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Year: 2020

What factors explain anti-Muslim prejudice? An assessment of the effects ofMuslim population size, institutional characteristics and immigration-related
media claims
Schlueter, Elmar ; Masso, Anu ; Davidov, Eldad

DOI: https://doi.org/10.1080/1369183X.2018.1550160

Posted at the Zurich Open Repository and Archive, University of Zurich
ZORA URL: https://doi.org/10.5167/uzh-168916
Journal Article
Accepted Version
Originally published at:
Schlueter, Elmar; Masso, Anu; Davidov, Eldad (2020). What factors explain anti-Muslim prejudice? An assessment of the effects of Muslim population size, institutional characteristics and immigration-related media claims.
Journal of Ethnic and Migration Studies, 46(3):649-664.
DOI: https://doi.org/10.1080/1369183X.2018.1550160

1What factors explain anti-Muslim prejudice? An assessment of the effects ofMuslim population size, institutional characteristics and immigrationrelated media claims

Elmar Schlueter*, Justus Liebig University, Giessen
Anu Masso, ETH, Zurich
Eldad Davidov, University of Cologne and University of ZurichThis is an electronic version of an article published online in Journal of Ethnic andMigration Studies on 22-Feb-2019. This journal is available online at: www.tandfonline.com.
The definitive publisher-authenticated version of this article is available online underhttps://doi.org/10.1080/1369183X.2018.1550160

Acknowledgements
The first two authors contributed equally to the article; order of authorship was decided by the
toss of a coin. Anu Masso would like to thank the European Commission (Marie Curie
626601) and the Estonian Research Council (IUT20-38) for financing her research. The third
author would like to thank the University of Zurich Research Priority Programme “Social
Networks” for their support. The authors would like to thank Lisa Trierweiler for the English
proof of the manuscript.

Bios

2

Elmar Schlueter is professor of sociology at the Justus Liebig University, Giessen. His main
research interests focus on interethnic relations and immigrant integration, often coupled withmethodological applications of multilevel and structural equation modeling.
Anu Masso is a senior researcher at the Institute of Social Studies at the University of Tartu
and a visiting researcher at the Chair of Social Networks at the ETH in Zürich. Her research
focuses on social transformations through the prism of spatial mobility, such as mobility
practices (e.g., activity spaces), mobility attitudes (e.g., the role of media in constructing
immigration attitudes) and media mobility (e.g., the role of smartphones in constructing
perceptions of social space and time). She has a special interest in research methods in thesocial sciences.
Eldad Davidov is Professor at the Institute for Sociology and Social Psychology at the
University of Cologne, Germany, and the Department of Sociology at the University of
Zurich, Switzerland, and co-director of the University of Zurich Research Priority Program“Social Networks”. He was president of the European Survey Research Association (ESRA)
between 2015 and 2017. His research interests are applications of structural equation
modeling to survey data, especially in cross-cultural and longitudinal research. Applications
include human values, national identity, and attitudes toward immigrants and other minorities.

3

What factors explain anti-Muslim prejudice? A comparative assessment of
Muslim population size, institutional characteristics and immigrationrelated media claims

Abstract
What factors explain majority members’ anti-Muslim prejudice? This is an increasingly
important question to ask, but to date only relatively few studies have sought to provide
answers from a cross-national comparative perspective. This study aims to help fill this gap.
Using data from the 7th round of the European Social Survey (ESS) linked with country-level
characteristics, our results indicate that (a) a larger Muslim population size, (b) more liberalimmigrant integration policies and (c) greater state support of religion are all associated withlower levels of majority members’ negative attitudes towards Muslim immigration – ourindicator of anti-Muslim prejudice. Such attitudes, however, prove to be unrelated to (d)cross-national differences in the frequency of negative immigration-related news reports asmeasured by the ESS media claims data. Collectively, these findings bring us one important
step closer towards a better understanding of interethnic relations between majority members
and Muslim immigrants in European host societies.

Keywords:
Anti-Muslim prejudice; immigrant integration policies (MIPEX); state support of religion;group threat theory; European Social Survey (ESS)4

Introduction
A constant increase in immigration has been one of the most crucial structural social changes
that European countries have undergone during the last decades. At the same time, negative
attitudes towards immigrants and immigration have also been on the rise (Semyonov,Raijman, and Gorodzeisky 2006), contributing to interethnic tensions and creating high social
costs. For example, past research demonstrates not only a robust positive association between
individual anti-minority attitudes and manifest discriminatory behaviours (Dovidio,
Kawakami, and Gaertner 2002; Schütz and Six 1996). In addition, aggregate public opinion
on immigrants and immigration is also viewed to provide the procedural norms motivating
explicit anti-immigrant violence. Consequently, to the extent that avoiding interethnic
tensions between majority members and immigrants is considered desirable, describing andexplaining host society members’ negative attitudes towards immigration is of primary
importance (see Heath et al. 2018 for an introduction to the Special Issue). Of course, we are
not the first to make this observation. However, while a large body of the literature hasstudied negative sentiments towards immigrants in general, only few studies exist that focus
on variations in negative attitudes towards Muslim immigrants in particular (Fetzer and Soper,
2005). But it is this latter group of immigrants and their descendants that currently represent a
prime target of negative attitudes among many citizens of European nation states (see, e.g.,
Strabac and Listhaug 2008; Helbling 2014; Statham 2016; see also the study of Heath andRichards, 2018 in this volume).
Taking previous theory and research on anti-immigrant attitudes as the vantage point,
this study seeks to improve our understanding of the sources driving cross-national variationin prejudice towards immigrants of Muslim faith. To this end, we focus on country-level
characteristics and examine simultaneously demographic, institutional and mass media
sources of citizens’ negative attitudes towards Muslim immigration – our indicator of antiMuslim prejudice. Empirically, our analysis takes advantage of recently acquired large-scale

5

survey data from the 7th round of the European Social Survey (ESS) that we combine withseveral country-level characteristics, and it applies multilevel modelling techniques for testing
our predictions.
The paper will proceed as follows. We will begin with a discussion of the theoreticalconsiderations leading us to expect how various country-level characteristics may explaincross-country differences in negative sentiments towards Muslim immigrants. Next, we will
present the country- and individual-level data and variables we use to test our predictions.
Then we will describe the empirical part and present the results of the multivariate analysis
using multilevel modelling. We will finalise with a summary and some concluding remarks.

Theoretical background
Which sources account for cross-national differences in majority members’ anti-Muslim
prejudice?1 In part, such variation in intergroup attitudes might certainly result fromaggregated individual differences of majority members living in different nation states (i.e.
compositional effects). However, previous theory and research on anti-immigrant prejudice ingeneral shows that the contexts within which intergroup relations take place affect majority
members’ reactions towards immigrants as well. Building on and extending this body ofresearch, we will test if and how anti-Muslim prejudice is shaped by (1) the relative groupsize of the Muslim population, (2) institutional characteristics, and (3) immigration-related

1 Some comments on terminology are in order before we outline our theoretical expectations. In this paper, we
adapt Crandall and Eshleman’s (2003, 414) general deﬁnition of prejudice as ‘a negative evaluation of a socialgroup or a negative evaluation of an individual that is signiﬁcantly based on the individual’s group membership’to designate majority members’ negative attitudes towards Muslim immigrants. The empirical and theoreticalfocus of anti-Muslim prejudice certainly encompasses multiple dimensions. However, it is the immigration ofpeople of Muslim faith that figures particularly high on the public agenda of European host societies, and it isthus this facet of anti-Muslim prejudice that is focused on in the present study.

6news reports. Below we will discuss the theoretical foundation of each of these explanatory
factors.

Relative size of the Muslim population
A popular contextual-level explanation attributes majority members’ anti-minority attitudes
and behaviours to the relative size of the minority population (Blalock 1967). Most studies
following this approach adhere to the assumption that for part of the majority population, alarger minority group size increases perceptions of intergroup competition for scarce orvalued goods. Presumably, such intergroup competition results in manifest anti-minority
prejudice. To date, a considerable number of cross-national studies on anti-immigrant
attitudes provide evidence for the assumption that a larger immigrant group size increases
various forms of anti-immigrant attitudes (Quillian 1995; Semyonov, Raijman, andGorodzeisky 2006; Schlueter and Wagner 2008). However, more recent studies tend to findless support for the presumed nexus between immigrant group size and anti-immigrant
attitudes (e.g. Kuntz, Davidov, and Semyonov 2017; Schlueter, Meuleman, and Davidov2013). This also holds for the cross-national study on anti-Muslim prejudice by Strabac and
Listhaug (2008), who report that the size of the Muslim population exerts no statisticallysignificant influence on majority members’ social distance towards their Muslim neighbours.
Interestingly, an alternative theoretical perspective suggests that a larger minority
group size might actually improve anti-minority attitudes. Specifically, intergroup contact
theory (Pettigrew 1998) has been linked to the proposition that a larger minority group size isoften associated with an increased chance for personal, positive intergroup contacts (Blau and
Schwartz 1984; Schlueter and Wagner 2008). Such contacts,and intergroup friendships inparticular,are well-known as channels to improve intergroup attitudes (Pettigrew 1998;
Pettigrew and Tropp 2006). Similarly, research on the mere exposure effect – the notion that
repeated exposure to a stimulus results in improved attitudes towards that stimulus (Zajonc

71968) – leads one to expect less anti-minority prejudice vis-á-vis a larger outgroup (see Kalin
and Berry 1982), at least as long as pre-existing hostile attitudes are relatively absent (Crisp,
Hutter, and Young 2009). Taken together, these latter theoretical perspectives converge insuggesting that a larger size of the Muslim population will be associated with less antiMuslim prejudice.

Institutional characteristics
Next, we shift our focus to country-level institutional characteristics. We first consider
two opposing, yet logically equally plausible theoretical perspectives on the role
immigrant integration policies might play for shaping anti-Muslim prejudice (Schlueter,
Meuleman, and Davidov 2013). A group threat perspective (Blalock 1967) leads one to
expect that granting minorities more rights as indicated by more encompassing policies
will enhance majority members’ anti-minority reactions. This prediction rests on theassumption that majority members will perceive granting more encompassing rights tothe minority group as a loss of their own (perceived) privileges, with greater negativity
serving to defend or restore such rights. On the other hand, group norm theory(Pettigrew 1991; see Schlueter et al., 2013) could be taken to imply that policies alsofunction as normative expectations about appropriate intergroup relations. This view
predicts that in contexts characterised by more permissive (restrictive) policies
concerning the position of the minority group, majority members will show less (more)anti-minority prejudice. Empirically to date, most studies examining the role ofimmigrant integration policies for majority members’ anti-immigrant prejudice find that
more permissive policies are associated with more positive attitudes towards
immigrants (Schlueter, Meuleman, and Davidov 2013; see also Heizmann 2015;
Hooghe and de Vroome 2015; Callens and Meuleman 2016; Green et al., 2018).

8

Immigrant integration policies, however, are certainly not the only institutional
characteristic affecting citizens’ attitudes towards Muslim immigrants. Recently,
Helbling and Traunmüller (2016) developed a novel approach according to which thereligious policies of governments are a major factor for shaping majority members’
anti-Muslim attitudes. Helbling and Traunmüller (2016) suggest that in politicalcontexts where religious minorities (i.e. Muslims) receive more government support,
members of the majority will perceive such gains as a loss of their own group’s cultural
resources and, hence, react with greater negativity towards the Muslim minority. In
clear support of their theoretical predictions, the authors find that citizens living in
Swiss cantons with a higher degree of religious regulation show more negative attitudes
towards Muslims. Notice that this evidence deviates from the alternative view thatpolicies on government–church relations granting the Muslim minority more rights
could reduce anti-Muslim attitudes (Fetzer and Soper 2005). This alternative view alsosuggests that people may perceive strengthening Islam as strengthening the position ofreligion in Europe in general – including Christianity – rather than as a zero-sum game
(Carol, Helbling and Michalowski 2015). This, in turn, may bring about positive
attitudes toward Muslims and reduce prejudice, particularly among Christian
respondents. While Helbling and Traunmüller (2016) test their expectations on a subnational level, it may be potentially fruitful to apply their theoretical model for
explaining also between-country differences in negative attitudes towards Muslim
immigrants across Europe (Helbling and Traunmüller 2016, 394). Accordingly, in thepresent paper we will also shed new empirical light on the prediction that granting
Muslims more comprehensive religious rights increases anti-Muslim attitudes.

News reports related to immigration and Muslims

9News reports on Muslim immigration and immigrants have long been hypothesised to shapecross-national variation in majority members’ anti-Muslim prejudice (Strabac and Listhaug2008). However, possibly also due to a lack of suitable data sources, to date no study seems toexist that provides a systematic empirical test of the presumed nexus between news reports
and negative attitudes towards Muslim immigrants. Historically, the assumption that mass
mediated news reports shape intergroup attitudes dates back to the early account of explaining
intergroup relations by Blumer (1958) or Allport (1954). The general theoretical vantage pointof this literature is the simple notion that the mass media represents an important source of
information for majority members’ attitudes towards other groups in society. Because news
reports about minority groups tend to emphasise problematic and, hence, negative aspects ofminority groups and their members, an increase in news reports about minority groupmembers with negative valence presumably increases anti-minority attitudes. Importantly,
news reports on minority-related issues might shape anti-minority attitudes not only via
individual exposure to the mass media, but also via, for example, interpersonalcommunication in peoples’ proximal social contexts (Schlueter and Davidov, 2013). This
opens up the possibility to conceptualise news reports of issues related to immigration as acontextual, country-level characteristic that also results in more negative attitudes towards
Muslim immigrants. In the next section we are going to examine the theoretical expectations
sketched above across 20 European countries.

Data, variables and method
Data
To examine our theoretical expectations we combined various individual- and country-level
data sources into a new data file. At the individual-level, our main data source is the7th roundof the European Social Survey (ESS) collected in 2014-152. The ESS is a biennial cross2 All data and replication materials are available from the first author upon request.

10

national survey programme carried out in several European countries that is widely
considered to rely on very high methodological standards in terms of questionnairedevelopment and fieldwork (see, e.g., Jowell et al. 2007). For each country, representative
samples were collected by means of face-to-face interviews of randomly drawn household
representatives aged 15 years or older. Given that this research focuses on majority members’
attitudes towards Muslim immigrants, we excluded the following respondents from thesample: those born outside the country of data collection; those without the country’s
citizenship; and those respondents who indicated they were Muslims. Furthermore, we
excluded the Israeli sample from the analysis because of the distinct character of immigrationin this country. The remaining total sample size included Ni = 31,557 respondents3 nested
within Nj = 20 countries.

Dependent variable
We assessed negative attitudes towards Muslim immigrants using the single item available in
the ESS data that explicitly inquired about respondents’ view towards Muslim immigration.
Respondents were asked to indicate to what extent they think that their country should allow
Muslims from other countries to come and live in their country. Response options were given
on a four-point Likert-type scale and comprised of the following categories: 1 = ‘allow many
to come and live here’, 2 = ‘allow some’, 3 = ‘allow few’ and 4 = ‘allow none’. This is a

3 The countries (with sample size in parentheses, including cases with missing values) in the analysis were:Austria (1,543), Belgium (1,480), the Czech Republic (2,092), Germany (2,708), Denmark (1,369), Spain(1,745), Estonia (1,474), Finland (1,982), France (1,641), United Kingdom (1,924), Hungary (1,662), Ireland(2,067), Lithuania (2,164), Netherlands (1,702), Norway (1,258), Poland (1,598), Portugal (1,165), Slovenia(1,107), Sweden (1,541) and Switzerland (1,089). Data were retrieved fromhttp://www.europeansocialsurvey.org/ where more detailed information about data collection procedures andmethodological documentation is provided.

11

simple but face-valid indicator, with the item wording clearly assessing negativity towards
Muslim immigrants in the societal domain.4,5Country-level independent variables
Muslim population relative group size. We employed the percentage of Muslims in thenational population for the year 2010 to operationalise Muslim outgroup relative size (Pew
Research Center 2015);6 these data were compiled from multiple sources such as censuses orpopulations registers . Countries with estimated Muslim populations of less than 10,000
persons were assigned the score 0.1%.
Immigrant integration policies. To assess the relation of immigrant integration policies withnegative attitudes towards Muslim immigrants, we used the Migration Integration Policy
Index (MIPEX) overall score for each country for the year 2015 (Huddleston et al. 2015).
These are expert-rated scores covering multiple policy areas such as immigrants’ access tocitizenship or their opportunities for political participation. Following previous research(Schlueter, Meuleman, and Davidov 2013), we used the sum of the scores across the eightpolicy areas for each country. Higher scores indicated more liberal immigrant integration
policies, enabling us to distinguish more liberal countries from those with more restrictive
immigrant integration policies.
State support of religious practice. To operationalise state support of religion, we use thecomposite measure of religious legislation available from the ‘Religion-and-State II’ (RAS II)

4 A recent study (Davidov, Cieciuch and Schmidt, 2018) has shown that the measurement parameters of thisitem are rather similar across European countries when included in a latent variable that measures attitudes tovarious specific immigrant groups such as Muslims, Sinti and Roma or Jews.
5 The number of missing values in this variable was rather moderate (4.3%). We therefore opted for listwise
deletion instead of more sophisticated procedures for dealing with missing data in the analysis. However,supplemental analyses using robust full information maximum likelihood estimations yielded highly similar
results to those reported here and are available in tabulated form upon request.
6 More detailed information on different variants of Islamic faith, for example Sunni, Shi’s or Alawites, wasunfortunately not available, but such a differentiation is beyond the scope of this study.

12dataset provided by Fox (2008). All data refer to 2008, the most recent year of data collection
for the index. Higher scores indicated higher levels of support for religious practices.
News reports related to immigration and Muslims. We used the ESS media claims dataset to
examine the role of negativity of news on immigration and Muslims for shaping negative
attitudes towards Muslim immigrants (European Social Survey 2017). These so-called claimsare ‘single communicative acts by non-media actors’ (Koopmans and Olzak 2004, 13). Dataon the claims were derived in each country from manual content analyses of news reports inleading national newspapers published during the national field phases of the ESS (ESS2016). The ESS database on the claims provided the number of positive, neutral or negative
claims during the ESS field phase on various issues including immigration-related ones ineach country (European Social Survey 2016; see Statham and Tumber 2013). For eachcountry we subtracted the number of positive from the number of negative claims on
immigration-related issues. Because this difference score was considerably skewed acrosscountries, we computed its median as a simple but more robust measure assessing thefrequency of negative media claims related to immigration and Muslims. Countries werecoded as 1 if their score was higher than the cross-country median and 0 if not.

Control variables
In addition to the aforementioned country indicators of primary theoretical interest, we
included several individual-level control variables in our models. Within countries, the major
purpose of these controls is to reduce concerns that compositional differences between
national samples might bias the results. Gender was coded with males as the reference
category (0 = male; 1 = female). Age was assessed in years. The highest level of education
was measured in the ESS based on a novel coding scheme comprising 28 categories (0 = ‘notcompleted ISCED 1’ up to 800 = ‘ISCED 6, doctoral degree’). To distinguish between those
who are unemployed and actively searching for a job from others who are not, we used a13

dummy variable (0 = other; 1 = unemployed). Furthermore, we included a variable assessing
respondents’ subjective feeling on whether their income meets their needs (1 = ‘Living
comfortably on present income’; 2 = ‘Coping on present income’; 3 = ‘Difficult on present
income’; 4 = ‘Very difficult on present income’) as an indicator of economic deprivation.
Religious affiliation was operationalised using six dummy variables (Roman Catholic,Protestant, Orthodox, Jewish, Eastern Religion, other Christian, other non-Christian).
Respondents without any religious affiliation served as the reference category. To measure
friendships with immigrants (e.g. Quillian 1995; Schlueter and Wagner 2008), respondents
were asked ‘Do you have any friends who have come to live in [country] from anothercountry?’ The original response options were 1 = ‘Yes, several’, 2 = ‘Yes, a few’ and 3 = ‘No,
none’. We recoded this variable so that higher values indicated more contact (for a moredetailed examination of the contact effect, see Green et al. 2018, and Meuleman et al. 2018, in
this volume). Since the extent of media consumption in the form of watching TV may also
explain negative attitudes towards Muslim immigrants (Popescu et al. 2015), we controlled
for individual TV exposure as well. The ESS included two such variables: the total number ofhours watching TV news (that we labelled ‘TV news’) and the total number of hours per day
watching TV in general. Both were measured on an eight-point scale (ranging from 0 = none
to 7 = more than 3 hours). We created a new variable which we labelled ‘TV not news’. The
latter was calculated by subtracting the scores of the variable ‘TV news’ from the scores of thevariable measuring the total number of hours watching TV7. A table summarising thedescriptive univariate statistics of the variables described above is presented in the Appendix.

Results
Descriptive results

7 Preliminary analyses have shown that the effect of ‘TV news’ is considerably lower. Therefore we did notinclude this variable as a control variable in the analysis.

14

Before considering the multivariate results, we briefly explored the bivariate country-level
relations between negative attitudes towards Muslim immigrants and the main independent
variables.8

ceAnti-Muslim

Figure 1: Bivariate country-level relations between negative attitudes towards Muslim
immigrants and (a) relative group size of Muslims, (b) immigrant integration policies, and (c)state support of religion. The y-axis represents the anti-Muslim prejudice scores in a country.

(a) Relative group size Muslims(b) Immigrant Integration
Policies(c) State Support of Religion

The three panels of the figure show clear between-country variations in attitudes towards
Muslim immigrants, with the country means stretching from a minimum of 1.81 in Sweden toa maximum of 3.46 in Hungary. The first panel of the figure illustrates remarkable differencesin the percentage of Muslims living in European nation states, ranging from (less than) 0.1%
in several eastern European countries to 5.9% in Belgium. Combined, the first panel
visualises that a higher percentage of Muslims per country is associated with less negativity
towards Muslim immigrants (r = -0.73; p < 0.001). In the second panel, we find that European
countries with more liberal immigration policies are associated with decreased negative
sentiments towards Muslim immigrants (r = -0.57; p < 0.05). The third panel of the figure,displaying the association between country-level negative attitudes towards Muslim
immigrants and the composite measure of state support of religious practice, reveals moredifferentiated results. The bivariate correlation for all 20 country cases is only weakly
negative (r = -0.11, p = 0.62). However, a closer inspection reveals that the data from
Hungary, the Czech Republic and Lithuania present influential cases in that they combine
8 No scatter plot is presented for the fourth independent country-level variable, News reports related toimmigration and Muslims, because it was a dummy variable. Instead, we report its association with country
variations in anti-Muslim prejudice below.

15very high scores on the single indicator assessing anti-Muslim prejudice with relatively highscores on the state support of religious practice. Indeed, when these three country cases are
excluded from the sample, the negative bivariate correlation rises to r = -0.58 (p < 0.05). This
indicates that the data show a negative relation between government support for religious
services and majority members’ anti-Muslim attitudes for the remaining 17 country cases. We
find no statistically significant relation between the dichotomous indicator assessing
immigration and Muslim-related claims in news reports and the average levels of anti-Muslim
attitudes across countries (r = 0.23; p = 0.33). To achieve a more comprehensive
understanding of these preliminary and aggregate-level findings we next turn to the results ofthe multilevel analyses.

Multilevel analysis
The data we used were hierarchically structured with respondents nested within countries. We
therefore used hierarchical linear regression modelling (HLM) techniques for testing ourhypotheses. These multilevel models allow separate residual terms for each level of analysis,
which yields adequate standard errors for the parameter estimates (Raudenbush and Bryk
2002). Technically, we fit a series of multilevel models that we estimated incrementally.
All models were estimated using restricted maximum likelihood procedures (Maas and Hox
2004) as implemented in SPSS 23 (IBM 2016). We began by calculating the intra-classcorrelation coefﬁcient (ICC) from a random effects ANOVA.
The ICC indicated that approximately 16.3% [= (.15 /.15 + .78) × 100] of thetotal variance in the dependent variable could be attributed to between-country
differences, a result which clearly pointed to the potential relevance of country-levelsources of anti-Muslim prejudice. Accordingly, Table 1 presents the findings from threemultilevel models that are structured incrementally. In Model 1, we included the individuallevel control variables. We only briefly consider the parameter estimates for these16variables, as they are not the main focus of our study. The results presented for Model 1
suggested that whereas respondents’ age and economic deprivation was associated withgreater negativity towards Muslim immigrants, they provided renewed empiricalevidence for the known effects of education and intergroup friendships as factors
reducing anti-Muslim prejudice. We further found that, as compared to atheists,
respondents identifying themselves as Catholic, Protestant or Orthodox reported morenegative attitudes towards Muslim immigrants. Importantly, even after accounting for these
individual-level characteristics, a substantial amount of country-level variance in thedependent variable remained unexplained (residual ICC = 11%). We therefore added
our country-level independent variables in Model 2.
In Model 2 we reduced the unexplained variance on the country-level by 68%. The
results presented for Model 2 revealed that the observed country-level differencesin negative attitudes towards Muslim immigrants were partly due to country differences in therelative size of the Muslim population. In line with the descriptive findings, the results
displayed a significant and negative association between the percentage of Muslims andnegative attitudes towards Muslim immigrants (Beta = -0.677). Stated differently, with anincrease of one standard deviation in the relative size of the Muslim population, anti-Muslim
prejudice decreased by 0.677 standard deviation units. This negative association deviatedfrom the expectations derived from group conflict theory that a larger minority group size
evokes greater anti-minority negativity, but is consistent with the assumption that increased
intergroup contact or familiarity with minority group members can alter anti-minority
sentiments. In the second model we added our country-level predictors. Are less restrictive
immigrant integration policies associated with decreased anti-Muslim prejudice? The results
provided clear support for this expectation (Beta = -0.391). Reconfirming findings fromearlier research on immigrants more generally, the data showed that countries with lessrestrictive policies displayed also more positive attitudes towards Muslim immigrants. In

17other words, we found that a one standard deviation increase in the permissiveness of acountry’s integration policies was associated with a 0.391 standard deviations decrease inanti-Muslim prejudice9. Before we examined the role of the state’s support of religious
practices for cross-national differences in majority members’ prejudice towards Muslims, weconsidered the relation between the relative frequency of negative immigration- or Muslimrelated claims in news reports and negative attitudes towards Muslim immigrants. The
nonsignificant parameter estimate indicated that the amount of negative claims on
immigration in the media is not reliably associated with anti-Muslim prejudice.
Turning our attention to the association between state support of religion and citizens’
anti-Muslim prejudice, our preliminary conclusion was that the associated negative parameter
estimate cannot be distinguished from zero. However, recall that the results from the bivariate
scatterplots suggested that a cluster of three country cases – the Czech Republic, Hungary and
Lithuania might had an inordinate influence on the statistical relations between state supportof religion and anti-Muslim prejudice. In Model 3, we therefore excluded these three
countries from the analysis. In fact, doing so revealed a significant and negative association
between state support of religion and citizens’ anti-Muslim prejudice (Beta = -0.362) among
the remaining 17 West European country samples. This result deviated from the predictionthat greater government support of religious practice in general and Muslim religious
practices in particular may generally be perceived as threatening the host society’s majority
group resources. Instead, the negative association between state support of religion and antiMuslim Prejudice suggested that such state support may foster acceptance of members ofminority religious groups in a country and, hence, may reduce negative attitudes towards
Muslim immigrants among citizens in European nation states. However, this finding could not
be generalized to all 20 West and East European countries in our sample.
9 As suggested by a reviewer, we furthermore examined whether accounting for country-level economic
conditions alters the association between immigrant integration policies and respondents’ negative attitudes
towards Muslims. However, it turned out that an additional model including country-level GDP left thepreviously observed parameter estimate of immigrant integration policies intact (beta = -.385; b = -.010, p < .05).

18

Summary and discussion
Existing research has advanced a variety of theoretical approaches to explain majority
members’ anti-Muslim prejudice. However, cross-national comparative studies that put these
theoretical expectations under systematic empirical scrutiny are still largely missing (Fetzer
and Soper 2005, p. 131). This study sought to help fill this gap in the literature. To this end,
we examined if and how the relative size of the Muslim immigrant population, institutional
characteristics in the form of immigrant integration policies as well as country support ofreligious practices and the negativity of claims in immigrant and Muslim-related news reports
shape majority members’ negative attitudes towards Muslim immigrants. Below wesummarize our findings.
First, our results confirm the important role played by the relative size of the Muslim
population as a demographic characteristic shaping majority members anti-Muslim prejudice.
The finding that a larger relative size of the Muslim population was associated with decreased
levels of negativity towards Muslims contradicts a group conflict perspective. Instead, a larger
percentage of Muslim residents seems to provide majority members more opportunities for
positive contact and mere exposure, both of which help to reduce negative sentiments. Future
research may examine these assumptions more closely and investigate the complete link
between the relative size of the Muslim population, anti-Muslim attitudes, and intergroupcontact with Muslims – an indicator not available in the present data – to tap into specificcontacts with Muslims.
Second, the present results provide renewed evidence that institutional characteristics
are crucial for understanding cross-national differences in anti-minority attitudes. Less
restrictive immigrant integration policies were associated with less negativity towards Muslim
immigrants, suggesting that policies shape majority members normative expectations about
appropriate intergroup relations. Relatedly, stronger state support of religious practices proved

19

to be associated with decreased levels of anti-Muslim prejudice as well (albeit this conclusion
held only for a subset of 17 (mostly West European old democracy) countries. Apparently,
majority members do not necessarily perceive enhanced state support for the practice of otherreligions as a threat to the dominant (Christian) population. Instead, respondents may view
such policy measures as a normative signal from the state that the practice of Islam in the
country is accepted and welcome. Notice, however, that in this study we used a measure ofstate support of all religions in a country. Thus, it would be interesting to explore in future
research the link between negative attitudes to Muslims and state support of the practice ofIslam in particular in European countries, when such a more specific measure becomes
available.
Finally, our study finds no evidence that more negative media claims about immigrants
and Muslims affect between-country differences in anti-Muslim prejudice. We emphasize,
however, that the absence of a meaningful relation between the tone of mass mediated news
referring to Immigrants/Muslims and anti-Muslim prejudice should be interpreted withcaution. Specifically, the ESS media claims data used in this research certainly represent a
useful source for the cross-national assessment of mass media coverage. However, thecollection and analysis of media claims data within the ESS framework is relatively novel andonly limited knowledge exists concerning the comparability of these data collected indifferent cultural and temporal contexts. Thus, future research initiatives that employ
alternative techniques for measuring mass media coverage of Muslim immigrants (including
both online and print media as well as social media channels) might offer better insights on
the link between media claims and anti-Muslim sentiments.
Before closing, we wish to acknowledge two additional caveats that may limit some of
the interpretations of the current findings. Specifically, this research shares the limitations of
all non-randomized research designs in that our findings are correlational only and cannot
provide strong evidence for causal effects. For example, it is conceivable that Muslim

20

immigrants choose to immigrate to those countries where attitudes towards Muslim
immigrants or immigrants in general are more positive to start with. As a result, in countries
with a larger relative size of Muslims we may find less negative attitudes towards Muslim
immigration due to a selection effect, deviating from the theoretical assumptions underlying
this research. Similarly, immigrant integration policies may also be a consequence (rather thana cause) of majority members’ attitudes towards immigration (Wlezien 2004; but see
Freeman, 1995). Other researchers may better address the question about the direction of thecausal flow by taking advantage of longitudinal cross-national data; Such an endeavour isbeyond the scope of the present study. In addition, we would like to underline the fact that thecountry-level sample size of this study comprised twenty cases only. In Model 3 in which
three samples were treated as influential cases and omitted from the analysis, most country
cases left included Western European host societies. Taken together, these limitations restrict
the possibility to generalize the results of our study to all Eastern and Western European
countries. Therefore, additional research that utilizes a larger and more diverse set ofcountries is essential to help evaluate the generalizability of our conclusions.
In spite of these limitations, our findings demonstrate that contextual factors such as
the size of the Muslim population, immigrant integration policies and state support of religion
are central for understanding the sources of majority members’ prejudice towards Muslim
immigrants. These predictors do not exclude each other but rather operate in a complementary
way. Thus, we believe that considering them in future explanations of cross-country
differences in negative attitudes towards Muslim immigrants in particular, and immigration in
general, may contribute to a better understanding and to a fuller delineation of the complex
picture that describes and explains how negative attitudes towards immigrant minorities may
come about. In so doing, we hope that the benefits of examining various theories on antiMuslim prejudice cross-nationally will be recognized.

21

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Table 1. Multilevel linear regression models for explaining anti-Muslim prejudice
Model 1b(s.e.)
Intercept

TV exposure

2.732
.000
(.019).005***(.001)-.001(.000)-.035(.018).102***(.013)-.234***(.018).057(.033).034(.023).001(.043)-.083(.058)-.106(.224)-.094(.074)-.200*(.084).027***(.006)

Muslim Population---

GenderAge
Education
Unemployed
Subjective feeling of income

Friendships with immigrants
Catholic
ProtestantOrthodox
Other ChristianJewishEastern Orthodox
Other non-Christian

Beta

.008
.114
-.195
-.039
.092
-.180
.064
.038
.001
-.093
-.120
-.106
-.248
.062

---

Immigrant integration policies

---

---

State support of religion
Immigration/ Muslim-related
news reports

---

---

--.683.110
20
31,557---

Within-country variance
Between-country varianceCountries
Individuals

Model 2b(s.e.)
Beta

Model 3b(s.e.)

3.843
.000
.000
(.019).005***(.001)-.001*** -.185(.000)-.035-.04(.016)
101*** .092(.013)-.234*** -.180(.018)
054.061(.033).034.038(.023).000.000(.044)-.083-.093(.058)-.106-.121(.224)-.093-.105(.074)-.221-.249(.098).031*** .062(.006)

3.641.002(.022).006***(.001)-.001***(.000)-.026(.017).120***(.011)-.229***(.021).069(.038).041(.024).038(.064)-.107(.067)-.181(.258)-.105(.076)-.286**(.093).034***(.006)-.088*** -.677(.013)-.011*-.391(.004)-.017-.167(.010).120.374(.094).683.035
20
31,557-.053**(.015)-.008*(.003)-.028*(.012)-.545--.694.021
17
26,249

---

Beta

.002
.123
-.197
-.028
.104
-.174
.076
.045
.043
-.118
-.200
-.116
-.317
.065

-.374
-.362

28
Note. Parameters are unstandardized (b) and standardized (beta) regression coefficients 10; standard errors (s.e.) inparentheses. * p < .05; **p < .01; ***p <. 001, (two-tailed). Because the data from Hungary, Czech Republic and Lithuania
proved as influential cases when assessing the role of state support of religion, we excluded these samples in model 3.

Appendix A. Variables and descriptive statistics (mean, SD, or percentage) for the variables included in the
study.

Variables

Definition

Mean (SD) or
percentage“Using this card, please tell me to what
extent you think [country] should allow
Muslims from other countries to come andlive in [country]?”

2.66 (0.97)

Muslim population

Size of the Muslim population per country inpercent for 2010 (Pew, 2017).

3.02 (2.52)

Immigrant integration policies

Additive index of the Migrant Integration
Policy Index (MIPEX) 2015 scores.

56.9 (12.25)

State support of religion

Composite measure of religious legislation
2008 (Religion and state, round II data).

8.65 (3.13)

Immigration/ Muslim-related news
reports

Median split based of the difference between
the total number of negatively and positively
evaluated claims related to immigration
and/or Muslims as derived from the ESS
media claims data (1 = above the median of
the country sample).

54%

Gender2 = female

52.4%

Age

in years

48.68 (18.75)

Education

Measured in 28 categories;
recoded from 1 = ‘not completing ISCED 1’
to 28 = ‘ISCED 6’.

14.28 (7.75)

Unemployment

0 = not unemployed; 1 = unemployed

5.8%

Subjective feeling of income(whether income meets respondents’needs)“Which of the descriptions on this cardcomes closest to how you feel about yourhousehold’s income nowadays?”

1.9 (.79)

Religious affiliation

Roman Catholic = 35.4%, Protestant = 15.5%, Orthodox = 0.9%,
Jewish = 0.1%, Eastern Religion = 0.3%, other Christian = 1%,other-non-Christian = 0.2%; reference category = no religious
affiliation = 46.7%.

Friendships with immigrants“Do you have any close friends who are of a

Dependent variable
Anti-Muslim prejudiceCountry-level independent variables

Individual-level control variables

10‘Yes, a few’ 35.2%

For the dummy-variables in our models, we divided the unstandardized regression coefﬁcient b by thestandard deviation of anti-Muslim prejudice. Thus, b expresses the change for anti-Muslim prejudice instandard deviation units when the independent variable changes from the reference category to one (Muthén and
Muthén, 1998–2010, p. 642).

29

TV exposure

different race or ethnic group from most[country] people?”‘Yes, several’ 11.5%

Difference between the variables ‘On an
average weekday, how much time, in total,
do you spend watching television?’ and‘And again on an average weekday, howmuch of your time watching television isspent watching news or programmes about
politics and current affairs?’

2.41 (1.75)

Note: Descriptive statistics were calculated using design weights provided by the ESS.

30
Appendix B: Cross-country variation of the dependent and the country-level explanatory variables

Variables

CountryAnti-Muslim
Prejudice

Percentage
MuslimPopulation

Immigrant
Integration
Policies*

State Support News Reportsof Religion** (Median)***

Mean

SD

Austria

2.68

.94

5.40

48

4

1

Belgium

2.60

.90

5.90

70

8

1

Switzerland

2.47

.87

5.00

46

11

1

Czech Republic

3.4

.76

0.10

45

13

1

Germany

2.09

.85

5.80

63

12

0

Denmark

2.43

.87

4.10

59

12

0

Estonia

3.05

.92

0.20

49

3

0

Spain

2.67

.97

2.10

61

10

1

Finland

2.71

.88

0.80

71

11

0

France

2.39

.87

7.50

54

7

1

United Kingdom

2.53

.93

4.80

56

11

1

Hungary

3.43

.75

0.10

46

9

1

Ireland

2.73

.95

1.10

51

7

0

Lithuania

3.01

.87

0.10

38

12

1

Netherlands

2.49

.84

6.00

61

4

0

Norway

2.23

.84

2.80

69

10

0

Poland

2.96

.92

0.10

43

8

0

Portugal

2.97

.92

0.30

80

5

0

Sweden

1.82

.80

4.60

80

11

1

Slovenia

2.60

.92

3.60

48

5

1

Notes: Means and standard deviations (SD) of Anti-Muslim prejudice were calculated using design weights
provided by the ESS.
* Higher scores indicate more liberal immigrant integration policies.
** Higher scores indicate higher levels of support for religious practices.
*** Frequency of negative media claims related to immigration and Muslims, 1- score of negativity higher thanthe cross-country median, 0 - otherwise.