2 presents multinomial logistic regression models that

estimate the likelihood of explaining racial inequality in each of the societal contexts by

either racial discrimination, socio-economic factors, blaming minorities, a mixture of the

explanations, or ‘other’/‘don’t know’.9 For each model the baseline category is

‘Discrimination’, so the coefficients indicate the likelihood that respondents choose the

respective classification as opposed to discrimination. In an ideal world I would estimate

8Most of the ‘other’ responses were idiosyncratic and could not be grouped into a category. All coding was done by the author. All responses were coded twice for reliability. Automated coding was inappropriate because computers can code for subject matter but not meaning and cannot distinguish between ‘it’s all about race’ and ‘it’s definitely not about race’. 9A potential concern with multinomial logistic regression is the independence of irrelevant alternatives (IIA) assumption, which requires that the odds of choosing between two response options do not depend on the presence or absence of the other response options. There are several statistical tests (e.g. the Hausman-McFadden test and the Small-Hsaio test) that attempt to test whether the IIA assumption has been violated. Unfortunately, research suggests that these tests are unreliable and should be avoided by applied researchers (Cheng and Long 2007). In this case, there are reasons to believe that the IIA assumption was not violated because my survey respondents did not have a formal choice among concrete response options. Although there is no way of knowing exactly what came into each subject’s head before answering, a reasonable assumption would be that each subject (consciously or subconsciously) surveyed the range of possible answers before responding.

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models that account for each of the twelve classifications in my coding scheme.

However, that would require a significantly larger sample size to have enough cases in

each response option for all values of each covariate. Given that constraint, I select these

five categories as the most substantively meaningful.

‘Table 2 about here’

The results in table 2 support for H1 because having no educational qualifications

is consistently associated with being more likely to cite discrimination as opposed to a

mixture of explanations for inequality. In addition, having a higher education degree is

associated with being more likely to cite a mixture of explanations as opposed to

discrimination in four of the five societal contexts. There is limited support for H2 as

being born in the UK is consistently associated with being more likely to cite

discrimination as opposed to blaming minorities. However, for neither H1 nor H2 is the

predicted relationship consistent across each of the coding category comparisons. There

is very little support for H3, as the relationship between being black and citing

discrimination is uneven across contexts and categorical comparisons. Overall, the most

striking pattern in table 2 is that no covariate has a consistent relationship with perceived

discrimination across all contexts and each categorical comparison. This supports H4, as

it suggests that the individual-level factors associated with perceived discrimination vary

across contexts.

Across the models in table 2, few covariates are statistically significant at

conventional levels and the r-squared statistics are low. This suggests that the standard

explanations for perceived discrimination are not good predictors of whether non-whites

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interpret racial inequality through the lens of discrimination. I now turn to H5 and ask

whether societal context is a better predictor of perceived discrimination.

The first evidence supporting H5 is the fact that very few individuals interpret

inequality the same way in each societal context. Less than one percent of non-whites

give the same explanation for each of the five examples of inequality. Even if I collapse

the coding scheme to six options (Only discrimination, Only blame, Only economic

difficulties, a Mixture, Other, Do not know), only two percent of respondents answer the

same way across each question.

For a formal comparison of individual and contextual factors, I estimate a series

of multilevel logistic regression models without any covariates and in which the variation

in citing discrimination is decomposed across the five questions and across individuals. I

expand the dataset to create five observations (one for each open-ended question) per

respondent. The likelihood of citing discrimination is the outcome variable, the question-

level grouping variables are categorical variables with five values corresponding to the

values for each of the five inequality questions, and the individual-level grouping

variable is the unique respondent identification code.

Table 3 summarizes results from the multilevel models and indicates that when

predicting the likelihood of citing only discrimination as opposed to all other responses,

80 percent of the variation is across individuals and 20 percent is across contexts. The

next column indicates the exact same results for a model predicting the likelihood of

citing any discrimination. However, when I exclude those who answered ‘Do not know’

and estimate models only among respondents who offered an interpretation of the

inequality, roughly two-thirds of the variation is across individuals and one-third is across

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contexts. The final column limits the analysis to those who answered either

Discrimination only or Blame only and here the variation is split evenly across

individuals and across contexts. These results suggest that among non-whites who are

able to provide a heuristic for interpreting inequality, much may depend on the context.

The results in table 3 do not support H5 as there is more variation in perceived

discrimination across individuals than contexts for most specifications. Yet, the models

summarized in table 3 decompose variation across all individual level characteristics,

including those which are unobserved, poorly understood by the literature, and random

noise. A more focused test of H5 would compare the importance of contexts with the key

independent variables cited in H1, H2, and H3. For this test, I again estimate logistic

regression models on the expanded dataset. I include individual-level covariates as well

as dummy variables for four of the five open-ended questions about inequality. Full

results are in appendix table 2, but the relationship between each covariate and the

likelihood of citing discrimination is summarized with a graph of average marginal

effects in figure 1.

‘Figure 1 about here’

Figure 1 provides strong evidence in favor of H5, as the average marginal effects

are much larger for the question variables than they are for the individual-level

characteristics. In the top graph, the average marginal effect for the inequality questions

ranges from 0.13 to 0.26, while the only effect for the individual covariates that is

statistically significant at the 5 percent level is 0.09 for having no qualifications.

Similarly, in the graph for responses that have any mention of discrimination, the average

marginal effect for the inequality questions spans from 0.11 to 0.26 while the largest

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effect for an individual-level covariate is 0.09 for having no qualifications. In summary,

results thus far offer limited support for H1, H2, or H3 but provide stronger support for

H4 and H5. Societal contexts may be at least as important as all individual-level factors,

but they are much more important than the individual-level variables cited in existing

literature as explanations for why minorities are more or less likely to view life through

the lens of racial discrimination.

Variation in perceived discrimination across societal contexts

Figure 2 explores my hypothesis (H6) about the contexts in which non-whites are

more likely to perceive discrimination with graphs of the percentage of respondents who

mentioned discrimination as either the only explanation or as one of multiple

explanations for inequality.10 In each graph the pattern is the same. Discrimination is

more likely to be cited as an explanation for inequality among CEOs, artists and

politicians than as an explanation for educational inequality. This supports H6 as it

suggests non-whites are more likely to perceive discrimination in contexts where they

have less control over the outcomes. Moreover, appendix figure 1 indicates that ‘Blaming

minorities’ is a more common response to the question about educational inequality than

the other four contexts. This is also consistent with the logic behind H6, as minorities

should be more likely to emphasize their own responsibility (as opposed to

discrimination) in contexts like education where they have more control over the

outcome.

‘Figure 2 about here’

10Full results with the percentage of respondents in each of the twelve coding categories for each of the five contexts are in appendix figure 1.

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A result from figure 2 that is inconsistent with H6 is the fact that discrimination is

more likely to be cited as an explanation for employment inequality than for political

inequality and it is mentioned by roughly the same percentage of respondents to explain

employment and arts inequality. I expected fewer mentions for employment inequality

because I claimed that minorities have more control over employment outcomes than

political and art outcomes. However, it is possible that non-whites feel less control over

employment outcomes than I anticipated. Alternatively, another form of variation across

societal contexts may be more important. For example, public debates about anti-

discrimination strategies in the UK have focused on the labor market and politics but not

as much on education and this may influence how non-whites interpret inequalities in the

different contexts (Givens and Case 2014).11 Future research should explore these

possibilities more closely.

Robustness checks

To ensure that my results are not dependent on coding decisions, I explore

alternate specifications of the independent variables. In the main analysis I coded mixed-

race respondents as ‘black’ because mixed-race individuals in Britain are likely to be

treated by society as black and are vulnerable to racial discrimination (Song 2003). Yet,

mixed-race individuals may be less sensitive to racial issues than other blacks, so I

conduct additional analyses with an operationalization of ‘black’ that excludes all mixed

individuals. A second issue is that socialization into the use of British racial categories

may occur within generations as immigrants spend more time in the UK. Therefore I

conduct additional analyses among non-whites born abroad and include a control variable

11I would like to thank one of the anonymous reviewers for suggesting this possibility.

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for how long respondents have been in the UK. Finally, in the previous models I did not

account for interactions between non-white subgroups and socio-economic outcomes or

generational status. This may be an oversight because being black and having no

qualifications or between being black and being born in the UK may be more important

for perceived discrimination than merely being black, having no qualifications, or being

born in the UK (Modood and Berthoud 1997).

I use the expanded datasets to estimate a series of logistic regression models with

the alternate specifications. I estimate models predicting the likelihood of citing only

discrimination and models predicting the likelihood of citing any discrimination.12 Full

results are in appendix table 3, but the main finding is that none of these alternate

specifications challenge my earlier findings. Neither the narrower definition of black nor

the amount of time spent in the UK among first-generation immigrants is a statistically

significant predictor of perceived discrimination. For the interaction terms, there is no

statistically significant relationship between being black, having no educational

qualifications, and perceiving discrimination. There is a relationship between being

black, being born in the UK, and perceiving discrimination, although it is not the

expected relationship. For both the ‘discrimination only’ and ‘any discrimination’

specifications, blacks born in the UK are less likely to cite discrimination than blacks

born abroad, non-blacks born in the UK, or non-blacks born abroad. However, the

differences among these groups in the predicted probabilities of citing discrimination are

minor (less than five percentage points), suggesting there is not much substantive

12These models are a useful overview of relationships with perceived discrimination but results are similar when I estimate models predicting the likelihood of citing discrimination in each of the five contexts.

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significance to the relationship. In short, my earlier findings are robust to several

alternate specifications.13

Another concern is that although I provide all subjects with the same stimuli in an

effort to obtain a common benchmark, individuals may have different experiences with

discrimination that influence how they respond to the questions about inequality. For

example, the relationship between having no qualifications and being more likely to

interpret inequality as discrimination may reflect the fact that individuals with no

qualifications are more likely to have experienced discrimination. To test for this

possibility, I use a survey item that asks how frequently subjects experience

discrimination. I then re-estimate the models from table 2 and appendix table 2 to see if

the relationships between individual-level covariates and perceived discrimination

become more consistent after controlling for self-reported exposure to discrimination.14

Full results from the models with an additional covariate for self-reported

discrimination are in appendix tables 4 and 5, and they suggest that the results are similar

to models without a covariate for personal discrimination. In all models, all of the

previously documented relationships are the same. In the multinomial logistic regression

models, having more personal experience with discrimination is mostly associated with

being less likely to cite discrimination as an explanation for inequality, although the

relationships are generally not statistically significant at the 10 percent level. In the

logistic regression models using the expanded dataset, having more experience with

13I also explored the possibility of gender differences in perceived discrimination. Non-white women in the sample are generally more likely than men to cite discrimination as an explanation for inequality, although the differences are fairly small and only statistically significant at the 10 percent level for the question about education. Moreover, the inclusion of a control variable for gender does not change any of the results presented in this article. 14In an ideal world I would also have controlled for variation in personal experiences with discrimination across the five societal contexts, but those questions were not included in the survey.

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discrimination is also associated with being less likely to mention discrimination as an

explanation for inequality, and in this case the relationships are statistically significant at

the 1 percent level.

These findings are counterintuitive as one might have expected individuals who

report more experience with discrimination to be more likely to interpret inequality

through the lens of discrimination. One possibility is that non-whites’ views on

discrimination in their personal lives and in public are two distinct dynamics. However,

one should not over-interpret results based on the closed-ended question about

discrimination. As discussed earlier, closed-ended questions that explicitly ask about

discrimination measure multiple dynamics that are difficult to disentangle. Nonetheless,

at a minimum these robustness checks suggest that the results in this article are not biased

by variation in personal experiences with discrimination.

The final area of concern is survey effects. Open-ended questions are more

challenging than closed-ended questions because they require subjects to formulate their

own response. As a result, subjects are more likely to not answer open-ended questions

(Geer 1988). As seen in appendix figure 1, ‘Do not know’ ranges from 27 to 45 percent

across the five questions about inequality and it is the model response for each question.

In comparison, ‘Do not know’ was less than 8 percent of responses to the closed-ended

question about discrimination. In some respects, these greater cognitive demands are

appropriate for my study because they mirror the real-world challenge of appraising a

situation and determining whether it involves discrimination (Essed 1991). However,

there are two ways in which the cognitive demands of open-ended questions may bias my

results.

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One possibility is that variation in perceived discrimination across individuals is

primarily about who is sufficiently cognitively engaged to answer the questions. This

could depress the relationships between other individual-level covariates and perceived

discrimination in the overall sample. To explore this possibility, I re-estimated the models

in table 2 only among non-whites who did not answer ‘Do not know’ for each question

(i.e. the most cognitively engaged sub-set of the sample) and the results were the same as

in table 2. In addition, if cognitive demands were the main factor driving ‘Do not know’

responses (as opposed to ambivalence or indecision) we would expect a large and

consistent amount of ‘Do not know’ responses to all five questions because people who

are vulnerable to the cognitive demands should immediately opt out of all open-ended

questions. Yet, while seventy percent of the sample answered ‘Do not know’ at least

once, less than five percent of the sample answered ‘Do not know’ to all five questions.

Finally, if I use educational attainment as a proxy for cognitive sophistication it is not a

statistically significant predictor at the 10 percent level of responding ‘Do not know’ for

any of the open-ended questions.15

The other potential survey effect is that variation in perceived discrimination

across contexts may not be due to the contexts themselves but rather the effect of

answering a series of challenging questions. This could lead to increasing numbers of ‘Do

not know’ responses as subjects progressed through the five questions and became more

vulnerable to the cognitive demands. Or, it could lead to decreasing numbers of ‘Do not

know’ responses as subjects became more habituated to the challenge and better able to

15This is based on a series of logistic regressions predicting the likelihood of answers being coded ‘Do not know’ as opposed to all other categories. I model education in three different ways. One is as the dummy variable: Higher education degree/No higher education degree. A second is as the dummy variable: Some higher education/No higher education. The third is: No qualifications/Some qualifications.

22

answer. Future work should randomize the order of open-ended questions to fully

mitigate this concern, but my results do not indicate that response patterns changed

consistently across questions. As seen in appendix figure 1, the number of ‘Do not

know’ responses is fairly constant across the first three questions, peaks at the fourth and

then drops for the final question.

Conclusion

This article offers a new way of studying perceived discrimination. I posit that

the standard practice of explicitly asking minorities about discrimination is likely to

produce inconclusive and misleading results because it cannot distinguish between the

extent to which minorities are responding based on their actual experiences with

discrimination or their levels of sensitivity to the issue of discrimination. To address this

concern, I focus on the latter dynamic and provide a series of open-ended questions that

measure the use of racial discrimination as a heuristic for understanding society.

My results break new theoretical ground by suggesting that the individual-level

variables cited in the existing perceived discrimination literature are less important than

variation across contexts. Furthermore, I argue that non-whites are more likely to cite

discrimination as a cause of racial inequality in societal contexts where they have less

control over the outcome. This builds on literature that connects minorities’ perceptions

of discrimination to strategies for preserving self-esteem.

A major implication of this article is that societal contexts are underappreciated

predictors of how minorities interpret events. For better or worse, this does not provide

easy answers for concerns about social cohesion in the UK and elsewhere. This article

23

suggests that all minorities – irrespective of socio-economic status, generational status, or

the particular non-white group – have the capacity to view life through racialized or non-

racialized heuristics. This raises new challenges, but the extent that it is an accurate

representation of how perceived discrimination operates, it is a useful contribution.

24

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Figures and Tables

Figure 1: Average marginal effects for the likelihood of citing discrimination as an explanation of inequality (expanded dataset with five observations per respondent)

Q.EmployQ.MPsQ.Arts

Q.CEOsHigher edNo quals

Prof.Manual

Born UKBlack

-.1 0 .1 .2 .3

Discrimination only

Q.EmployQ.MPsQ.Arts

Q.CEOsHigher edNo quals

Prof.Manual

Born UKBlack

-.1 0 .1 .2 .3

Any discrimination

Note: Results are calculated from models in appendix table 2.

The omitted question is about educational inequality. Bars are surrounded by 95% confidence intervals.

30

Figure 2: Percentage of non-white respondents citing discrimination as an explanation for racial inequality

Note: The graphs plot responses to open-ended questions with the percentage of

respondents who either only cited discrimination or who cited discrimination as well as another explanation for the five different kinds of inequality.

31

Table 1: Coding scheme for responses to the open-ended questions about explanations for racial inequality

Single-factor Mixed-factors Other Racial discrimination Discrimination & Economic Other

Economic factors Discrimination & Blame Do not know Blame minorities Economic & Blame

Disc/Econ/Blame Discrimination & Other Economic & Other Blame & Other

32

Table 2: Multinomial logistic regression results for responses to open-ended questions about inequality

Education Unemployment MPs Arts CEOs Socio-

economic Higher education -.646 (.261) ** .297 (.404) .208 (.289) .251 (1.14) .363 (.460) No qualifications -1.46 (1.17) -.362 (1.09) .209 (.712) -9.96 (1.31) *** .747 (1.13)

Professional .589 (.263) ** -.264 (.400) -.664 (.324) ** 1.27 (1.43) -.647 (.477) Manual .991 (.471) ** -.040 (.575) .240 (.395) 1.55 (1.37) -.721 (.778)

Born in UK -.039 (.233) .229 (.346) -.170 (.259) -.124 (1.15) .214 (.413) Black -.080 (.265) -.213 (.374) .430 (.261) .002 (1.20) .131 (.409)

Constant .045 (.259) -2.55 (.455) ** -1.47 (.305) -5.89 (.164) *** -3.18 (.521) ***

Blame minorities

Higher education .052 (.215) -.015 (.186) .067 (.175) -.066 (.200) -.116 (.220) No qualifications .164 (.704) -.533 (.555) -.629 (.562) -.466 (.599) -1.52 (1.05)

Professional .088 (.215) .007 (.187) -.258 (.175) .028 (.202) -.106 (.233) Manual .990 (.393) ** -.115 (.300) .289 (.268) .059 (.280) .260 (.314)

Born in UK -.049 (.182) -.137 (.166) -.254 (.152) * -.038 (.176) -.001 (.202) Black -.044 (.203) .149 (.182) -.092 (.170) .148 (.193) .136 (.218)

Constant 1.03 (.210) *** -.576 (.194) *** .328 (.174) * -.963 (.208) -1.42 (.231) ***

Mixed Higher education .078 (.222) .172 (.152) .095 (.200) .018 (.212) -.015 (.152) No qualifications -.235 (.745) -.979 (.491) ** -.503 (.580) -2.20 (1.05) ** -.563 (.489)

Professional .276 (.222) .065 (.153) -.154 (.196) .079 (.211) -.106 (.153) Manual 1.38 (.394) *** .304 (.216) .434 (.291) .020 (.292) -.136 (.220)

Born in UK .169 (.189) .122 (.131) -.242 (.171) .074 (.183) .205 (.131) Black .338 (.205)* -.009 (.146) -.074 (.190) -.029 (.200) -.023 (.145)

Constant .472 (.224) ** .004 (.156) -.167 (.203) -1.07 (.214) *** -.313 (.150) **

Other/ Don’t Know

Higher education -.244 (.209) .050 (.148) -.094 (.156) -.168 (.133) -.052 (.134) No qualifications -.225 (.689) -.606 (.413) -.704 (.452) -.657 (.374) * -.155 (.400)

Professional .213 (.216) -.038 (.150) -.089 (.154) .031 (.135) -.073 (.134) Manual .958 (.387) ** .142 (.213) .354 (.238) -.039 (.189) .069 (.189)

Born in UK .125 (.179) .049 (.128) -.103 (.134) -.074 (.113) .110 (.115) Black .081 (.196) -.002 (.142) -.025 (.146) .059 (.126) -.040 (.126)

Constant 1.31 (.210) *** .296 (.147) 1.02 (.160) *** .899 (.129) .284 (.135) N 2008 2008 2008 2008 2008 Pseudo R2 0.01 0.00 0.00 0.00 0.00

Weighted data Cells provide coefficients with robust standard errors in parentheses

*=p<.10, **=p<.05, ***=p<.01 Baseline category is ‘Discrimination only’

33

Table 3: Individual-level and question-level variation in responses Disc. only Any disc. Disc. only

(w/o dk) Any disc. (w/o dk)

Disc. vs. Blame

Individual 80% 80% 62% 64% 51% Question 20% 20% 38% 36% 49% Note: Results are calculated from multilevel logistic regression models with variation

across individuals and across questions specified as random effects. The first two columns include all respondents. The third and fourth columns exclude respondents who answered with a form of ‘I do not know’. The fifth column includes only respondents who answered Racial discrimination only or Blame minorities only.

34

Online Appendix Dependent variable: “Recent studies suggest that many BME groups are less likely than whites in Britain to finish secondary school. In your opinion, what is the most important explanation for this finding?” “Over the past few decades the unemployment rate for many BME groups has consistently been higher than for whites in Britain. In your opinion, what is the most important explanation for this dynamic?” “After the 2010 General Election, BMEs now represent 4% of the Members of Parliament but are roughly 11% of the United Kingdom population. In your opinion, what is the most important explanation for this underrepresentation?” “In recent years, many BME artists have complained that they do not receive the same funding or publicity opportunities as white artists. In your opinion, what is the most important explanation for this dynamic?” “In your opinion, what is the most important explanation for why there are few BME CEOs of large corporation in Britain?” Independent variables Education: What is the highest educational or work-related qualification you have?

0- No formal qualifications 1- Youth training certificate/skillseekers 2- Recognized trade apprenticeship completed 3- Clerical and commercial 4- City and Guild certificate 5- City and Guild certificate - advanced 6- ONC 7- CSE grades 2-5 8- CSE grade 1, GCE O level, GCSE, School Certificate 9- Scottish Ordinary/ Lower Certificate 10- GCE A level or Higher Certificate 11- Scottish Higher Certificate 12- Nursing qualification (eg SEN, SRN, SCM, RGN) 13- Teaching qualification (not degree) 14- University diploma 15- University or CNAA first degree (eg BA, B.Sc, B.Ed) 16- University or CNAA higher degree (eg M.Sc, Ph.D)

35

17- Other technical, professional or higher qualification 18- Don't know 19- Prefer not to say

Higher education (14, 15, 16, 17) No qualifications (0)

Current Occupation:

0- Professional or higher technical work 1- Manager or Senior Administrator 2- Clerical 3- Sales or Services 4- Foreman or Supervisor of Other Workers 5- Skilled Manual Work 6- Semi-Skilled or Unskilled Manual Work 7- Other 8- Have never worked

Professional (0) Manual (5, 6) Place of birth: 0 – Not in the UK, 1 – UK Race/ethnicity:

0- White and Black Caribbean 1- White and Black African 2- White and Asian 3- Any other mixed background 4- Indian 5- Pakistani 6- Bangladeshi 7- Any other Asian background 8- Black Caribbean 9- Black African 10- Any other black background 11- Chinese 12- Other ethnic group [open]

Black (0, 1, 8, 9, 10) Personal experiences with discrimination: “How often, if at all, would you say you experience discrimination in Britain?”

0 – Rarely/not very much, 1 – Some of the time, 2 – Often

36

Appendix Figure 1: Responses to open-ended questions about the most important explanation for various forms of racial inequality in Britain

Note: Weighted data.

The percentage of non-whites coded in each of the twelve response categories for each of the five questions about racial inequality.

‘E’ – Education, ‘U’ – Unemployment, ‘P’ – Politicians, ‘A’ – Arts funding, ‘C’ - CEOs

37

Appendix Table 1: Descriptive Statistics – Percentage of non-whites in various demographic categories

Weighted sample Unweighted sample Mean SD Min Max Mean SD Min Max

Age 38.4 14.2 18 83 38.6 13.6 18 83 Sex 0.52 0.50 0 1 0.51 0.50 0 1

Born in UK 0.55 0.50 0 1 0.56 0.50 0 1 Higher ed. 0.64 0.48 0 1 0.66 0.47 0 1 No quals. 0.02 0.14 0 1 0.02 0.14 0 1

Professional 0.29 0.46 0 1 0.31 0.46 0 1 Man. laborer 0.11 0.31 0 1 0.11 0.31 0 1

Mixed 0.12 0.32 0 1 0.13 0.34 0 1 Black 0.29 0.45 0 1 0.28 0.45 0 1

Black Caribbean 0.12 0.32 0 1 0.11 0.31 0 1 Black African 0.12 0.32 0 1 0.12 0.32 0 1 South Asian 0.48 0.50 0 1 0.47 0.50 0 1

Indian 0.27 0.45 0 1 0.27 0.44 0 1 Pakistani 0.12 0.32 0 1 0.11 0.32 0 1

Bangladeshi 0.05 0.22 0 1 0.05 0.22 0 1 Note: Sex: 0-Male, 1-Female.

The composite categories ‘Black’ and ‘South Asian’ include respondents with ‘Mixed’ ethnicity.

38

Appendix Table 2: Logistic regression results for the likelihood of citing discrimination as an explanation of inequality (expanded dataset with five observations per respondent)

Discrimination only Any discrimination Unemployment question 1.16 (.098) *** 1.03 (.078) ***

MPs question .822 (.101) *** .525 (.082) *** Arts question 1.29 (.097) *** .881 (.079) ***

CEOs question 1.48 (.096) *** 1.27 (.078) *** Higher education .033 (.062) .074 (.054) No qualifications .545 (.177) *** .425 (.161) ***

Professional .025 (.062) .083 (.054) Manual -.175 (.089) ** -.068 (.077)

Born in UK -.010 (.053) .010 (.047) Black -.019 (.058) -.015 (.051)

Constant -2.36 (.099) *** -1.66 (.078) *** N 10,040 10,040

Pseudo R2 0.04 0.03 Weighted data

Cells provide coefficients with robust standard errors in parentheses *=p<.10, **=p<.05, ***=p<.01

The omitted question is about educational inequality

39

Appendix Table 3: Robustness checks: Logistic regression results for the likelihood of citing discrimination as an explanation of inequality (expanded dataset with five

observations per respondent) with covariates for non-mixed black respondents, time spent in the United Kingdom, and interactions between black and no qualifications and black

and being born in the UK Discrimination only Any discrimination I II III IV V VI VII VIII

Black (excluding mixed) -.037 (.060)

-.035 (.053)

Time since arrival in UK .002 (.003)

.005* (.003)

Black (including mixed) -.026 (.057)

.157* (.085)

-.020 (.050)

.121 (.075)

No qualifications .420** (.183)

.255 (.167)

Born in the UK .079 (.061)

.067 (.053)

Black (including mixed) * no qualifications

.346 (.505)

.563 (.465)

Black (including mixed) * Born in the UK

-.336*** (.114)

-.248** (.101)

Constant -1.31*** (.029)

-1.34*** (.078)

-1.32*** (.031)

-1.35*** (.045)

-.795*** (.026)

-.902*** (.070)

-.805*** (.027)

-.835*** (.040)

N 10,040 4,420 10,040 10,040 10,040 4,420 10,040 10,040 Pseudo R2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Weighted data Cells provide coefficients with robust standard errors in parentheses

*=p<.10, **=p<.05, ***=p<.01

40

Appendix Table 4: Robustness checks: Multinomial logistic regression results for responses to open-ended questions about inequality with additional covariate for prior

exposure to discrimination Education Unemployment MPs Arts CEOs

Socio-economic

Higher education -.672 (.271) ** .274 (.402) .199 (.289) .285 (1.09) .338 (.464) No qualifications -1.51 (1.17) -.413 (1.08) .194 (.712) -9.48 (1.24) *** .696 (1.12)

Professional .572 (.263) ** -.281 (.403) -.670 (.323) ** 1.28 (1.43) -.669 (.479) Manual .995 (.472) ** -.042 (.576) .238 (.395) 1.56 (1.36) -.730 (.786)

Born in UK -.055 (.233) .215 (.347) -.175 (.261) -.107 (1.14) .199 (.412) Black -.112 (.264) -.243 (.371) .420 (.261) .044 (1.20) .099 (.420)

Personal disc. .274 (.157) * .251 (.212) .086 (.150) -.211 (.283) .289 (.245) Constant -.160 (.282) -2.75 (.493) *** -1.53 (.315) *** -5.77 (1.69) *** -3.40 (.527) ***

Blame

minorities Higher education .042 (.214) -.020 (.186) .058 (.176) -.065 (.200) -.127 (.221) No qualifications .147 (.702) -.543 (.556) -.646 (.558) -.464 (.559) -1.55 (.105)

Professional .080 (.216) .004 (.187) -.265 (.175) .029 (.202) -.113 (.234) Manual .990 (.393) ** -.115 (.300) .288 (.268) .060 (.279) .259 (.314)

Born in UK -.056 (.182) -.140 (.167) -.260 (.151) * -.037 (.178) -.008 (.201) Black -.057 (.203) .142 (.182) -.103 (.170) .150 (.194) .122 (.201)

Personal disc. .117 (.125) .051 (.109) .090 (.100) -.013 (.119) .109 (.133) Constant .951 (.232) *** -.613 (.210) ** .260 (.191) -.953 (.226) *** -1.50 (.252) ***

Mixed Higher education .062 (.222) .165 (.153) .090 (.201) .020 (.215) -.031 (.152)

No qualifications -.263 (.743) -.992 (.492) ** -.513 (.581) -2.20 (1.06) ** -.595 (.492) Professional .264 (.223) .060 (.153) -.158 (.196) .080 (.211) -.117 (.153)

Manual 1.38 (.393) *** .303 (.217) .433 (.291) .019 (.292) -.137 (.221) Born in UK .159 (.189) .118 (.132) -.245 (.171) .075 (.184) .195 (.131)

Black .317 (.204) -.017 (.146) -.080 (.190) -.026 (.200) -.044 (.145) Personal disc. .171 (.130) .070 (.085) .051 (.111) -.020 (.125) .165 (.086) *

Constant .349 (.245) -.048 (.162) -.205 (.218) -1.06 (.221) *** -.435 (.166) ***

Other/ Don’t Know

Higher education -.261 (.209) .040 (.148) -.098 (.156) -.180 (.132) -.064 (.134) No qualifications -.254 (.692) -.626 (.411) -.712 (.452) -.680 (.373) * -.179 (.400)

Professional .201 (.212) -.045 (.150) -.093 (.154) .023 (.135) -.082 (.134) Manual .959 (.387) ** .142 (.212) .353 (.283) -.040 (.188) .068 (.189)

Born in UK .115 (.179) .043 (.128) -.106 (.134) -.081 (.113) .103 (.115) Black .060 (.197) -.015 (.142) -.030 (.147) .045 (.126) -.057 (.126)

Personal disc. .179 (.122) .101 (.083) .045 (.086) .114 (.073) .126 (.075) * Constant 1.18 (.231) *** .221 (.158) .991 (.173) *** .814 (.141) *** .193 (.145)

N 2008 2008 2008 2008 2008 Pseudo R2 0.01 0.00 0.00 0.00 0.00

Weighted data Cells provide coefficients with robust standard errors in parentheses

*=p<.10, **=p<.05, ***=p<.01 Baseline category is ‘Discrimination only’

41

Appendix Table 5: Robustness checks: Logistic regression results for the likelihood of citing discrimination as an explanation of inequality (expanded dataset with five

observations per respondent) with additional covariate for prior exposure to discrimination

Discrimination only Any discrimination Unemployment question 1.16 (.098) *** 1.03 (.078) ***

MPs question .823 (.101) *** .525 (.082) *** Arts question 1.29 (.097) *** .882 (.079) ***

CEOs question 1.48 (.096) *** 1.27 (.078) *** Higher education .043 (.062) .084 (.054) No qualifications .564 (.177) *** .442 (.161) ***

Professional .032 (.062) .089 (.054) * Manual -.175 (.089) *** -.068 (.077)

Born in UK -.004 (.003) .015 (.047) Black -.006 (.058) -.003 (.051)

Personal discrimination -.101 (.034) *** -.092 (.031) *** Constant -2.28 (.103) *** -1.59 (.081) ***

N 10,040 10,040 Pseudo R2 0.04 0.03

Weighted data Cells provide coefficients with robust standard errors in parentheses

*=p<.10, **=p<.05, ***=p<.01 The omitted question is about educational inequality

42