What determines the rejection of immigrants through an integrative model

Accepted Manuscript What determines the rejection of immigrants through an integrative model a M Ángeles Cea D'Ancona PII:

S0049-089X(17)30431-3

DOI:

10.1016/j.ssresearch.2018.05.008

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YSSRE 2173

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Social Science Research

Received Date: 18 May 2017 Revised Date:

22 March 2018

Accepted Date: 28 May 2018

Please cite this article as: D'Ancona, Ma.Á.Cea., What determines the rejection of immigrants through an integrative model, Social Science Research (2018), doi: 10.1016/j.ssresearch.2018.05.008. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT TITLE: WHAT DETERMINES THE REJECTION OF IMMIGRANTS THROUGH AN INTEGRATIVE MODEL

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AUTHOR: Mª Ángeles Cea D’Ancona is a Professor of Sociology at Complutense University of Madrid (Social Research Methodology Department) and the Director of GEMI

(International Migration Studies Group: http://www.ucm.es/info/gemi/). Her recent research focus has been within the field of sociology of migration, racism and

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xenophobia, public opinion, with a special emphasis on methodological aspects. She has published widely on quantitative methods, multivariate analysis and survey

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methodology. She is the main researcher of projects MEXEES I (SEJ2005-00568), MEXEES II (CSO2009-07295), MEDIM I (CSO2012-36127), MEDIM II (CSO201675946-R) and Living Together (JLS/FRC/2007), and is carrying out annual researches about the evolution of racism and xenophobia for OBERAXE (the Spanish Observatory

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on Racism and Xenophobia) since 2007.

ADDRESS:

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Departamento de ‘Sociología: Metodología y Teoría’ (antiguo Sociología IV) Facultad de Ciencias Políticas y Sociología Universidad Complutense de Madrid Campus de Somosaguas 28223 Pozuelo de Alarcón (Madrid) España Tel: +34-91-3942671 Fax: +34-91-3942673

Email: [email protected]

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ACCEPTED MANUSCRIPT SPONSOR OF THE RESEARCH: This work was supported by a grant from the Spanish Ministry of Finance and Competitiveness (CSO2012-36127); a research project that has continuity in another on the measurement of multiple discrimination (CSO2016-75946-R). This is a revised

(Gijón, Asturias, Spain, June 30, July 1-2, 2016).

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version of a paper presented and discussed at the 12th Spanish Congress of Sociology

‘The authors declare that there are no potential conflicts of interest with respect to the

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research, authorship, and/or publication of this article.’

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WHAT DETERMINES THE REJECTION OF IMMIGRANTS THROUGH AN INTEGRATIVE MODEL

ABSTRACT

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This study tests different explanations of anti-immigrant attitudes through a model that incorporates aspects of group conflict, social identity and intergroup contact theories.

Multigroup structural equation modelling was applied in three surveys, which tracked the same indicators in no similar economic and migratory contexts. In times of economic crisis, the perceived economic threat seems to lead more to discrimination and rejection of

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immigrants (in line with Group Conflict Theory), while sense of cultural threat is more likely to curb any desire for coexistence with them. Both threats show to be affected by the

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perceived size of the immigrant population, which increases the feeling of threat in those traditionally favourable to immigration. Other explanatory factors were also corroborated. Increasing contact with immigrants helps to lessen rejection, especially in the case of cultural threat. Effects due to insecurity were less marked and those relating to qualification were contrary to what was hypothesized.

equation modelling

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Keywords: xenophobia, group conflict, intergroup contact, threat, multigroup structural

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1. Introduction: theoretical framework

The current research attempts to clarify possible determinants of the rejection of

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immigrants by checking, simultaneously, hypotheses derived from three consolidated theories in the explanation for racism and xenophobia. One of the longest established approaches is the Group Conflict Theory (Sherif and Sherif, 1953; Allport, 1954; Blalock, 1967; Bobo, 1983; Olzak, 1992; Quillian, 1995; Scheepers et al., 2002). According to GCT, ethnic prejudice and anti-immigrant feeling are a defensive reaction to the perception of intergroup competition over scarce resources and the threat to certain interests of one’s own social group. The perceived threat translates into an irrational antipathy, prejudices and overreaction to the negative consequences of immigration (Quillian, 1996; Pehrson and Green, 2010). Although GCT has been widely confirmed, conflicting evidence cannot be ignored (Meuleman et al., 2009). As stated by Burns and Gimpel (2000) and Hainmueller and 1

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Hopkins (2014), personal and national economic outlook play only a small role in predicting attitudes towards immigration: the effect of economic hardship is that it activates prejudices that are already latent. The consideration of immigrants as a cultural threat to national identity is the second most firmly established approach that explains racism and xenophobia. Opposition to immigration

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may be motivated by prejudices related to the cultural and ethnic difference in the immigrant population and the fear of loss of national characteristics (Hainmueller and Hopkins, 2014; Zarate et al., 2004). According to Social Identity Theory, SIT (Tajfel, 1981; Tajfel and

Turner, 1986), individuals derive part of their self-concept from their membership within

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social groups. Groups perceived as threatening a nation’s distinctive identity are likely to elicit hostility (McLaren, 2003). People who are similar to the group belong to the in-group and those who strongly identify themselves with their in-group are more likely to feel

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threatened by the out-group (Bizman and Yinon, 2001; Curseu et al., 2007; Brylka et al., 2015). This perception depends, as does the economic threat, on a demographic balance, not on one’s personal economic situation. As Schneider (2008) and Markaki and Longhi (2013) have shown in Europe, and Ha (2010) and Newman (2012) in US, the percentage of nonwestern or non-EU immigrants (in Europe) and Hispanic immigrants (in US) has increased

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feelings of ethnic and cultural threat. Other studies (Schildkraut, 2005; Wong, 2010; Morrison et al., 2010) have reached a similar conclusion: Americans who take an ethnocultural view of national identity are more supportive of restricting immigration. Researchers have begun to consider these two conceptions of threat as complementary

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rather than mutually exclusive. As Al Ramiah, et al. (2011) state, in a real-world setting it is likely that both objective and psychological causes interact to explain intergroup conflict. The Integrated Threat Theory (ITT), posited by Stephan and colleagues (Stephan and Stephan,

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1996; Stephan et al., 1998, 1999, 2005), proposes that both causes can influence outgroup attitudes simultaneously. They combine these types of threats into a more comprehensive threat model of prejudice where threats are classified into four major types: realistic threat, symbolic threat, intergroup anxiety, and negative stereotypes. Realistic threat is similar to the threats considered by GCT (threats to the physical and economic well-being of the in-group: economic assets and employment opportunities), and symbolic threat to the ideas underlying symbolic racism, where the threat arises from a conflict in values, norms, and beliefs between groups (customs, behaviours, religious practices). These authors have produced persuasive empirical evidence that the four threats predict attitudes toward immigrants in a series of studies conducted across Spain, Israel, Mexico, and US. However, they have acknowledged 2

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that ITT is not a comprehensive theory of prejudice and has now been replaced by Intergroup Threat Theory (Stephan et al., 2009), including antecedents to threat. Threats (intergroup anxiety, realistic, and symbolic) are posited to mediate relationships between antecedents (negative contact, status, in-group identification, and negative stereotyping) and attitudes. Antecedents affect attitudes by influencing threats (Aberson and Gaffney, 2008).

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The revised ITT drew on Intergroup Contact Theory (ICT), based on Allport’s ‘contact hypothesis’ (1954) and later developed into a more comprehensive theoretical framework (Pettigrew, 1998, 2008; Brown and Hewstone, 2005; Pettigrew and Tropp, 2006, 2011;

Pettigrew et al., 2011). According to ICT, increasing contact between members of different

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groups promotes mutual understanding and decreases biases against given outgroups. Groups are more likely to become familiar with each other and develop relationships that would then counteract stereotypes and feelings of threat. It is the quality of contact what determines the

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extent to which contact positively affects outgroup attitudes (Hewstone, 2015). Negative outgroup contact leads to an increase in feelings of threat (Pettigrew, 1998; Barlow et al., 2012). 1.1. Aims

The purpose of this paper is to clarify possible determinants of the rejection of immigration

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through a statistical model drawn up in order to verify, simultaneously, hypotheses derived from GCT, SIT and ICT. More specifically: 1) to establish which perceived threat most determines different expressions of xenophobia; 2) what does the perception of both threats

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depend on; 3) to check whether their effects are constant over time or dependent on contextual changes in the economic situation and flow of immigrants. According to GCT, the scale of competitive threat is a function of both the relative size of

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the immigrant group and the severity of the current economic circumstances (Quillian, 1995; Lapinski et al., 1997; Coenders and Scheepers, 1998, 2008; Scheepers et al., 2002; Rowthorn and Coleman, 2004; Schneider, 2008; Semyonov et al., 2008). An increase in the relative size of the immigrant population or the magnitude of an economic crisis −or a combination of the two− will tend to create greater competition between natives and immigrants over scarce resources. Nevertheless, Sari (2007) shows that individuals’ perception of threat has the most powerful effect on prejudice; the economic scenario does not to matter. In Spain, the greatest increase in xenophobia coincided with a period of economic growth and a greater demographic presence of immigrants (Author 2015). Sudden changes in immigration and the perception of it or economic conditions may affect labour, housing, and other markets more 3

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strongly than slow-paced evolution because of the limited time for the native population to absorb these changes (Olzak, 1992). The current research focusses on Spain, a country whose experience with immigration has attracted international attention, not least due to the degree of immigration the country sustained over a very short period of time, but also to how it dealt with this incoming

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population (Arango, 2012). Between 2000 and 2009, Spain’s foreign-born population more than quadrupled, rising from under 1.5 million to over 6.5 million. During this time, the

average annual net inflow of foreign-born individuals was close to 500,000 people, making Spain the second-largest recipient of immigrants in absolute terms among OECD

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(Organization for Economic Cooperation and Development) countries, after US. However, after 2009, Spain’s economic and migratory cycle saw a significant downturn. According to Eurostat, in 2011 Spain recorded the first negative figures (-0.9 per 1,000 inhabitants) in raw

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terms for net migration since 1993 (-5.1 in 2012, -5.5 in 2013); in 2008 this figure stood at 9.5 and 1.3 in 2010. With regard to the economic situation, in late 2007, Spanish GDP stood at +3.8%; in 2008 it fell to 1.1; in 2010 to 0.2; and in 2012 to -2.1%. The unemployment rate in 2007 stood at 8.60% (12.18 for the foreign population, 7.61 for Spaniards), soaring to 11.34 in 2008; 20.06 in 2010; and 26.02% in 2012 (36.53% for the foreign population, 24.23% for Spaniards), according to the Labour Force Survey produced by the Spanish National Institute

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of Statistics (INE in its Spanish acronym). Both economic indicators1 show that the economic crisis became gradually worse from 2008 to 2012. This also had an effect on the significant downturn in net migration in the period analysed here.

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The role of economic and cultural threats in explaining xenophobia is analysed in three no similar economic and migratory contexts in Spain (2008, 2010 and 2012), as shown by previous statistical data, in order to check whether their effects are constant over time or

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dependent on contextual changes in the economic situation and flow of immigrants. As it can be seen in figure 1, both threats are suggested as mediating the impact of individual differences (qualification, insecurity, conservatism) and situational factors (contact with immigrants) on attitudes towards immigration. A better knowledge of their effects on economic and cultural threat is needed, as well as establishing whether these vary over time. In the integrative model by Ward and Masgoret (2006), ‘multicultural ideology’ was the only

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Economic conditions have often been defined in terms of GDP per capita (Quillian, 1995; Semyonov et al., 2008). However, as GDP does not tell us anything about the distribution of wealth in a country, a second indicator must be used: unemployment rates (Davidov and Meuleman, 2012)

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latent variable for individual differences, and only three indicators for a latent variable measured attitude towards immigrants. In the present research, nine indicators in three latent endogenous variables measure xenophobia: discrimination, coexistence and rejection. We would thus like to differentiate the social distance component and support for discrimination from the most generic and blatant2

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rejection of immigration: the desire for the expulsion of immigrants. This is in line with the progressive scale of rejection behaviours proposed by Allport (1954): avoiding contact would be the second level (after negative criticism); discrimination (deprivation of rights and

opportunities) would be the third; and expulsion, the last or clearest expression of rejection3.

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Within each of these dimensions of xenophobia, the effects of both perceived threats will be verified.

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1.2. Hypotheses

In order to cover the research objectives, several hypotheses were derived from the theories and researches summarized in the Introduction:

H1) In times of economic crisis, perceived economic threat is the factor which most determines discrimination and rejection of immigrants (by increasing the competition

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for scarce goods: GCT). Its effects are expected to be more pronounced in years of greatest economic hardship (lower GDP and higher unemployment rate: 2012). Attitudes are more likely to be intolerant when perceiving a greater threat to the economy and employment from immigrants (Kehrber, 2007; Gustin and Ziebarth, 2010)

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H2) Cultural threat (by evoking affective responses) leads more to avoid coexistence with immigrants than to discrimination. This does not necessarily vary at the three time

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points related to the different intensity of economic recession. H3) The perceived size of the immigrant population is what most determines both economic and cultural threat4. Its effect is expected to be greater in years of higher immigrant 2

Pettigrew and Meertens (1995) also differentiated between subtle and more blatant prejudice. On the other hand, the measurement model obtained applying multigroup confirmatory factor analysis (MGCFA), and described in section 3.1, showed that the correlations between the three latent endogenous variables (discrimination, rejection and coexistence) were not extremely high (discrimination ↔ rejection = 0.79, 0.76, 0.78; coexistence ↔ rejection = -0.63, -0.64, -0.61; discrimination ↔ coexistence = -0.58, -0.58, 0.59). The option of a single latent endogenous variable (xenophobia) with nine indicators was also ruled out on empirical grounds (over the model with three separate latent endogenous variables). 4 Though it might be true that the perception of economic or cultural threat would affect the perceived size of the immigrant population, the most corroborated hypothesis is the reverse. According to Group Conflict Theory, the relative size of the immigrant population leads to an increased perceived economic threat of immigrants (Blalock, 1967; Olzak, 1992; Quillian, 1995; Coenders and Scheepers, 1998; Scheepers et al., 2002; Kehrberg, 3

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population (2008) and lower in 2012 (when net migration fell by 5.1 per 1,000 inhabitants) H4) Perceived economic threat is greater in economically vulnerable groups to compete with immigrants for scarce goods (jobs, social welfare benefits, housing...) (as stated by GCT): those with less education and income, the unemployed and low-skilled workers

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manifest greater anti‐immigrant sentiment than the well-off (Scheepers et al., 2002; Mayda, 2006; O’Rourke and Sinnott, 2006; Pettigrew et al., 2007; Lancee and PardosPrado, 2013).

H5) Cultural threat comes mostly from the ideologically conservative. This will depend less

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on economic vulnerability and more on conservatism, or the desire to maintain the

integrity of the national culture (customs, behaviours, religious practices). Identity will explain the rejection of immigrants (SIT, ITT) and will affect coexistence. Unlike

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economic vulnerability, its effect is not expected to vary over time.

H6) Economic and cultural threat increases as the level of qualification falls. The sense of threat is expected to develop primarily among those at the lower end of the education and occupation scales, as the well-off exhibit lower levels of ethnocentrism, express less prejudice, present fewer negative stereotypical attitudes towards minorities, and place

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more emphasis on cultural diversity (Citrin et al., 1997; Burns and Gimpel, 2000; Chandler and Tsai, 2001; Card et al., 2012; Author and Other, 2015). H7) Greater contact with immigrants leads to lowered economic and cultural threat and the consequent rejection of immigration (ICT). ‘One of the most powerful antidotes to

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perceiving threat is intergroup contact’ (Stephan et al., 2005, p. 16). The negative relationship between contact and both threats is not expected to depend on the economic context.

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H8) The perceived size of the immigrant population mediates the relationships between sociodemographic variables and both threats. The perception of larger size increases the feeling of economic and cultural threat in those who are traditionally favourable to immigration: people with better qualifications, less insecurity and conservatism, and those who have contact with immigrants (Author, 2015). Its effect is expected to be greater in years of higher net migration (2008). Nevertheless, the deepening economic

2007; Schneider, 2008; Semyonov et al., 2006). The perception of cultural threat also depends on the demographic balance (Portes and Rumbaut, 2006; Scheneider, 2008; Ha, 2010; Newman, 2012; Markaki and Longhi, 2013).

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recession (higher in 2012) may also contribute to increase the perception of immigrants, and the consequent feeling of threat and rejection. Figure 1 illustrates the model drawn up by the eight hypotheses to be tested,

Figure 1. The hypothesized model

Qualification ξ1

- (H6)

Economic Threat η2

+ (H4)

+ (H1)

Insecurity ξ2

+ (H1)

- (H8) - (H7)

- (H1) + (H3)

+ (H8)

- (H8)

+(H3)

+ (H5) - (H7)

2. Materials and method

Cultural Threat η3

+(H2)

- (H2)

Desired coexistence η5

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Contact ξ4

Rejection η6

+ (H2)

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Conservatism ξ3

Perceived immigrants η1

+ (H8)

Discrimination η4

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- (H6)

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simultaneously, through structural equation modelling, as explained below.

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The data analysed corresponds to the annual surveys on Attitudes towards Immigration, commissioned by the Spanish Observatory on Racism and Xenophobia (OBERAXE) to the Centre for Sociological Research (CIS) from 2007, to run the fieldwork. These are ‘face-to-

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face’ surveys that have maintained the same sample design (a longitudinal, but not panel study) and questionnaire design since 2007, tracking the same indicators longitudinally in Spanish nationals aged 18 years and over. For this study, the surveys from 2008, 2010 and 2012 were selected to verify whether the same model held true in three different contexts of immigration and economy, as has been described in Introduction. These are nationwide surveys, whose sample populations stood at 2,768 (2008), 2,800 (2010) and 2,464 (2012) Spaniards randomly selected, following a multistage sampling design stratified by clusters,

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with random, proportional selection of primary sampling units (municipalities), and individuals selected by random routes, and gender and age quotas5. 2.1. Method: description of the model, variables and their links The hypotheses were tested using Multigroup Structural Equation Modelling (MGSEM),

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as it provides a straightforward method of dealing with multiple interrelated relationships of dependence simultaneously in different samples. In MGSEM a given model is checked with each set of data in order to determine whether the relationships hypothesized in the model differ depending on the value of the moderator (the three surveys/time points: 2008, 2010,

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2012). As a structural equation modelling technique, the objective is also to verify a

theoretically-driven model linking indicators to latent variables (measurement model) and displaying functional relationships between latent variables (structural model). The model

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consists of four exogenous latent variables: qualification, insecurity, conservatism and contact.

1) Qualification (ξ1) is measured by two indicators: education (X1) and occupation (X2). Though these are related to economic position, it was decided to separate them to verify separately the effects of education and job training and those related to instability in

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employment and the economy, as regards perception of economic and cultural threat. Education (the reference variable) is a rough measure of skill; it stimulates critical thinking and makes people more open-minded (Hainmueller and Hiscox, 2010).

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However, education is not synonymous with either tolerance or economic well-being. As Jackman and Muha (1984) indicated, better-educated people are simply trained to avoid sounding bigoted when they express opposition to racial policies. Bearing in mind that

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the survey questions may not be eliciting respondents' true level of prejudice, the welleducated are expected to express less perception of threat and the consequent rejection of immigration than the less educated. 2) Job-related and economic insecurity (ξ2) is measured by three indicators: experience of unemployment (X3), economic situation (X4) and income (X5). Experience of

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For more information, please see the technical details of the surveys in the CIS database: 2008 (http://www.cis.es/cis/opencm/EN/1_encuestas/estudios/ver.jsp?estudio=9680), 2010 (http://www.cis.es/cis/opencm/EN/1_encuestas/estudios/ver.jsp?estudio=11344), and 2012 (http://www.cis.es/cis/opencm/EN/1_encuestas/estudios/ver.jsp?estudio=13244). The analytical reports of these nationwide surveys by Author and Other are published by OBERAXE in annual reports on the changing pattern of racism and xenophobia (www.oberaxe.es//).

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unemployment is the reference variable. Workers who feel insecure about their job or who experience income deprivation are expected to develop more negative outgroup attitudes. Attitudes towards immigration may be an expression of self-interested calculation, based on one's own personal economic position and anxiety over the economy and employment at large. As Becchettia et al., (2010) found, both job loss and a

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drop in income make natives perceive immigrants as a threat to their job situation or economic welfare. Nevertheless, Bauer et al., (2000) found no effect of unemployment on tolerance.

3) Conservatism (ξ3) is measured by two indicators: political ideology (X6) and

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religiousness (X7). Ample evidence has been presented that outgroup attitudes are closely associated with religiousness and right-wing voting (Semyonov et al., 2006). Political ideology (the reference variable) has a distinct role to play in explanations of public

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opinion, which is independent of self-interest or education (Citrin et al., 1990). The terms ‘liberal’ and ‘conservative’ act as abstractions that define the poles on a continuum of favourable or unfavourable judgments on public policy alternatives. Those on the conservative side of the political and religious spectrum are more likely to identify more strongly with the nation they live in and to reject immigration (Chandler and Tsai, 2001;

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McDaniel et al., 2011; Author and Other, 2015). Immigrants with different religions and cultural traditions could threaten their worldview. 4) Contact (ξ4) is measured by three indicators: having immigrant friends (X8), neighbours (X9) and co-workers or schoolmates (X10). ICT states that contact with individuals from

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other ethno-cultural groups may foster favourable attitudes towards them. As Pettigrew and Tropp (2006) show, even casual everyday contacts between members of different

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groups in neighbourhood, school or workplace settings predominantly lead to more positive intergroup attitudes. Contact with and knowledge about the outgroup will help diminish perceived threats and prejudice (Curseu et al., 2007). This is one of the most important situational influences on racism and xenophobia (Escandell and Ceobanu, 2009). Intercultural contacts contribute to a decreased perception of threat, which lead to positive attitudes towards immigrants (Pettigrew, 1998; Brown and Hewstone, 2005; Ward and Masgoret; 2006; Aberson and Gaffney, 2008). The model proposes paths linking six endogenous latent variables: perceived immigrants, economic threat, cultural threat, discrimination, coexistence and rejection of immigration. Although there are reasonable arguments to contend that pre-existing discriminatory attitudes 9

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might also affect threatened group interests, to date the weight of empirical evidence seems to be in favour of a unidirectional relationship of perceived threat with hostile attitudes towards outgroups (Riek et al., 2006; Schlueter and Scheepers, 2010). 1) Perceived immigrants (η1) are measured by two indicators: perceived number of those born elsewhere (Y1) and assessment of the number of immigrants (Y2). The way in which

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the size of the outgroup is perceived seems to have a greater influence on attitudes than the actual size of the immigrant population (Semyonov et al., 2004; Alba et al., 2005; Brade et al., 2008; Herda, 2010; Author, 2015). ‘Overestimating the level of immigration exacerbates the sense of threat and boosts restrictionist sentiment’ (Sides and Cintrin,

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2007, p. 477). The relationship between the perceived proportion of immigrants and exclusionary attitudes is mediated by perceived threats.

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2) Economic threat (η2) is measured by four indicators: immigrants take jobs away from natives (Y3), they reduce salaries (Y4), they receive more social benefits (Y5), and they receive more school aid (Y6). Labour market competition is not the only economic threat. Competition over social benefits is another factor: questions related to the impact of foreigners on the welfare of the economy. These beliefs may be stereotypical and serve as a basis for expectations regarding outgroups, leading to discrimination and rejection.

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3) Cultural threat (η3) is measured by three indicators: protest against building mosques (Y7), positivity of religious diversity (Y8), and preserving one’s own culture (Y9). A somewhat ethnocentric and protective attitude towards one’s own culture may be

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considered under the heading of cultural threat: seeing the newcomers as a menace to cherished cultural traditions and not recognizing the wealth of cultural and religious diversity. According to Stephan et al. (2009), the immigrant group’s desire to maintain its

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own culture would constitute a threat to the values of the host culture. 4) Discrimination (η4) is measured by three indicators: employment (Y10), education (Y11) and healthcare (Y12). In surveys, discrimination-based attitudes are captured by questions on the respondent’s preferences towards limiting the population of a particular minority or restricting certain jobs, welfare or rights to citizenship for the members of the minority (Raijman et al., 2003; Coenders et al., 2009; Levanon and Lewin-Epstein, 2010). This research selects three direct indicators of discrimination. These are related to the natives’ right to preferential treatment: priority access to jobs, education and healthcare. Discriminatory attitudes are seen as a result of threats posed to the individual or the group in the economic and the social arena (Blalock, 1967; Bobo, 1983; Scheepers et al., 2002). 10

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It is expected that the perception of threat rationalizes the exclusion of immigrants from access to citizenship rights: ‘denying to individuals or groups of people the equality of treatment which they may wish’, citing Allport’s (1954, p. 51) definition of discrimination. 5) Desired coexistence (η5) is measured by three indicators: the desire of marriage to an

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immigrant (Y13), of having an immigrant boss (Y14), and of living in the same building (Y15). These are the three types of possible coexistence with immigrants selected by this research, since they are indicative of social distance: desire not to have contact with

immigrants on a different scale of proximity. In surveys, prejudices are usually captured

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by questions on whether the respondent is opposed to interethnic marriage, or unwilling to socialise or work with people from the minority group (Tolsma et al., 2008). While having contact with immigrants may diminish perceived threats and foster favourable

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attitudes towards them, no desire to have contact (coexistence) is a subtle expression of rejection or negative attitudes towards them. In the current research, the ‘desired coexistence’ is considered an endogenous variable, in line with the progressive scale of rejection behaviours proposed by Allport (1954), while the ‘real contact’ is an exogenous variable (just like the studies of Brown and Hewstone (2005), Ward and Masgoret (2006)

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or Aberson and Gaffney (2008)). One can have immigrant neighbours o co-workers (‘real contact’) and not want to live with them (‘desired coexistence’). The quality of the real contact determines the extent to which ‘contact’ affects the feelings of threat (Pettigrew, 1998) and outgroup attitudes (Barlow et al., 2012; Hewstone, 2015).

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6) Rejection of immigration (η6) is measured by three indicators: attitudes towards deporting unemployed immigrants (Y16), assessment of immigration laws as being too tolerant

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(Y17), and negative assessment of immigration (Y18). As stated in the Introduction, this latent dimension includes indicators of the most blatant and unequivocal expression of rejection of immigrants: the desire to curb immigration. Table A.1 in the Appendix details the exact wording of the survey questions that correspond to the indicators analysed. The model was estimated by Multigroup Structural Equation Modelling (MGSEM) and Maximum Likelihood estimations (ML), using AMOS. It also includes 6 perturbance terms (ζ) and 28 measurement errors (non-explained residual variance: a combination of random and systematic error, plus some residual coming from a source other that the intended latent variable) for each indicator [delta (δi) for the 10 11

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indicators of the exogenous latent variables and epsilon (εj) for the 18 indicators of the endogenous latent variables]. Nevertheless, neither latent variables nor error terms and correlations between exogenous latent variables are included in figure 2, in order to improve the display of the model, though these are described in the following section. As testing for possible group differences on structural paths (MGSEM) was an aim of the

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current research, the first analytical step was to test for measurement equivalence using multigroup confirmatory factor analysis (MGCFA). Metric invariance is a requirement in a multi-group situation, when it is the aim of comparing structural relations (MGSEM). To the extent that an assessment measure is not operating equivalently across groups, any

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interpretation of between-group differences is necessarily open to question (Cheung and

Rensvold 2002; Byrne 2004; Byrne and Van der Vijver 2010). The second step was to test for equality of structural relations between the latent variables by MGSEM. The main question is

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whether the structural relations between latent variables is invariant among the three groups.

3. Results

3.1. Measurement model: testing for an invariant ten-factor model The measurement model defines relationships between observed variables and latent

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constructs. A ten-factor model (qualification, insecurity, conservatism, contact, immigrants, economic threat, cultural threat, discrimination, coexistence and rejection) was fitted to the three groups simultaneously. In AMOS, measurement invariance is checked by means of a

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series of hierarchical multigroup models in which the equivalence of a larger number of parameters among the groups is progressively established (constraining the parameters of the measurement model to be invariant across groups). The analysis initially considers a less

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restrictive model (unconstrained) and subsequently models with increasingly more restrictive conditions are added. The constraints are placed in a sequence of nested models. The hierarchical structure of the process ensures that if the most restrictive conditions are verified, then so too are the least restrictive ones (Wu et al., 2007) There are five levels within which confirmation of measurement invariance is required. First, the most basic form is configural invariance. It guarantees only that the factorial structure is the same for all the groups. It is tested by specifying the same measurement model across the groups. No equality constraints are imposed on the parameters. The first group (2008) serves as reference group. As measuring instruments are often group-specific in the way they operate, it is possible that baseline models may not be completely identical across 12

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groups (Byrne and Stewart, 2006). This configural model serves as the baseline against which all subsequent tests for equivalence are compared. Acceptable goodness-of-fit between this initial model and the multigroup data is imperative. ‘If this model does not fit the data, then measurement invariance does not hold at any level’ (Kline, 2011, p. 252) Second, testing for factor loading equivalence (measurement weight) or weak invariance

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across groups. It requires that equality constraints be specified for all freely estimated firstorder factor loadings. These constraints are assigned to all factor loadings except those related to Items fixed to 1.00 for purposes of model identification and latent variable scaling.

Invariance holds if goodness-of-fit related to this model is deemed to be adequate, and if there

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is minimal difference in fit from that of the configural model. Likewise, these same criteria hold in the testing of all subsequent invariance models. ‘On the other hand, given findings of non-equivalence related to particular factor loadings, one may proceed with subsequent tests

and Van der Vijver 2010, p. 110).

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for equivalence if the data meet the conditions of partial measurement equivalence’ (Byrne

Third, testing for the invariance of intercepts (measurement intercepts) or strong invariance. Equality constraints are placed on both the first-order factor loadings and on the observed variable intercepts (they must also be constrained equal across groups along with

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both the first- and second-order factor loadings). Fourth, testing for structural covariances imposes additional constraints on factor variances and covariances (analysis of covariance structure assumes that all observed variables are measured as deviations from their means; i.e., their means are equal to zero). And the final and most stringent test of measurement

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equivalence is testing for residual invariance (measurement residuals6: a combination of random measurement error and error that is specific or unique to a particular measuring instrument). It requires cross-group equality in factor loadings, intercepts, structural

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covariances and residual variances (Wu et al. 2007, p. 4). Nevertheless, according to Byrne and van der Vijver (2010, p. 111), ‘it is now widely accepted that this test for equivalence is not only of least interest and importance, but also somewhat unreasonable and indeed not recommended.’

Table 1 shows results of test for measurement invariance in the five models obtained by MGCFA. The traditional perspective of the invariance-testing evaluative process is based on chi-square difference (or ∆χ2) values. When models are nested, they can be compared in pairs by computing the difference in their overall χ2 values and the related degrees of freedom. If 6

In MGCFA ‘measurement residuals is short for variances and covariances of residual (error) variables in the measurement part of the model’ (Arbuckle 2013, p. 365).

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this value is statistically significant, in the comparison of two nested models, it suggests that the constraints specified in the more restrictive model do not hold (the models are not equivalent across groups). Nevertheless, ∆χ2 is very sensitive to sample size and in such large samples (2008=2,760; 2010=2,800; 2012=2,464)7, invariance should not be assessed using it, as there are almost bound to be significant differences, even when the magnitude of the

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differences is trivial. Looking at incremental changes in CFI (Comparative Fit Index), RMSEA (Root Mean Squared Error of Approximation) and TLI (Tucker-Lewis Index) across models is likely to be more informative. Cheung and Rensvold (2002) recommend ∆CFI as the best statistic to use when assessing measurement equivalence across multiple groups. A

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value of ∆CFI smaller than or equal to 0.01 indicates that a null hypothesis of equivalence should not be rejected. The differences in TLI up to 0.05 are considered trivial (Byrne, 2010). Respect to RMSEA, Browne and Cudeck (1993) suggest that a RMSEA of 0.05 or less

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indicates a close fit; a value of about 0.08 or less indicates a reasonable error of approximation; and a value greater than 0.10 would suggest that the model should be rejected. Employing this definition of close fit, PCLOSE gives a test of close fit while ρ gives a test of exact fit. And finally, AIC (Akaike Information Criteria) is a comparative measure between

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models, where the smallest value indicates the model with better fit and greater parsimony. Table 1. Goodness-of-Fit Statistics for Tests of Invariance χ2 (df)

Model

6948.41 (915) 7936.69 (951) 6868.63 (1007) 7194.99 (1115) 7295.59 (1171)

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1. Unconstrained 2. Measurement weights 3. Measurement intercepts 4. Structural covariances 5. Measurement residuals

ρ

.000 .000 .000 .000 .000

TLI

.920 .893 .935 .942 .946

∆TLI

CFI

(M1)

-.027 .015 .022 .026

∆CFI

RMSEA (ρ)

AIC

.029 (1.000) .034 (1.000) .027 (1.000) .026 (1.000) .026 (1.000)

7722.41 8638.69 7458.63 7568.99 7557.59

(M1)

.952 .934 .955 .951 .950

-.018 .003 -.001 -.002

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Results from the testing of metric invariance revealed a well-fitting model in terms of CFI= 0.952, and RMSEA= 0.029 in factor structure equivalence (Model 1). According to Hu and Bentler (1995), values greater than 0.95 on CFI and values lower than 0.06 on RMSEA indicate a good fit. As the unconstrained model was a good fit to the data, it was possible to proceed with the test of invariance of factor loadings. Compared to the baseline model 1, the factor loading equivalence (Model 2) fitted the data worse, though CFI indicated an

7

According to Hu and Bentler (1995, p. 96), ‘at larger sample sizes power is so high that even models with only trivial misspecification are likely to be rejected’ on the basis of χ2. In cases where the sample size exceeds about 200 respondents, a trivial divergence between the observed covariance matrix and the fitted model may result in the rejection of the specified model. The test may not be a good enough guide to model adequacy.

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acceptable fit (0.934) and RMSEA a good fit (0.034). ∆CFI= -0.018, barely exceeds the 0.01 criterion. The other models fitted data better. Therefore, the assumption of measurement invariance across groups was not rejected. The factor structure in the MGCFA was the same in the three groups, but the loadings were not completely invariant between them. However, the deviations were, with some exceptions, modest. Accordingly, we decided to retain Model

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3 (measurement intercepts) as the measurement model for the subsequent structural part of the study; CFI value was the greatest (0.955) and AIC, the smallest (7458.63), indicating the model with better fit and greater parsimony.

Detailed outcomes for Model 3 are included in Table 2. This table reports lambda

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coefficients (λij), which correspond to each indicator that saturates in each latent variable (standardized factor loadings), as well as the proportion of variance explained of each observed variable (squared multiple correlations: R2). The indicators with greatest weight in

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each exogenous latent variable were: education (in qualification), income (insecurity), religiousness (conservatism) and friends (contact). With respect to latent endogenous variables, they were: assessment (perceived immigrants), they take away jobs (economic threat), against mosques (cultural threat), school (discrimination), marriage (desired coexistence), and deporting immigrants (rejection); with no significant difference in the three

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surveys, as shown in table 2. Construct reliability values for reference group (2008) are also included as a footnote. According to Hair et al. (2005, p. 777-778), ‘coefficient alpha remains a commonly applied estimate although it may understate reliability. Different reliability coefficients do not produce dramatically different results, but a slightly different construct

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reliability (CR) is often used in conjunction with SEM models.’ A value of 0.70 or higher suggests good reliability. This was case of the endogenous constructs: economic threat (0.74),

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discrimination (0.78), coexistence (0.76), and rejection (0.77). Reliability between 0.60 and 0.70 may be acceptable provided that other indicators of a model’s construct validity are good: qualification (0.68), insecurity (0.66), contact (0.62), and cultural threat (0.68).

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ACCEPTED MANUSCRIPT Table 2. Measurement Model Standardized Factor Loadings 2008 2010 2012 .80 .62 .47 -.60 -.82 .63 .55 .79 .53 .56

.81 .63 .47 -.62 -.84 .64 .56 .73 .51 .51

Education Occupation Unemployment Economic situation Income Ideology Religiousness Friends Neighbours Co-workers

.65 .37 .21 .35 .65 .36 .28 .55 .24 .28

.64 .38 .22 .37 .67 .39 .30 .62 .28 .31

.65 .39 .22 .38 .71 .41 .31 .54 .26 .26

.45 .77 .74 .58 .60 .62 .72 -.52 -.67

.44 .75 .74 .58 .59 .65 .74 -.56 -.67

.41 .69 .78 .60 .63 .69 .76 -.55 -.69

Number Assessment Take jobs Reduce salaries More aids More school aids Against mosques Positive religious diversity Keep up their culture

.20 .59 .55 .34 .37 .38 .52 .27 .45

.19 .56 .55 .34 .35 .42 .55 .31 .45

.17 .48 .61 .36 .40 .48 .58 .30 .48

.70 .80 .77 .75 .71 .64 .87 .58 .79

.71 .78 .77 .78 .72 .67 .88 .60 .82

Employment School Healthcare Marriage Boss Living in same building Deport immigrants Tolerant laws Negative immigration

.48 .58 .56 .58 .52 .45 .71 .30 .59

.49 .64 .59 .56 .50 .41 .76 .34 .62

.50 .61 .59 .61 .52 .45 .77 .36 .67

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.69 .76 .75 .76 .72 .67 .84 .55 .77

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.81 . 61 .46 -.59 -.81 .60 .53 .74 .49 .53

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Exogenous variables: Education ← Qualification Occupation ← Qualification Unemployment ← Insecurity Economic situation ← Insecurity Income ← Insecurity Ideology ← Conservatism Religiousness ← Conservatism Friends ← Contact Neighbours ← Contact Co-workers ← Contact Mediation variables: Number ← Immigrants Assessment ← Immigrants Take jobs ← Economic threat Reduce salaries ← Economic threat More aids ← Economic threat More school aids ← Economic threat Against mosques ← Cultural threat Positive religious diversity ← Cultural threat Keep up their culture ← Cultural threat Dependent variables: Employment ← Discrimination School ← Discrimination Healthcare ← Discrimination Marriage ← Coexistence Boss ← Coexistence Living in same building ← Coexistence Deport immigrants ← Rejection Tolerant laws ← Rejection Negative immigration ← Rejection

Squared Multiple Correlations 2008 2010 2012

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Notes: All coefficients are significant at ρ < 0.001 Construct reliability values for reference groups (2008): qualification (0.68), insecurity (0.66), conservatism (0.48), contact (0.62), immigrants (0.55), economic threat (0.74), cultural threat (0.68), discrimination (0.78), coexistence (0.76), and rejection (0.77)

3.2. Structural model: moderation effects The next analytical step was to perform MGSEM to test the hypotheses detailed in Introduction. Specifically, the analytic procedure involved a sequence of nested models. First, all the parameters in the structural part of the model were estimated freely with the specified M3 measurement model as a base for the latent variables, with all the factor loadings constrained to be equal in the three groups (measurement equivalent model). Second, a model with some structural paths constrained (structural paths which link latent exogenous variables with the endogenous constructs). Third, a model with all the structural paths constrained (structural weights). Fourth, all of the above and the structural covariances were constant 16

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across groups (structural covariances). Fifth, all of the above and the structural residuals (the variance of zeta) were constant across groups. Sixth, all parameters were constant across groups. Multi-group analysis was used as a test of whether years (economic and migratory contexts) moderate the model. Because χ2 difference tests are susceptible to sample size, model fit was additionally assessed using CFI, TLI, RMSEA and HOELTER’s critical N.

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As shown table 3, most of the suitability-of-fit indices indicated a good, but not perfect, fit for the model obtained when all the parameters in the structural part of the model were

unconstrained (M1): CFI= 0.93, TLI= 0.91, RMSEA= 0.04 and HOELTER (0.01) =783

(indicating a good fit: in multigroup, 200 times the number of groups). When some structural

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paths were constrained (M2), neither ∆TLI (0.005) nor ∆CFI (-0.001) indicated that a null hypothesis of structural equivalence should be rejected. ∆χ2 was statistically nonsignificant (ρ= 0.41) suggesting that the specified equality constraints were tenable. A test of the

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additional constraints of M3 (all structural paths were constrained) yielded similar results (∆CFI= 0.001, ∆TLI= 0.001, ∆χ2 is statistically nonsignificant: ρ= 0.276), indicating structural equivalence across the three groups. The other nested model comparisons obtained ∆χ2 values statistically significant at a probability of less than 0.05, suggesting that the equality constraints specified in these more restrictive models (M4, 5 and 6) did not hold

Table 3. Model Fit Summary Model

χ2 (df)

∆χ2 (∆df)

ρ

12235.32 (1079) 12262.75 (1105) 12270.29 (1111) 12306.24 (1131) 12328.60 (1141) 12427.40 (1197)

27.43 (26) 7.54 (6) 35.95 (20) 22.36 (10) 98.80 (56)

.412 .276 .016 .013 .000

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1. Unconstrained 2. Partial structural weights 3. Structural weights 4. Structural covariances 5. Structural residuals 6. Measurement residuals

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across the three groups.

TLI .905 .910 .911 .914 .916 .926

CFI

RMSEA (ρ)

929 .928 .929 .928 .928 .928

.036 (1.000) .035 (1.000) .035 (1.000) .035 (1.000) .035 (1.000) .034 (1.000)

HOELTER .01 783 798 803 815 820 851

Figure 2 offers an overview of significant structural paths for the group specific model (M1: unconstrained) in the three groups. In the final model four structural paths included in figure 1 were eliminated for being statistically nonsignificant (ρ > 0.05): perceived immigrants ←insecurity; cultural threat←insecurity; economic threat← conservatism; immigrants ←contact. The final structural model is also described in table 4, which details its main components in the three groups. As a MGSEM, results from the three groups were compared to see how similar they are. T-tests (available critical ratio test in AMOS) were conducted to pinpoint which paths were significantly different across groups. Table 4 17

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provides the results from the t-tests. The groups in the parentheses show the significant differences on the corresponding structural coefficient and their critical ratio (for testing the hypothesis that some two model parameters are equal in the population). There were significant differences on the direct effect of qualification and conservatism on perceived immigrants; contact on economic threat; qualification, conservatism, contact and immigrants

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on cultural threat; economic threat and cultural threat on discrimination; and economic threat on coexistence.

Qualification ξ1

.24, .25, .20

Economic Threat η2

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Figure 2. Final structural model

.64, .66, .71

Discrimination η4

Insecurity ξ2

-.44, -.50, -.45

.98, .97, .94 .11, -.23, -.17

Conservatism ξ3

.34, .34, .35

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.12, .09, .08

.31, .31, .25

Perceived immigrants η1 .73, .82, .73

-.10, -.16, -.18 .17, .19, .11

Cultural Threat η3

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Contact ξ4

-.26, - .26, -.35

-.25, -.14, -.25

Rejection η6

.47, .52, .45

.28, .29, .29

-.87, -.92, -.90

Desired coexistence η5

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In line with H1, the structural model corroborated that in times of economic crisis, perceived economic threat is the factor that most determines discrimination and rejection of

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immigrants. Although the most overt or blatant rejection of immigration was found to increase as the two perceived threats increased, the effect was somewhat more marked in the case of economic threat (β62 = 0.34, 0.34, 0.35) than in cultural threat (β63= 0.28, 0.29, 0.29). Both structural paths were invariant across groups. Particularly of note was the scale of the effects of economic threat on discrimination (β42= 0.64, 0.66, 0.71), coupled with the negative and higher effect of the cultural threat on the desired coexistence with immigrants (β53 = -0.87, -0.92, -0.90), corroborating H2: cultural threat most restricts any desire to coexistence with them. Cultural threat also affected discrimination (β43 = 0.47, 0.52, 0.45), although with a lesser effect than in the case of coexistence (a bit higher in 2010, too) and lower than shown by the economic threat. The 18

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economic threat also reduced the desire for coexistence, however its negative effect was minor (β52=-0.25, -0.14, -0.25). Except the direct effect of cultural threat on coexistence, the other three structural paths were significantly different across groups. The effect of perceived economic threat was somewhat greater in the worst years of the recession (lower GDP and higher unemployment rate in 2012: -2.1 and 26.02%, respectively), corroborating H1, while

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the effect due to cultural threat increased in 2010, falling slightly in 2012, which contradicts H2, but only in the case of its effect on discrimination. The effects of perceived cultural threat on both the desire for coexistence and the most blatant rejection of immigrants were statistically invariant across groups.

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The resulting model also corroborated H3. According to GCT, the perceived immigrant population was what most determined both threats. Its effect on economic threat was almost total (β21= 0.98, 0.97, 0.94), obtaining the highest standardized regression weights in the three

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surveys. Although the effect was somewhat greater in 2008, as it was expected8, the differences between groups were nonsignificant. On the other hand, the effect of the perceived immigrant population on cultural threat was also very important and significantly different across groups: somewhat more so in 2010 (β31= 0.82) than in 2008 (β31= 0.73) and 2012 (β31= 0.77). This changing pattern may be explained by the rise islamophobia recorded in Spain in

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2010, when political and media-based debates on Muslims increased. In 2012 the debates over banning the burqa in public places and the downturn in multiculturalism decreased in Spain and other European countries, as did islamophobia, albeit without reaching the figures obtained before 2010 (Author, 2015).

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H4 was also corroborated. Economic and job insecurity did have a positive effect on the economic threat, albeit small and statistically invariant across groups (γ22= 0.12, 0.09, 0.08).

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This may be due to the worse economic climate in 2010 and 2012. As Billiet et al. (2014) noted, in times of economic crisis the gap between the employed and unemployed narrows (strong feelings of economic insecurity and the resulting levels of perceived economic threat may also be felt by employed natives). As was indicated in figure 2, the path cultural threat←insecurity was eliminated from the final model due to being non-significant (ρ> 0.05) The fact that cultural threat is greater mostly among the ideologically conservative (H5) was also corroborated, but with an equally mild effect and less pronounced in the most recent poll (γ33= 0.17, 0.19, 0.11), being the difference between 2010 and 2012 significant. 8

In 2008 the crude rate of net immigration was at its highest (9.5 per 1,000 inhabitants; 1.3 in 2010; -5.1 in 2012), according to Eurostat, and the perception of the number of immigrants as ‘too many’ was the greatest (46%; 45 in 2010; 39 in 2012) in OBERAXE-CIS surveys.

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Similarly, the path economic threat←conservatism was removed from the final model for being non-significant, as it may be seen in figure 2. Qualification was the second variable that most affected the sense of economic threat, although only to a small extent and, contrary to expectations, positive (γ21= 0.24, 0.25, 0.20) and statistically invariant across groups: increases in scales of natives’ qualification did not

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decrease, but rather increased slightly, the sense of economic threat. These results contradict the hypothesis that economic threat increases as the level of qualification falls (H6), although the effect was weak. It appears that the economic recession contributes to the better qualified population feeling economically threatened by immigrants. In contrast, the effect of

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qualification on the perception of cultural threat was negative and in the hypothesised

direction: cultural threat increased as the level of qualification fell (γ31=-0.11, -0.23, -0.17), being the difference on the standardized coefficients significant between 2008 and 2010 (the

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year of greatest islamophobia). This fits in with the greater recognition of the richness of cultural diversity from immigration that the better qualified declared in surveys (Author and Other, 2015).

The effect of qualification on the perception of the presence of immigrants also went in the predicted direction: a greater perception as natives’ qualification levels decreased. Moreover,

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this effect was greater in scale (γ11=-0.44, -0.50, -0.45) and with significant differences across groups. As in the case of conservatism, the second variable that affected the perception of the immigrant population: the perceived presence increased as one moved towards more conservative positions (γ13= 0.31, 0.31, 0.25). Consequently, we may conclude that the direct

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effect of qualification on economic threat was contrary to expectations. Not so, the indirect effect on both threats through the perception of immigrant population, which was as

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hypothesised and slightly more pronounced than the former. H8 was also corroborated, but only in the case of qualification and conservatism; the effects of both insecurity and contact were non-significant and deleted from the final model (Figure 2). The effect of qualification on both threats and the effect of conservatism on cultural threat appeared to be more indirect (through the perceived immigrant population) than direct. The fact that their perceived presence was greater than their actual presence contributed to increasing both feelings of threat, even in groups of population which are traditionally more favourable to immigration, as are the highly qualified. For both threats the effect was not greater in 2008 (the year of highest net migration), but in 2010 (the year of

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greatest economic and job-related uncertainty and greatest islamophobia, being the differences on cultural threat significant across groups). Finally, the resulting model also corroborated H7. Contact with immigrants was the second and third variable that most affected both threats. Its effect was consistent with ICT: increasing contact reduced the sense of threat and the consequent rejection of immigration.

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Unlike perceived presence, contact with immigrants had a greater effect on reducing cultural threat (γ34=-0.26, -0.26, -0.35) than economic threat (γ24=-0.10, -0.16, -0.18) in all three

surveys and with significant differences across groups. Therefore, contact with immigrants

Table 4. Structural Model

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Standardized Regression Weights 2008 2010 2012 -.44 -.50 -.45

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seems to affect the dimensions of culture and coexistence more than the economic dimension.

Correlations between exogenous latent variables 2008 2010 2012

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Immigrants ← Qualification Qualification ↔ Insecurity -.74 -.73 -.73 (2008, 2010: -1.98) Qualification ↔ Conservatism -.29 -.32 -.26 Immigrants ← Conservatism .31 .31 .25 Qualification ↔ Contact .31 .37 .33 (2008, 2012: 7.21) Insecurity ↔ Conservatism .19 .16 .10** Economic threat ← Qualification .24 .25 .20 Insecurity ↔ Contact -.22 -.20 -.11* Economic threat ← Insecurity .12* .09** .08** Conservatism ↔ Contact -.35 -.36 -.46 Economic threat ← Contact -.10 -.16 -.18 (2008, 2012: -2.04) Squared Multiple Economic threat ← Immigrants .98 .97 .94 Correlations Cultural threat ← Qualification -.11* -.23 -.17 (2008, 2010: -.2.41) 2008 2010 2012 Cultural threat ← Conservatism .17 .19 .11* Immigrants .37 .44 .33 (2010, 2012: 1.97) Economic threat .94 .82 .87 Cultural threat ← Contact -.26 -.26 -.35 Cultural threat .77 .79 .77 (2008, 2012: -2.25) Discrimination .75 .69 .76 Cultural threat ← Immigrants .73 .82 .77 Coexistence .57 .57 .61 (2008, 2010: 2.23) Rejection .35 .34 .37 Discrimination ← Economic threat .64 .66 .71 (2008, 2012: 2.07) Discrimination ← Cultural threat .47 .52 .45 (2010, 2012: 2.68) Coexistence ← Economic threat -.25 -.14* -.25 (2008, 2010: -1.98) Coexistence ← Cultural threat -.87 -.92 -.90 Rejection ← Economic threat .34 .34 .35 Rejection ← Cultural threat .28 .29 .29 Notes: Significant coefficients at ρ < 0.001; *ρ< 0.01; **ρ< 0.05. Groups in the parentheses show the significant difference on the corresponding structural coefficient and their critical ratio.

Table 4 details each gamma (γ) and beta (β) coefficient. As these are standardized coefficients, they express the change (in units of standard deviation) in the variable indicated by the arrow for each unit of change in the variable which the arrow comes from. Table 4 also 21

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shows the correlations between exogenous latent variables and the squared multiple correlations of each endogenous latent variable. In the first case, as was expected, there was a significant negative correlation between qualification and insecurity (r= -0.74, -0.73, -0.73): the more highly skilled, the less insecurity. Equally negative was the correlation between qualification and conservatism, though less pronounced (r= -0.29, -0.32, -0.26): conservatism

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decreased as the level of qualification of the respondent increased, although somewhat less in 2012 (coinciding with the worsening economic crisis). Instead, qualification correlated

positively with contact (r= 0.31, 0.37, 0.33) increasingly, as skill level rose. This positive relationship of education with contact is consistent with the finding that the more highly

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educated commonly hold more tolerant worldviews and values, and are less reluctant to engage in intergroup contact (Vogt, 1997; Author and Other, 2015). Contact, however,

correlated negatively with conservatism, and somewhat more so in 2012 (r= -0.35, -0.36, -

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0.46), showing more contact between the less conservative. All these correlations were as expected and consistent with previous studies: greater conservatism is detected in older people, characterized by a lower level of qualification and less contact with immigrants (Author and Other, 2015). The narrowest correlation was between conservatism and insecurity (r= 0.19, 0.16, 0.10), losing significance in 2012.

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Regarding the squared multiple correlations of endogenous latent variables, economic threat was the variable with the greatest proportion of variance explained by its predictors (R2= 0.94, 0.82, 0.87). This was followed by cultural threat (R2= 0.77, 0.79, 0.77) and discrimination (R2= 0.75, 0.69, 0.76). In contrast, the perceived immigrant population (R2 =

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0.37, 0.44, 0.33) and rejection (R2= 0.35, 0.34, 0.37) were the latent variables with the lowest proportion of variance in the three surveys; the explanation of this involves other predictors

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not included in the model. 4. Conclusions

This paper extends previous research on xenophobia by examining, simultaneously, the mediating role of economic and cultural threats in explaining the rejection of immigrants, as well as the moderating effect of economic and migratory context highlighted by GCT. Three large nationwide surveys were analysed, tracking the same indicators longitudinally and applying MGSEM, in order to test the possible effects ought to changes in immigration flows and unemployment rates in a period of economic recession. Few significant variations were obtained. The main changes corresponded to the increase in the effect of economic threat on

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discrimination in the worst years of the recession (lower GDP and higher unemployment rate in 2012: -2.1 and 26.02%, respectively), corroborating GCT, while the effects of cultural threat were higher in years of increasing political and media-based debates over banning the burqa and the downturn in multiculturalism (2010). In 2011, and especially in 2012, these debates lost their momentum (Author, 2015) and the economic situation took the leading role

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once again in explaining xenophobia, as it is expected in a context of economic recession (consistent with GCT).

The first main finding was the different effect of both threats on the three dimensions of xenophobia analysed. While the perceived economic threat showed to lead most to supporting

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discriminatory policies, defending the right of preference for natives versus immigrants, the sense of cultural threat was more related to the component of social interaction, the desire for coexistence with immigrants, and to a greater extent. This result is in line with previous

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research (Hainmueller and Hopkins, 2014), which stated that personal and national economic outlook play only a small role in predicting xenophobia, and support the proposal that the perceived cultural threat (by evoking affective responses) leads more to prejudice and avoiding coexistence with immigrants than to discrimination. On the contrary, the effect of economic threat on discrimination, and on the most clearly expressed dimension of rejection

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of immigration, followed an upward trend in line with the context of economic recession and the rise in unemployment figures. The effect of cultural threat on the most blatant rejection of immigrants followed a similar trend, albeit to a lesser extent. Other novel contribution was testing the impact of individual differences (job and

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educational qualifications; job-related and economic insecurity; and conservatism) and situational factors (contact with immigrants) on both threats, and the mediating role of the perceived presence of immigrants in their effect on both threats. The integrative model

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proposed by Ward and Masgoret (2006) only included ‘multicultural ideology’ as a latent variable for individual differences, and in ITT (Stephan et al., 2009) four threats were posited to mediate relationships between antecedents and attitudes. The second main finding of the current study was, precisely, corroborating that the perceived presence of immigrants is likely what most affects both sense of threat, particularly the economic threat (consistent with GCT). Moreover, the perception of larger size seems to increase the feeling of threat in those who are traditionally favourable to immigration: people with better qualifications, less insecurity, the less conservative, and those who have contact with immigrants. Nevertheless, the effects due to economic and job-related insecurity were less pronounced and those resulting from qualification were contrary to what was hypothesized. Although 23

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qualification was the second variable that most affected the sense of economic threat (only to a small extent), its effect was positive, coupled with the fact that qualification also had a lesser causal effect on the cultural threat. This may be due to what Billiet et al. (2014) also confirmed: in times of major recession the gap between the employed and unemployed narrows (strong feelings of economic insecurity may also be felt by the employed). Or, as

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Stephan et al. (2009) observed, though in general low power groups are more likely than high power groups to experience threat, high power groups (to the extent that they perceive that they are threatened) will react more strongly to threat because they have a great deal to lose. This idea finds support in research showing that the relationship between threat and

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intergroup attitudes is stronger for high power groups than for low power groups (Riek et al., 2006).

Though the perceived economic threat was greater in economically vulnerable groups (the

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unemployed and those with lower income), corroborating previous research (Scheepers et al., 2002; Mayda, 2006; O’Rourke and Sinnott, 2006; Lancee and Pardos-Prado, 2013), its causal effect lost significance with a deeper recession. The notorious increase in the unemployment rate in Spain from 2008 (11.34) to 2012 (26.02%), also affecting people with the highest jobrelated and academic qualifications, increasing their sense of economic threat, may be the

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explanation for the inverse relationship obtained. Nevertheless, this explanation requires new research in favourable economic contexts of lower unemployment rates and higher GDP. The same is the case for conservatism: cultural threat was greatest mostly among those who were ideologically conservative (in keeping with SIT), but its effect was not major and lessened as

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recession progressed. The economic crisis seems also to have contributed to narrow the gap between the most conservative and liberal positions. In relation to contact with immigrants, the resulting causal model corroborates that

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increasing contact reduces the sense of both threats and their consequent rejection of immigrants. As Pettigrew and Tropp (2006) showed, even casual everyday contacts between members of different groups in neighbourhood, school or workplace settings predominantly lead to more positive intergroup attitudes. Unlike the perceived presence of immigrants, contact had a greater causal effect on reducing cultural threat than economic threat. Its effect is consistent with ICT, affecting the dimensions of culture and the desire for coexistence with immigrants more than economic concerns. As the current research is correlational, the direction of causality cannot be conclusively established. The analyses were based on non-experimental survey data, although in three time points. Nevertheless, there is a sound rationale, supported by theories and research 24

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(summarized in the Introduction) behind the model whose adequacy has been checked9. A worsening economic scenario seems to have a greater effect on economic threat than cultural, where more factors come into play, such as political discourse and media coverage of minorities and immigrants. These aspects will need to be confirmed in specific research at

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some future date.

Acknowledgements

This work was supported by a grant from the Spanish Ministry of Finance and Competition (blinded for review); a research project that has continuity in another on the measurement of

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discussed at the (blinded for review) conference

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multiple discrimination (blinded for review). Some of the results were presented and

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ACCEPTED MANUSCRIPT Table A.1. Description of the variables observed in the analysis

X8 Friends X9 Neighbours X10 Co-workers Y1 Number Y2 Assessment Y3 Take jobs Y4 Reduce salaries Y5 More aids Y6 More school aids Y7 Against mosques Y8 Positive religious diversity Y9 Keep up their culture

Y10 Employment Y11 School Y12 Healthcare

Q30. Among your neighbours, are there any who are immigrants or foreigners? Among your coworkers or fellow students? Among your friends? Yes (1) No (2). Q7. I would like you to tell me, for every 100 people living in Spain, how many do you think were born outside Spain? Q9. In your opinion, is the number of immigrants currently in Spain...? Not enough (1) Acceptable (2) High (3) Too many (4). Q26. Immigrants take jobs away from Spaniards; By accepting lower wages, immigrants drive down wages: Strongly agree (1) Tend to agree (2) Tend to disagree (3) Strongly disagree (4). Q20. In your opinion, do immigrants get from the state...? Much more than they put in (1) More than they put in (2) As much as they contribute (3) Less than what they contribute (4) Much less than they contribute (5). Q19. Although they have the same income, more school aid is given to immigrants than to Spaniards: Strongly agree (1) Tend to agree (2) Tend to disagree (3) Strongly disagree (4). Q27. In general, considering all possible cases. Does it seem to you very, fairly, not very or not at all acceptable for people to protest against the building of a mosque in their neighbourhood? Very acceptable (1) Fairly (2) Not very (3) Not at all (4). Q14. On a scale of 0-10, where 0 indicates ‘very negative’ and 10 ‘very positive’, assess the desirability of Spanish society being made up of people of different religions. Q24. Which of the following statements do you most agree with? Even if they learn our culture and customs, it is also good for immigrants to preserve their own culture and customs (1) Immigrants should be able to keep up only the aspects of their culture and customs which will not upset the remainder of Spaniards (2) Immigrants should forget their culture and customs and adapt to Spanish ones (3). Q27. In general, bearing in mind all possible cases, do you consider it very, fairly, not very or not at all acceptable, when hiring a person, that a Spaniard should be preferred to an immigrant? Very acceptable (1) Fairly (2) Not very (3) Not at all (4). Q19. Spaniards should have precedence when choosing their child's school; Q18. Spaniards should have preference in access to health care: Strongly agree (1) Tend to agree (2) Tend to disagree (3) Strongly disagree (4). Q29. Now I am going to read a series of possible relationships between yourself and immigrants. Please tell me in each case whether you accept or would accept that kind of relationship, you would try to avoid it or reject it: your son/daughter marries an immigrant; an immigrant is your boss at work; living in the same block as immigrants: Would accept (1) Would try to avoid it (2) Would reject (3) It depends (4). Q26. If someone who comes to live and work here remains unemployed for a long time, they should be deported: Strongly agree (1) Tend to agree (2) Tend to disagree (3) Strongly disagree (4). Q13. In your opinion, do you consider that the laws governing foreigners’ entry and residence in Spain are too tolerant, fairly tolerant, appropriate, rather harsh; or too harsh? Too tolerant (1) Fairly tolerant (2) Appropriate (3) Rather harsh (4) Too harsh (5). Q28. Generally speaking, do you believe that immigration is very positive, positive, negative or very negative for this country? Very positive (1) Positive (2) Negative (3) Very negative (4) Not positive or negative (5).

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Y13 Marriage Y14 Boss Y15 Living in same building

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X7 Religiousness

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X6 Political ideology

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X4 Economic Situation X5 Income

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X2 Occupation X3 Unemployment

Survey Question * Q50a. What is the most advanced level of official course you have studied (regardless of whether or not you completed it)? Q57. And what is/was your current/last job or occupation? Q54 In the last five years have you ever been unemployed or threatened with losing your job? Yes (1) No (2). Q52. How would you define your personal economic situation? Very good (1) Good (2), Neither good nor bad (3) Bad (4) Very bad (5). Q60. Currently, taking together all members of the household (including the respondent) and all items, how much net income do you have on average per month in your home? Less than or equal to €300 (1) From €301-€600 (2) From €601 to €900 (3) From €901-€1200 (4) From €1201-€1800 (5) From €1801€2400 (6) €2401to €3000 (7) from €3001-€4500 (8) from €4501-€6000 (9) Over €6,000 (10). Q40. When talking about politics we normally use the expressions, left and right. On this card there are a number of boxes that go from left to right. Which box would you place yourself in? Left (0).... Right (10). Q51a. Do you consider yourself...? Very religious (1) Fairly religious (2) Some religious (3) Not at all religious (4).

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Variable X1 Education

Y16 Deport immigrants Y17 Tolerant laws Y18 Negative immigration

*This is the exact wording of the question in the questionnaire. To run the analysis all the variables were recoded to process them metrically, following the procedure described in Sofroniou and Hutcheson (1999), and Author (2002).

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Highlights • Different explanations of anti-immigrant attitudes are tested, by means of multigroup structural equation modelling, in three surveys.

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• The mediating role of both economic and cultural threat is examined in three dimensions of xenophobia. • Economic threat leads more to support discriminatory policies and the rejection of immigrants. • Culture threat curbs any desire for coexistence with immigrants.

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• Both threats are determined by the perceived presence of immigrants.

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• Both Group Conflict and Intergroup Contact Theory are corroborated.