Civilizing vs destructive globalization? A multi-level analysis of anti-immigrant prejudice

Abstract

This study investigates the impact of the latest wave of globalization on anti-immigrant prejudice. We discern and test two contradictory accounts of the impact of globalization on anti-immigrant prejudice from the prejudice and globalization literatures. On the one hand, there is the ‘civilizing/integrative globalization’ thesis, which implies that globalization should help to decrease prejudice by creating sustained and equal contact between previously alien cultures and peoples, and by spreading economic gains to everybody. On the other hand, there is the ‘destructive globalization/globalization as a threat’ thesis, which argues that globalization should increase anti-immigrant prejudice by intensifying competition over resources and by increasing perceived threat by native populations as a result of increasing immigrant populations. We test these two accounts using a multi-level analysis of 64 countries and nearly 150,000 individuals, derived from the World Values Surveys (waves 3–5). Our analyses reveal support for ‘destructive globalization/ globalization as a threat’ thesis, but emphasize the multi-dimensional character of globalization. We find that citizens of countries with higher levels of trade openness have significantly more anti-immigrant sentiments. There is also some evidence that in countries where unemployment is accompanied by high levels of trade openness or the existence of large immigrant populations, citizens hold high anti-immigrant prejudice. By contrast, foreign direct investment (FDI) has a weak effect.

Keywords

anti-immigrant prejudice civilizing globalization destructive globalization

1. Introduction

Although international migration has lagged behind the recent increase in international trade and investment flows, it remains one of the most significant, if not controversial, issues in the globalization literature. The impact of incoming immigrants on domestic populations and prejudice against them are debated widely in social sciences and the public sphere. The continuing increase of the number of immigrants also clearly displays the importance of this issue. Between 1970 and 2005, the total international migration stock increased from around 80 million people to over 190 million (Lucas, 2008).¹

There is a seemingly endless literature on the integration of national economies under globalization in recent decades. Scholars observe that the ongoing integration of national economies since the 18th and 19th centuries intensified in the last two or three decades (Held et al., 1999). Many emphasize the increase in flows of capital, and goods and services across borders in recent decades (see Brady et al., 2007). Some skeptics of globalization claim that developments labeled as globalization are not historically unprecedented (e.g. Bairoch, 1996; Hirst and Thompson, 1996). Following Held and colleagues (1999), Robertson (1992), and Therborn (2000), we accept the fact that the integration of the world started before the late 20th century, but maintain the idea that the current era has distinctive properties.²

Despite the ever-growing interest and burgeoning literature on globalization, we do not know if and how globalization of national economies influenced prejudice against immigrants. Transnational migration is certainly not a new phenomenon and several periods in human history are marked with major population flows across countries (Levitt and Jaworsky, 2007). However, the increasing integration of national economies in recent decades and the concurrent mobility of people across borders create a compelling question about the effect of these on the prejudice against immigrants around the world. Although scholarly works and public debate on globalization and its impact on societies occupy significant space in the literature and media, the relationship between increasing economic openness and liberalization, and social and political attitudes in general and anti-immigrant prejudice in particular remains an unexplored area, which we address.

Using prejudice and globalization literatures, this study discerns and tests for two contradictory accounts regarding the impact of globalization on anti-immigrant prejudice from the literature: the ‘civilizing/integrative globalization’ and the ‘destructive globalization/ globalization as a threat’ theses. We perceive globalization as a dynamic process that affects the socioeconomic context in which citizens are exposed to different dimensions of economic interdependence. We operationalize globalization through the international flows of goods and services, capital, and labor. The results of our analyses indicate that the latest wave of globalization was a significant influence on anti-immigrant prejudice. We find that citizens of countries with higher levels of trade openness have significantly more anti-immigrant sentiments. There is also some evidence that in countries where unemployment is accompanied by high levels of trade openness or the existence of large immigrant populations, citizens hold high anti-immigrant prejudice. In contrast, foreign direct investment (henceforth, FDI) has a weak effect.

Overall, this study’s contribution to the literature is manifold. First, as stated above, this study investigates an important but overlooked effect of globalization on prejudice against immigrant populations. To our knowledge, this is the only study that examines the effect of globalization on attitudes towards immigrants. By doing so, we also contribute to the debate about the consequences of globalization between the proponents and opponents of globalization both in the academy and the public sphere (e.g. Bhagwati, 2004; Mander and Goldsmith, 2001). In addition, our contribution is to incorporate less developed countries. In recent years, there has been a significant increase in the cross-national analyses of prejudice against immigrants, mainly in Western Europe (see Ceobanu and Escandell, 2010; Pettigrew, 1998; Quillian, 2006; for reviews). We believe that the literature can benefit from the inclusion of less developed countries into the cross-national analyses of prejudice. The existing literature documents a significant amount of anti-immigrant prejudice and xenophobia in less developed countries (e.g. Akokpari, 2005; Campbell, 2003). Less developed countries attract significant amount of migration, especially from neighboring less developed countries (Lucas, 2008). In addition, the level of economic development is presented as a significant determinant of prejudice against minorities and immigrants in the literature (e.g. Quillian, 1995; Semyonov et al., 2006). Including less developed countries into analyses of prejudicial attitudes against immigrants will provide a more rigorous test of the conclusions of the existing literature.

Therefore, this study analyzes the level of anti-immigrant prejudice with a sizable sample of developed and less developed countries from the World Values Surveys (henceforth, WVS). Our sample includes 148,759 individuals from 64 countries. In the following section, we review the debates on prejudice and the possible impact of globalization.

2. Past research

2.1. Cross-national studies of prejudice

There is a long line of research on prejudice in social sciences, which significantly enhanced our understanding of the phenomenon although it did not always produce uniform answers. A dominant line of thinking in the study of prejudice has been the ‘group-threat’ and ‘competition’ theories, which argue that the size of the minority groups and economic conditions and competition over resources shapes the prejudice against them (Blalock, 1957; Blumer, 1958). An equally prolific approach, the ‘contact theory’ contends that increase in the size of minority groups or immigrants creates opportunities for sustained and personal contact between the members of dominant and minority groups thus reducing prejudice (Allport, 1954; Hewstone and Brown, 1986).

In addition, until recently, the bulk of the studies on prejudice examined the effect of social, demographic, and psychological characteristics of individuals in the development of such attitudes against the members of out-groups (Quillian, 1995). These studies effectively showed that individual-level factors such as age, income and education have a significant impact on the level of prejudicial attitudes of individuals. However, in the last decade or so, a growing body of scholars, proponents of both group threat and contact theories, considered both individual-level and group-level characteristics to examine prejudicial attitudes in different regional and national contexts (e.g. Quillian, 1995; Semyonov et al., 2006; Wagner et al., 2006).

In his influential account, Blumer (1958) argued that racial prejudice by the members of a dominant group towards a subordinate group builds on a feeling of superiority over the subordinate group, a feeling that the subordinate group is inherently different, and a perception that the subordinate group threatens the position of the dominant group. In his pioneering cross-national study of prejudice, Quillian (1995) shows that the level of the perceived threat measured by the size of the minority group and the economic situation of a country, as indicated by GDP per capita, determine the level of prejudice against racial minorities and immigrants in Western Europe. Scheepers et al. (2002) argue that people living in competitive economic conditions are more likely to see immigrants as threats and thus be more prejudiced.

In a similar study, Evans and Need (2002) analyze the attitudes towards minority rights in 13 Eastern European countries, arriving at similar conclusions.³ In a more recent study, Semyonov et al. (2006) find that the size of foreign population and unemployment significantly increases the level of prejudice in EU member countries between 1988 and 2000. In the case of developing countries, Zagefka and colleagues (2007) argue that economic competition and the perceived maintenance of their original culture by immigrants in Turkey fuels prejudice towards them. Akokpari (2005) explains how the rising immigration and increasing economic problems intensified anti-immigrant attitudes and encouraged anti-immigrant politics in the untypical immigrant recipient country of Lesotho, mainly due to its geographical location. Similarly, Campbell (2003) argues that perception of immigrants as a threat to the economic prosperity and national culture in Botswana led to xenophobia.

The contact theory that originates from Allport’s (1954) study also inspired significant amount of studies and attracted significant interest in recent years (Pettigrew et al., 2007; Wagner et al., 2006). In his groundbreaking study, Allport (1954) carefully distinguishes between casual and impersonal, and sustained and personal contact. He suggests that causal and impersonal contact will not have the same effect. He also adds that contact has to be between groups with equal status, and incorporate common goals and cooperation between groups.

There is also ample support for contact theory in the literature. Wagner and colleagues (2006) find that increasing size of minority groups, measured as the percentage of ethnic minorities in a district, creates opportunity for intergroup contact and reduces prejudice among the members of dominant group in Germany. Pettigrew et al. (2007) demonstrate that both indirect and direct contact with foreigners and Muslims decrease the prejudice of Germans against these groups. In a comparative study of European countries, McLaren (2003) shows that individuals who have no foreigner friends feel more threatened by them. In their analysis of residential segregation and inter-group contact in Europe, Semyonov and Glikman (2009) find that positive contact with minorities decreases prejudice, although they find no direct positive or negative effect of residential integration on the likelihood of positive contact.

Overall, the literature on prejudice provided significant evidence for both approaches. Many scholars find that a combination of contact and threat accounts helps to explain prejudice and argue that existence of immigrant or minority populations can at the same time both trigger feelings of threat and create opportunities for contact (e.g. Dixon, 2006; Tolsma et al., 2007). In addition, the recent surge in cross-national studies of prejudicial attitudes provides strong evidence that region and country-level factors beyond individual traits are also instrumental in shaping attitudes of dominant groups against the members of out-groups. The most recent wave of globalization is likely to impact prejudicial attitudes by creating opportunities for contact while also producing new threats. In the following section, we discuss how the increasing globalization of countries, especially economically, may influence attitudes towards immigrants and foreigners.

2.2. Globalization and the perception of immigrants

The increasing integration of the world since the mid-1970s that is known as globalization has become a focal point of interest and debate in the social sciences and popular discourse in recent years. Although theories of globalization project long historical trajectories, the locus of thinking on globalization has been the changes in recent decades. It is not possible to provide a comprehensive review of globalization literature here, thus we will briefly mention relevant studies.⁴

However, our reading of globalization literature reveals two seemingly contradictory accounts. On the one hand, there are intellectuals and scholars who perceive globalization as a force for connecting people all around the world to one another and enhancing economic growth and social welfare (e.g. Bhagwati, 2004; Friedman, 2006; McGrew, 1997). Partly following Guillen’s (2001) and Lizardo’s (2006) conceptualization, we call this approach the ‘civilizing/integrative globalization’ thesis.⁵ On the other hand, there are many intellectuals and scholars who see globalization as a destructive force, which worsens the economic and social conditions of the masses, polarizes the social structures and causes protest and backlash (e.g. Appadurai, 1998; Chomsky, 1998; Swank and Betz, 2003). Again following Guillen (2001) and Lizardo (2006), we label this approach the ‘destructive globalization/globalization as a threat’ thesis.

The ‘civilizing/integrative globalization’ thesis, which resonates with the contact theory in the prejudice literature, builds on the idea that the increasing economic welfare and exposure to democratic ideas, foreign people and cultures through globalization will help societies to be more tolerant. Although many proponents of this approach can be found in the popular literature (e.g. Friedman, 2006; McGrew, 1997); many scholars also project a positive influence of globalization in terms of preventing prejudice.

In one of the boldest defenses of globalization, Bhagwati (2004) argues that the recent wave of globalization improved living conditions of the people, helped women, and increased democratic participation in countries all around the world. Similarly, Bhalla (2002) argues that globalization increased the well-being of people overall and caused significant reduction in poverty and inequality. In the popular literature, Friedman (2006) makes very optimistic observations and predictions about the impact of increasing integration to the global economy on economic well-being, social modernization, and democracy in India. International organizations such as IMF and the World Bank use similar arguments when advocating economic liberalization and openness, especially in the developing world (e.g. World Bank, 2002). They present a positive picture of globalization where economic, social, and cultural benefits of globalization are realized together.

Overall, the ‘civilizing/integrative globalization’ account implies that globalization spreads economic gains to everybody and introduces previously alien cultures and peoples to one another, thus increasing interaction among them and enhancing tolerance. As discussed above, the contact theory emphasizes the impact of contact between dominant and out-groups on prejudice while competition and group-threat theories put great emphasis on the socio-economic context. If the ‘civilizing/integrative globalization’ account is correct, globalization should help to create sustained and equal contact between immigrants and native populations, and prevent the perception of increased threat by spreading economic gains to everybody. Thus, our first hypothesis is:

H1: Countries with higher levels of integration to the global economy should have less anti-immigrant prejudice.

In contrast to this optimistic account, the ‘destructive globalization/globalization as a threat’ thesis implies that the economic conditions and political climate created during the latest wave of globalization is a ripe environment for prejudice towards others. For example, Appadurai (1998) argues that globalization sharpens and threatens identities, and creates reactionary backlash all around the world. Barber (1995) contends that while globalization demands the integration of national economies, it pits cultures against one another and people against people.

Many people around the world attribute their declining fortunes to the impact of globalization. Scheve and Slaughter (2001) show that American workers perceive globalization, measured by international trade, investment, and migration, as a primary source of their economic troubles. Swank and Betz (2003) find that increasing international flows of trade and investment contribute to the electoral success of right-wing parties, which in turn promote strong anti-immigrant and protectionist agendas in Western European countries. As globalization increases job insecurities, especially among the sectors most vulnerable to ‘sending jobs overseas’ in developed countries, prejudice towards immigrants and foreigners grows (Scheve and Slaughter, 2004). Domestic groups are likely to perceive these new comers (with different ethnicity and language) as a threat to their socioeconomic well-being.

As for less developed countries, some argue that increasing international trade increased the exploitation of workers in less developed countries by transnational corporations (e.g. Fernandez-Kelley, 1983; Lee, 1998; Mander and Goldsmith, 2001). The effect of globalization on less developed countries also comes through intense privatization, in which public workers are laid off or lost their previous job security. As more foreign companies buy these previously public companies and enter the market, their increasing visibility not only increases nationalist discourse, but also anti-immigrant and anti-foreigner prejudice (Scheve and Slaughter, 2004). A recent study on Lesotho finds that immigration inflow and economic problems increased resentment against immigrants and inflamed anti-immigrant politics (Akokpari, 2005).

In the prejudice literature, competition and group-threat theories predict that worsening economic conditions and increase in the size of out-group populations inflame prejudice. Overall, if the ‘destructive globalization/globalization as a threat’ thesis account is correct, inequalities and economic troubles caused by globalization should increase anti-immigrant prejudice by intensifying competition over dwindling resources and by increasing perceived threat by native populations as a result of increasing immigrant populations. So, our second hypothesis is:

H2: Countries with higher levels of integration to the global economy should experience more scapegoating of immigrants and anti-immigrant prejudice.

Finally, the ‘destructive globalization/globalization as a threat’ thesis puts great emphasis on the increasing economic marginalization of the masses during the latest wave of globalization. Group-threat and competition theories established significant evidence that unemployment is an important factor determining the level of prejudice (Kunovich, 2004; Quillian, 1995; Semyonov et al., 2006). In countries with low unemployment, globalization may have no impact or even promote tolerance for immigrants. However, on the contrary, high levels of unemployment may aggravate the effect of globalization on anti-immigrant attitudes.

In the globalization literature, there is significant evidence that economic globalization, measured by international trade and investment, disproportionately hurts subsectors in national economies and employment these sectors (e.g. Brady and Denniston, 2006; Spence, 2011). While globalization creates job opportunities for some, it causes many to lose their jobs. If countries face increasing unemployment as their economies integrate into the global economy, we would see more prejudice toward immigrants that are seen as part of the threat of globalization. For example, a recent study suggests that high levels of unemployment might cause people to exaggerate the size of the immigrant population in a country (Citrin and Sides, 2008). In his analysis of municipalities in Sweden, Hjerm (2009) found that economic conditions, particularly the level of unemployment, are the determining factor in anti-immigrant prejudice. Palmer (1996) finds that opposition to immigration is highly correlated with unemployment in Canada. Strabac and Listhaug (2008) argue that cross-national variation in anti-Muslim prejudice in Europe cannot be explained with the size of Muslim populations, but the rate of unemployment. Akokpari (2005) and Campbell (2003) argue that anti-immigrant prejudice is highest among the unemployed in Botswana and Lesotho. Therefore, the impact of globalization on anti-immigrant sentiments in a country might be conditional upon the level of unemployment in a country. Thus, our third hypothesis is:

H3: The level of anti-immigrant prejudice should be higher in countries where high levels of integration to the global economy are accompanied by high levels of unemployment.

3. Data and methods

Our analysis of the impact of globalization on anti-immigrant prejudice relies on data from several sources at the individual and country levels. Individual-level data on anti-immigrant prejudice, income, education, age and other key variables come from the World Values Surveys rounds 3–5, conducted in 1995–2007.⁶ Our sample includes all countries where data were available for our dependent and independent variables at the individual level. We test our hypotheses in 64 countries at different time points. Please see the Appendix Table 1 for a list of countries and years of data collection. These countries show significant variation in terms of factors such as geographical region, the level of democracy, and economic development.⁷ There are a total of 122 country-years, 64 countries and 148,759 individuals in our sample.⁸

3.1. Dependent variable

The dependent variable derives from the WVS question of whether or not respondents would like to have ‘immigrants/foreign workers’ as their neighbors.⁹ This question is straightforward and directly measures how people feel about members of other nationalities who immigrate permanently or stay temporarily because of largely economic reasons. If a person would not like to have them as their neighbors it is coded 1, otherwise 0. One can say that the meaning of being neighbors varies from culture to culture and this variable primarily indicates social distance. However, we believe that our dependent variable goes beyond this and shows preferences by emphasizing immigrant status over other characteristics, which were addressed in the survey questionnaire, regardless of cultural context. In addition, this variable was also used in similar studies. In the literature, Quillian (1995) and Wagner and colleagues (2006) included questions about preferring immigrants as neighbors or not into their analysis or prejudice against them. In any case, we initially intended to compose an index from multiple questions that gauge anti-immigrant prejudice. However, the WVS asks only one question and we use it here. The advantage of the WVS, however, is that we can increase the number of countries and time points, and in particular can include less developed countries.¹⁰

Figure 1 presents the country-averages of people who would not want an immigrant or a foreign worker as their neighbors by survey year. As shown by the line fitted through the data points, the level of anti-immigrant prejudice seems to be increasing in recent years, although not every country was surveyed in each year. The graph also shows that there is significant variation across countries. The lowest anti-immigrant prejudice mean scores are .02, .03 and .04 in Sweden in 2006, Iceland in 1999, and Brazil in 1997 respectively. The highest anti-immigrant prejudice score belongs to Malaysia with .57 in 2006. South Korea, Thailand and Egypt follow this country with the mean scores of .47 in 2001, .42 in 2007 and .42 in 2001, respectively. Given that the average of country means scores for anti-immigrant prejudice across the globe is .19, this great variation begs for explanation. Appendix Table 1 presents the mean and standard deviation of anti-immigrant prejudice for all countries according to the survey year.

Figure 1.

Mean anti-immigrant prejudice score in 67 countries, 1995–2007.

3.2. Independent variables

3.2.1. Globalization variables

We operationalize globalization through the international flows of goods and services, capital and labor. Our first globalization variable is trade openness (exports as a percent of GDP + imports as a percent of GDP).¹¹ Data on exports and imports comes from the World Development Indicators Database of the World Bank (2008). Increasing trade openness might make domestic populations vulnerable to the global economy and stir up anti-immigrant prejudice (Scheve and Slaughter, 2001; Swank and Betz, 2003). Second, we add inward FDI stock as a percent of GDP to models. Incoming investments into a country might increase or decrease anti-immigrant prejudice to the extent that domestic populations perceive these investments beneficial or harmful to themselves (Scheve and Slaughter, 2004). Finally, we add international migration stock to the models. It is measured as the percent of migrants to the total population in a country (World Bank, 2008). The group threat and competition theories, and the contact theory put equal emphasis on the size of out-groups, although they have contradictory expectations regarding its impact (e.g. Quillian, 1995; Wagner et al., 2006). Appendix Table 2 presents the country-years with highest and lowest levels of globalization. We logged all globalization variables to control for outliers and skew.¹²

3.2.2. Individual-level variables

At the individual-level, we use the standard variables used by similar studies (e.g. Quillian, 1995; Semyonov et al., 2004). Most of these studies emphasize the importance of socio-economic resources and argue that high level of income and education are associated with more tolerance toward immigrants and members of other types of out-groups such as ethnic minorities. In particular, recent studies show that people with higher education place greater value on diversity and are more exposed to political and economic liberal discourse, thus supporting trade and immigration (Hainmueller and Hiscox, 2007; Mansfield and Mutz, 2009). Therefore we include these two variables into the analyses. The WVS measures income using a 1 to 10 scale; and education with a 1 (no formal education or incomplete primary school education) to 8 (university level education with a degree) scale.¹³ The other individual level variables are age and male (male = 1). Age is coded as the actual age of a person in years. Research shows that older people, and men, compared to women, tend to be more prejudicial (Quillian, 1995; Scheepers et al., 2002). Thus, we expect these two variables to have negative effects on anti-foreigner and anti-immigrant prejudice. As explained above, the source for all individual-level variables is the WVS (waves 3–5).¹⁴

3.2.3. Country-level control variables

Our first country-level control variable, at level 2, is the GDP per capita, measured with the Purchasing Power Parity (PPP). GDP per capita PPP is used is similar studies as a better indicator of living conditions in a country (Quillian, 1995; Semyonov et al., 2006). Group threat and competition theories argue that the economic situation of a country has a significant effect on prejudicial attitudes of the individuals. Following the literature, we expect this variable to have a negative impact on anti-immigrant prejudice. Second, some previous studies suggest that democracies facilitate a social environment in which people with diverse characteristics interact with each other; and as a result they are more tolerant towards each other. For example, Peffley and Rohrschneider (2003: 245) argue that citizens in democracies have ‘more opportunities to practice or observe toleration through elections, pluralistic conflicts of interest and so forth’, so they develop more tolerance to disliked groups. Another study finds that ‘the persistence and quality of democracy increases levels of civic culture attitudes’ (Muller and Seligson, 1994: 635). To test for this, we add the level of democracy measured by Polity score, which is 21-point scale ranging from -10 to +10, as a control to our models (Marshall and Jaggers, 2005).

Third, we add unemployment into the models (World Bank, 2008).¹⁵ Unemployment may cause marginalization from the general society and resources it offers, and thus increase the scapegoating of immigrants and foreigners. Several studies find that unemployment played an important role in increasing anti-immigrant prejudice and support for extremist political parties (Golder, 2003; Jackman and Volpert, 1996; Strabac and Listhaug, 2008). We expect high level of unemployment to increase anti-immigrant prejudice.

Fourth, we add year into the models. This allows us to control if any of the effects we detect in the models are due to changes in the historical context. In addition, this variable will also help us assess if the level of anti-immigrant prejudice increased during the period of 1995–2007, the latest wave of globalization.

The third level includes two independent variables that do not change over time. These two variables are post communism and high-immigration regions. First, the post-communist countries were ruled by very similar regimes that adopted ethnic/immigrant policies within and across themselves. Many note that the post-communist countries adopted ‘nationalities policies’ in which internal immigration was allowed and encouraged to a certain extent in the former Soviet Union republics (e.g. Simon, 1991). As a result, in the aftermath of the breakdown of the communist regimes, these countries witnessed much more heterogeneous societies. In addition to the common legacy, we expect that interethnic marriages and cultural exchanges in the past made these countries more tolerant toward outsiders. A recent study suggests that ethnically heterogeneous post-communist countries show high tolerance, except for those (i.e. Balkan countries) that experienced civil war (Guerin et al., 2004). Second, we add a dummy variable for high immigration regions, which includes Western Europe, North America and Oceania. These regions are economically highly developed, receive the bulk of international immigration and may have distinct dynamics and cultural traits that may affect the perception of immigrants. Our sample includes countries from at different levels of economic development and from different geographical regions. Adding this variable allows us to control for some of the diversity in our sample. Table 1 presents the descriptive statistics for all variables.

Table 1.

Descriptive statistics: Means and standard deviations

Variable	Mean	SD
Anti-immigrant prejudice	0.18	0.39
Income	4.54	2.48
Education	4.41	2.27
Male	0.48	0.50
Age	41.90	16.20
GDP per capita (PPP)	14061.43	10849.40
Level of democracy	7.00	4.63
Unemployment	9.99	6.61
Year	2000.01	3.96
Post-communism	0.27	0.45
High immigration regions	0.34	0.48
KOF – Economic globalization	64.46	16.64
KOF – Social globalization	60.19	20.37
International migrant stock (logged)	1.14	1.54
Trade openness (logged)	4.18	0.52
Inward FDI stock (logged)	2.88	0.79

3.3. Method

The combination of individual- and contextual-level data is essential for our analyses, so we use a multi-level model. Multi-level models allow accounting for the nature of the relationship between individual and country-level factors and simultaneous control for individual and contextual level variables (Raudenbush and Bryk, 2002). In our dataset, some variables such as post-communism and high-immigration regions do not vary across time while other national and individual control variables do. In our models, given that individuals are nested in country-years and country-years are nested in countries, we adopt three-level models for estimation. Using dummy variables in Ordinary Least Squares (OLS) to assess the impact of national-level contextual variables would result in under-estimation of the standard errors of the coefficients. OLS assumes that individual level errors are uncorrelated with others in a given country, which causes a Type I error (Steenbergen and Jones, 2002). Given that our dependent variable is binary, we estimate a hierarchical generalized linear model that employs a logit link.

As the level 1 model shows below, the anti-immigrant attitude of an individual depends on the intercept specific to country-year, π _0jk, country-year specific factors (π _1jk, π _2jk, π _3jk, π _4jk, π _5jk) and residual e_ijk.¹⁶

\begin{array}{l} A n t i - Im m m i g r a n t S e n t i m e n t s_{ijk} = π_{0jk} + π_{1jk} {Income}_{ijk} . + π_{2 jk} {Education}_{ijk} + π_{3 jk} \\ {Age}_{ijk} + π_{4 jk} {Male}_{i j k} + e_{ijk} \end{array}

We add the country-year covariates to the model at Level-2:

π_{0 j k} = β_{00 k} + β_{01 k} Trade {Openness}_{jk} + β_{02 k} {FDI}_{jk} + β_{03 k} Immigration {Stock}_{jk} + β_{04 k} {GDPpercapita}_{jk} + β_{05 k} {Polity}_{jk} + β_{06 k} {Unemployment}_{jk} + β_{07 k} {Year}_{jk} + r_{0 jk}

In order to test our hypothesis on the relationship between globalization and immigration size, we introduce our cross-level interaction variables to the model:

β_{01 k} = γ_{010} + γ_{011} Trade {Openness}_{jk} + u_{01 k}

β_{02 k} = γ_{020} + γ_{021} {FDI}_{jk} + u_{02 k}

β_{03 k} = γ_{030} + γ_{031} Immigration {Stock}_{jk} + u_{03 k}

Finally, given that repeated cross-sectional surveys are nested within countries we introduce and run our level-3 model. In the models, the country-specific intercepts depend on the overall fixed effect, γ₀₀₀, and the random effect (u_00k) associated with the country k.

β_{00 k} = γ_{000} + γ_{001} High Immigration {Regions}_{k} + γ_{002} {Post-communism}_{k} + u_{00 k}

4. Results

Table 2 presents the predictors of anti-immigrant prejudice in the 64 countries we analyze. Overall, the results in this model confirm the findings in the literature. As income and the level of education of individuals increase, they become more likely to endorse tolerance toward their immigrant neighbors. For a one unit increase in education, such as from primary school to secondary school, and a unit increase in income categories, anti-immigrant prejudice will decrease by 7 and 3.6 percent respectively.¹⁷ Again similar to previous research, we find older people and men more likely to express prejudice. Being male increases anti-immigrant prejudice by 8.9 percent whereas a 10-year increase in age increases it by 3.2 percent.

Table 2.

Predictors of anti-immigrant prejudice

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Individual level control variables
Education	−0.0688***	−0.0688***	−0.0688***	−0.0688***	−0.0688***	−0.0688***	−0.0688***	−0.0688***
	(−18.787)	(−18.782)	(−18.780)	(−18.775)	(−18.779)	(−18.780)	(−18.787)	(−18.775)
Income	−0.0351***	−0.0349***	−0.0349***	−0.0349***	−0.0349***	−0.0349***	−0.0349***	−0.0349***
	(−10.278)	(−10.226)	(−10.227)	(−10.233)	(−10.223)	(−10.229)	(−10.231)	(−10.225)
Age	0.0032***	0.0032***	0.0032***	0.0032***	0.0032***	0.0032***	0.0032***	0.0032***
	(6.882)	(6.927)	(6.927)	(6.927)	(6.925)	(6.922)	(6.926)	(6.927)
Male	0.0857***	0.0857***	0.0857***	0.0857***	0.0857***	0.0857***	0.0857***	0.0857***
	(6.110)	(6.108)	(6.108)	(6.109)	(6.110)	(6.109)	(6.107)	(6.110)
National level control variables Level-2
GDP		−0.000009	−0.000009	−0.000015	−0.000013	−0.000011	−0.000008	−0.000011
		(−0.662)	(−0.583)	(−1.007)	(−0.897)	(−0.749)	(−0.548)	(−0.747)
Democracy		−0.0219	−0.0221	−0.0195	−0.0211	−0.0182	−0.0189	−0.0225
		(−1.333)	(−1.339)	(−1.205)	(−1.312)	(−1.130)	(−1.186)	(−1.397)
Unemployment		0.0018	0.0020	0.0014	0.0018	−0.0158	−0.1764†	−0.0185
		(0.168)	(0.187)	(0.129)	(0.175)	(−1.043)	(−1.747)	(−0.582)
Year		0.0272*	0.0270*	0.0228†	0.0296*	0.0288*	0.0267†	0.0295*
		(2.419)	(2.363)	(1.956)	(2.138)	(2.115)	(1.947)	(2.130)
Level-3
Post-communism		0.1237	0.1223	0.0182	−0.0245	−0.1047	−0.1069	−0.0254
		(0.615)	(0.630)	(0.086)	(−0.116)	(−0.481)	(−0.500)	(−0.122)
High immigration Regions		−0.4422	−0.4377	−0.3501	−0.3732	−0.3913	−0.4772	−0.3933
		(−1.307)	(−1.287)	(−1.043)	(−1.134)	(−1.189)	(−1.443)	(−1.203)
Globalization variables
International migration stock			−0.0090	−0.0365	−0.0169	−0.1724	−0.0359	−0.0318
			(−0.142)	(−0.575)	(−0.345)	(−1.629)	(−0.582)	(−0.517)
Trade openness				0.2750†	0.3338*	0.3604*	−0.0222	0.3507*
				(1.737)	(2.034)	(2.188)	(−0.087)	(2.142)
Inward FDI stock					−0.0909	−0.0762	−0.0763	−0.1704
					(−0.960)	(−0.813)	(−0.814)	(−1.134)
Interactions
Unemployment* Int. migration stock						0.0148†
						(1.655)
Unemployment* Trade openness							0.0426†
							(1.776)
Unemployment* FDI								0.0078
								(0.686)
Constant	−1.7772***	−1.7922***	−1.7915***	−1.7963***	−1.7933***	−1.7862***	−1.7921***	−1.7924***
	(−18.854)	(−22.653)	(−22.617)	(−23.693)	(−24.045)	(−23.834)	(−24.279)	(−24.328)
2 log likelihood (E+005)	−2.102294	−2.102206	−2.102210	−2.102201	−2.102197	−2.102172	−2.102147	−2.102182

Notes: Entries are full maximum likelihood coefficients with robust standard errors estimated with HLM 6.02. Each cell contains the unstandardized coefficient, and the t-scores in parentheses. The N is 148,759 individuals, 122 country-years, and 64 countries; and same for all models.

†

<.1; *p < .05; **p < .01; ****p < .001 (two-tailed).

Model 2 adds the country-level control variables and shows that the direction and significance of the individual-level variables are nearly identical to the first model. Among the country-level control variables, the year variable has a positive significant effect on anti-immigrant prejudice at the .05 level. This suggests that anti-immigrant prejudice actually increased in the countries in our sample during the latest wave of economic globalization since the mid-1990s. For each year, the probability of having anti-immigrant prejudice increases by 2.7 percent.

It is surprising that GDP per capita does not have a significant negative effect on anti-immigrant prejudice. However, we should note that the high-immigration regions variable takes away the statistical significance of GDP. In the absence of our high-immigration region variable, which includes countries with high GDP, GDP per capita variable is statistically significant. The unemployment variable, as well as two third-level variables, post-communism and high-immigration regions, also have statistically insignificant coefficients.

Starting with the third model, we add globalization variables. First, in Model 3, we add international migration stock. Surprisingly, international migration stock does not appear to have any statistically significant effect on anti-immigrant prejudice, while the effects of other variables do not change. This is contrary to the expectations of group threat and competition theories, while not providing any support for contact theory either. The fourth model adds trade openness. In countries that are more open to international trade domestic populations are more likely to express anti-immigrant prejudice. This suggests that insecurities created by trade liberalization might be directed against immigrants. Previous research suggests that trade openness hurt workers in import-competing sectors directly and stir up anti-immigrant prejudice (Mayda, 2006; Swank and Betz, 2003). In Model 5, we add inward FDI stock to our model. Similar to international migration stock, FDI has no effect on anti-immigrant prejudice while the effect of trade openness is robust.

The lack of the impact of unemployment and migration stock is puzzling, but as we discussed in regard to our third hypothesis, the effect of these two variables might be conditional on one another. Therefore, we finally add the interactions of unemployment and globalization variables to test our third hypothesis. Model 6 presents the interaction results between immigration stock and unemployment, while Model 7 displays the result for the interaction of trade and unemployment. The result suggests that in countries with high levels of trade openness or immigration stock, accompanied with unemployment, anti-immigrant prejudice is higher as well. Finally, Model 8 presents similar interaction for FDI. The finding suggests that the FDI does not have similar dynamic relations with unemployment that the two other variables do.

The interpretation of interaction effects requires caution. For this reason, using HLM 6.2 software, we create interaction figures for the trade and immigration model. Figure 2a and Figure 2b make it easy for us to interpret the interaction variables. For these two figures, we calculated anti-immigrant prejudice levels for globalization measures as a function of unemployment and two globalization measures, trade and migration stock. Figure 2a suggests at the mean levels of migrant stock, anti-immigrant prejudice remains about the same over the lowest and highest unemployment level (around 0.13). However, if a country has very high migrant level (logged level = 3.7), anti-immigrant prejudice increases from around 0.08 to 0.20. Similarly, at the mean trade openness level, anti-immigrant prejudice level remains constant. As we move to the highest trade openness countries, we see that the prejudice level increases significantly, from 0.17 to 0.37.

Figure 2.

The effect of unemployment on anti-immigrant prejudice in low, medium, and high immigration situations (predicted probabilities).

5. Discussion and conclusion

In this study, using a sample of 64 developed and less developed countries and nearly 150,000 individuals, we tested two contradictory approaches regarding the impact of globalization on anti-immigrant prejudice: the ‘civilizing/integrative globalization’ and the ‘destructive globalization/globalization as a threat’ theses. Our analyses reveal some support for the ‘destructive globalization/globalization as a threat’ thesis. Countries that have higher levels of openness to international trade have significantly more anti-immigrant prejudice in our sample. This suggests that workers around the world direct their frustration originating from the problems created by economic globalization towards immigrants (Scheve and Slaughter, 2001, 2004; Swank and Betz, 2003). This finding is consistent with those who argue that the recent surge in international trade hurts the working people by contributing to the demise of the traditional labor classes in developed countries and increasing the exploitation of workers in less developed countries by transnational corporations (e.g. Brady and Denniston, 2006; Fernandez-Kelley, 1983; Lee, 1998).

However, our results also show that not all aspects of globalization increase anti-immigrant prejudice. So, the possible negative effect of globalization may not be as extensive as predicted by some critics (e.g. Barber, 1995; Mander and Goldsmith, 2001). The modest impact of FDI and international migration is surprising given the claims in the literature (e.g. Quillian, 1995; Scheve and Slaughter, 2004; Semyonov et al., 2006). However, the disparity in the effects of international trade, FDI and migration is important because it emphasizes that globalization is a multi-dimensional and process and these dimensions do not have a uniform effect. The lack of effect of FDI might be due to visibility. FDI tends to have less visibility compared to the imported products, which have an everyday presence in the lives of people around the world.

Interestingly, anti-immigrant prejudice seems to stem mostly from the economic threat created by globalization, not the actual existence and inflow of immigrants. International migration stock has no effect on anti-immigrant prejudice by itself in the models. However, our results show that unemployment has a significant effect on anti-immigrant prejudice in countries with significant presence of immigrants. This finding is consistent with Citrin and Sides (2008) who find that people may exaggerate the number of the immigrants and hold them as scapegoats when unemployment becomes a serious issue. In addition, the interaction of unemployment and international trade also has a significant effect. This suggests that in countries where high levels of unemployment are accompanied by higher levels of integration to the global economy, we will see more prejudice toward immigrants because they are seen as part of the threat of globalization. While globalization increases job opportunities for some, it causes many to lose their jobs (e.g. Brady and Denniston, 2006; Spence, 2011). People who lose their jobs or experience hardship in securing employment are more likely to scapegoat immigrants.

However, our analysis has several limitations that future studies may overcome with better data, which cautions us not to dismiss the predictions of the ‘civilizing/ integrative globalization’ thesis. For example, we were not able to assess the level of segregation of immigrants from the native populations, which may be a significant factor. There were also other issues, which we could not address in this study. First, we were not able to test for the skill level, and sectoral and occupational composition of immigrant populations because of the lack of data. If these data become available, it will help to better assess the level and the nature of the perceived threat created by immigrants. Second, our analysis spans only 13 years, while globalization is a longer historical process. As new waves of the WVS become available, inclusion of new data will provide a more rigorous test of the impact of globalization. Third, our dependent variable tests for anti-immigrant prejudice on one issue. When data on as many countries as found in the WVS and multiple measures of anti-immigrant prejudice become available, combining multiple questions to compose an index that gauges anti-immigrant prejudice would improve our understanding of anti-immigrant prejudice and the impact of globalization on it. Fourth, in this study, we could not analyze the impact of globalization on anti-immigrant prejudice through its impact on processes such as economic development, expansion of education and democratization. This would help to develop a more comprehensive understanding of the impact of globalization on prejudicial attitudes. Finally, the differing effects international trade, FDI and migration creates compelling questions about the differences in public knowledge and perception of these different dimensions of globalization, which are beyond the scope of this current study and can be addressed by future studies.

Overall, this research assesses a relatively less studied issue in the globalization debate: the impact of globalization on anti-immigrant prejudice. We have shown that people generally perceive globalization as a threat, although different dimensions of globalization do not have a uniform effect. Thus, we join scholars and intellectuals who perceive globalization as a multi-dimensional process. This study has broader implications on scholarly and policy-oriented work. It suggests that at least in the short run, the economic effects of globalization increase prejudice towards immigrant populations. In particular, unemployment seems to trigger this feeling. It gives warning signals to policy-makers that resentment may turn into unwanted actions against immigrants in countries with high economic vulnerability.

Footnotes

Appendix

Appendix Table 2.

Country-years with highest and lowest levels of trade openness, FDI, and international migration stock

Country	Year	Trade openness (%)	Country	Year	FDI (%)	Country	Year	Migration stock (%)
Highest 20
Singapore	2002	369.4	Singapore	2002	153.7	Jordan	2001	40.5
Luxembourg	1999	249.2	Luxembourg	1999	96.1	Luxembourg	1999	36.7
Malaysia	2006	216.9	Netherlands	2006	75.8	Singapore	2002	33.6
Ireland	1999	164.0	Ireland	1999	75.4	Latvia	1996	28.3
Estonia	1999	149.0	Belgium	1999	70.9	Latvia	1999	22.8
Belgium	1999	146.3	T. & Tobago	2006	68.6	Australia	1995	22.5
Netherlands	2006	140.6	Chile	2005	62.8	Switzerland	2007	22.3
Estonia	1996	137.0	Chile	2000	60.8	Estonia	1996	21.5
Moldova	2006	137.0	N. Zealand	1998	60.0	Switzerland	1996	20.5
Thailand	2007	132.5	Sweden	2006	59.0	Australia	2005	20.1
Moldova	2002	130.6	Nigeria	1995	57.8	N. Zealand	1998	19.9
Moldova	1996	129.1	Switzerland	2007	52.5	Estonia	1999	18.2
Slovakia	1998	128.8	UK	2006	47.7	Canada	2000	18.1
Slovenia	2005	126.6	Netherlands	1999	46.3	Ukraine	2006	14.5
Slovakia	1999	125.9	Morocco	2007	44.4	Ukraine	1999	14.1
Netherlands	1999	119.5	Estonia	1999	44.3	Ukraine	1996	13.7
Lithuania	1997	114.6	Spain	2007	43.9	US	2006	12.9
Czech Rep.	1999	112.0	Moldova	2002	38.5	Sweden	2006	12.4
Bulgaria	1997	111.9	Jordan	2001	38.2	Germany	2006	12.2
Czech Rep.	1998	109.5	Moldova	2006	38.1	Germany	1999	11.9
Lowest 20
China	1995	43.9	Russia	1999	9.3	Romania	1998	0.6
China	2001	43.0	S. Africa	1996	9.2	Bulgaria	1997	0.6
Tanzania	2001	40.1	Italy	1999	9.0	Mexico	2000	0.6
Australia	2005	39.9	S. Korea	2001	8.8	Nigeria	1995	0.5
Uruguay	1996	39.5	Turkey	1996	8.7	India	2006	0.5
Egypt	2000	39.0	Armenia	1997	8.3	Mexico	1996	0.5
Australia	1995	38.7	Pakistan	2001	7.6	Brazil	1997	0.5
Uganda	2001	36.4	Brazil	1997	7.5	Morocco	2007	0.4
Peru	2001	33.4	Germany	1997	7.4	Philippines	2001	0.4
Peru	1996	31.4	US	1995	7.3	Morocco	2001	0.4
Pakistan	2001	30.7	Moldova	1996	7.2	Brazil	2006	0.3
US	2006	26.8	Finland	1996	6.8	Egypt	2000	0.3
India	2001	26.3	Uruguay	1996	6.2	Peru	1996	0.2
Brazil	2006	26.3	India	2006	5.7	Peru	2001	0.2
US	1999	24.3	Iceland	1999	5.5	Indonesia	2001	0.2
US	1995	23.3	India	2001	4.2	Peru	2007	0.2
India	1995	23.2	Ukraine	1996	3.2	Indonesia	2006	0.1
Argentina	1999	21.3	S. Korea	1996	2.1	China	2007	.05
Argentina	1995	19.7	India	1995	1.6	China	2001	.04
Brazil	1997	15.8	Russia	1995	1.4	China	1995	.03

Note: Unlogged values are presented in the table.

Acknowledgements

We thank David Brady and anonymous reviewers for constructive comments and suggestions.

Notes

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