Immigration, diversity and the relevance of ascriptive characteristics in defining national identity across 21 countries and 28 West-German districts

Immigration, diversity and the relevance of ascriptive characteristics in defining national... Abstract The relevance of ascriptive characteristics in defining national identity—i.e. attributes that categorically exclude certain groups from national identity—depends on an individual’s perception of immigration-related diversity. Increasing diversity can stand for both a growing threat as well as growing familiarity. In contrast to many other studies, we conceptualize increasing diversity as a factor of growing familiarity, reducing the relevance of ascriptive attributes. We tested this familiarization thesis using multilevel models based on ISSP data of 21 countries and 28 West German districts surveyed in 2003/4 and 2013/14. We exploit cross-sectional and longitudinal effects. Our findings reveal that increasing diversity reduces the relevance of ascriptive characteristics in defining national identity. At the country level, this relationship relies on longitudinal effects and at the district level, on an interaction of cross-sectional and longitudinal effects. We discuss the results and make suggestions for future research. 1. Introduction In his article ‘Race in the Modern World,’ Appiah (2015: 1) argues that ‘identities rooted in the reality or the fantasy of shared ancestry, in short, remain central in politics, both within and between nations. In this new century, as in the last, the color line and its cousins are still going strong.’ He points to a social phenomenon which could be readily observed in the recent election campaigns in Europe and the USA, in which ethnic/race and religious categories have been important components used to mobilize citizens (Betz and Meret 2009; Gökarıksel and Smith 2016). In this context, national identities can effectively be used to demarcate in- and out-groups within a society, and this demarcation is negotiated both politically and societally (Wimmer 2008). However, when ancestry becomes relevant in defining national identity, descent determines who counts as part of that identity and who does not. Some groups are categorically excluded from that national identity as a result, and becoming part of that identity remains unachievable. Although civic and meritocratic principles are supposed to determine membership into the settled mainstream in modern democratic nation-states (Alba and Nee 2003) descent-based definitions of national identity can be prevalent among the population. For example, people may more swiftly associate the label ‘American’ with those of European descent rather than with others (Devos and Ma 2013). Lines determining in- and out-groups may not initially result in intergroup bias such as hostility toward out-group members (Brewer 1999); however, they could for this purpose. Moreover, the exclusionary definition of national identity yields greater political mobilization than its inclusive counterpart, as a recently published study reveals (Helbling et al. 2016). Radical right-wing politicians are appealing to constituents by sketching multiculturalism and diversity as a threat and appealing to older homogenous identities (Pirro 2014; Loch and Norocel 2015). In this vein, we are mainly interested in how different patterns of immigration-related diversity are associated with the relevance of ascriptive characteristics in defining national identity among settled citizens. For this purpose, we employ country-level and district-level ISSP data from 2003/4 and 2013/14 in order to conduct a multilevel analysis accounting for the cross-sectional and longitudinal characteristics of the data. In this article, immigration-related diversity is in general defined as an occurrence of new identity categories that enter a society through immigration. It also implies the process of diversification, whereby new categories combine with the existing ones (Crisp and Hewstone 2007). Therefore, an increase in immigration also means an increase of diversity and diversification, which can manifest in several ways (e.g. one-way assimilation or hybridization; see Canan 2015). 2. Theoretical frame and research 2.1 National identity and diversity One method of conceptualizing national identity is to apply the ethnic–civic distinction (Kohn 1944; Brubaker 1992). Ethnic concepts of national identity emphasize descent as well as cultural aspects, such as language and customs. By contrast, civic concepts emphasize the legal-political community in which citizens are equal under the law, and membership not determined by descent (Smith 1991). Some criticized the civic–ethnic distinction as too ambiguous for the purposes of empirical analysis (Shulman 2002). More specifically, cultural categories such as language may fall under either concept (Brubaker 1999). Furthermore, ethnic concepts containing cultural categories may suggest openness, but remain nontheless exclusive because they are based on descent (Kymlicka 1999). Wright (2011) proposes a more general distinction that divides the concept of national identity into ascriptive and achievable aspects. While ascriptive characteristics are those that are categorically exclusive, achievable characteristics are those that can be attained. This distinction, seen as more appropriate in the context of migration and integration, recognizes the opportunity for immigrants to achieve membership. In this light, we define the ascriptive characterization of national identity as one that excludes certain groups based on unachievable or hard-to-achieve attributes in the long-term and across generations; for example, skin color or religious affiliation. The question is then: under which conditions do settled citizens regard ascriptive characteristics as relevant in defining national identity? Threat, or the perception of threat, is a very powerful factor in explaining intergroup bias in the context of diversity. One can divide theoretical concepts addressing threat perceptions into two general camps. One group suggests that threat perceptions evolve in the context of competition for scarce resources, and result in exclusionary reactions towards out-groups (e.g. Blumer 1958; Sherif 1966; Blalock 1967). The other group maintains that individuals have a concept of themselves, which is linked to the evaluative connotations of the social categories or groups to which individuals perceive themselves to belong (Tajfel and Turner 1986). A positive social identity—stemming from intergroup comparisons with relevant other groups—helps individuals to maintain their self-concept. Accordingly, social identities are connected to values worthy of protection and perceived threat to those identities result in exclusionary reactions towards out-groups (Branscombe et al. 1999). More than likely, both forms of threat perception are at play when it comes to immigration-related diversity (Sniderman et al. 2004). Against this backdrop, increasing diversity is usually conceptualized as a threat to an individual’s own group resulting in higher anti-immigrant prejudice (Quillian 1995; Sides and Citrin 2007); whereas, factors such as intergroup contact can ease this relationship (Schlueter and Scheepers 2010). This sort of thinking can easily be applied to beliefs or imaginations of national identity; in the face of increasing diversity ‘mainstream populations might respond by adopting a view of their nation drawn along more ascriptive, immigrant-exclusive lines’ (Wright 2011: 842). Yet, the alternative idea of increasing diversity as a source of experience and familiarization might be more suitable in analyzing concepts of national identity, since the empirical factum of diversity and diversification might not be easily ignored.1 The following section outlines this argument and presents the formulated hypotheses. 2.2 Diversity, familiarity and national identity National identity defines a social identity that can enhance self-concept (Tajfel and Turner 1986). It is relatively abstract and therefore can be highly contentious regarding criteria of belonging (Onorato and Turner 2002). In this context, increasing diversity can help to change those criteria to be more inclusive as it enables experience and familiarity with those initially considered ‘others’. More concretely, high levels of diversity means that substantial diversification is ongoing among immigrant groups by adopting characteristics of the native population and combining them with their own characteristics (Canan and Foroutan 2016). Diversification can result in recognition of ‘togetherness’ and reduce intergroup bias, as former out-group members become similar to the in-group (Crisp and Hewstone 2007). Moreover, re-categorization into a common group identity can take place (Gaertner and Dovidio 2000). A broader population can experience diversity by intergroup contact (Pettigrew and Tropp 2006), simple day-to-day encounters (Zajonc 2001; Blokland and Nast 2014), representations (e.g. in politics or media, see Bodenhausen et al. 1995), and societal narratives comprising norms and rights (Foroutan 2016). Along this line, one can formulate the following hypothesis: H1: The higher the levels of diversity, the less important ascriptive characteristics will be in defining national identity. This expectation contradicts Wright (2011), who suggests that high levels of diversity represent a potential threat resulting in higher relevance of ascriptive attributes; his analysis of 16 European countries seems to confirm this relationship (p < 0.1). Moreover, the rapid growth in diversity indicated by the increased share of the foreign-born population is clearly associated with a higher relevance of ascriptive characteristics in defining national identity. However, Wright does not account for potential interactions and non-linear relationships, which would be reasonable with the argument of increasing familiarity.2 Moreover, it can be insightful to look at both the differences between individuals living in regions with different shares of immigrants (between or cross-sectional effects) as well as the differences between individuals exposed to different levels of increase in the shares within regions (within or longitudinal effects) (Fairbrother 2014). How might inter-acting and non-linear patterns appear? First, existing levels of diversity and changes in diversity levels may represent inter-related factors affecting the relevance of ascriptive characteristics in defining national identity. In this respect, increasing diversity over time may be largely effective in decreasing the relevance of ascriptive attributes, especially when the initial levels of existing diversity are low and as familiarization progresses. This argument for the diminishing marginal effects of increasing diversity on the relevance of ascriptive attributes assumes that diversity establishes itself (i.e. social praxis) at a specific point (see Wessendorf 2013), and that society is not sensitive towards changes in diversity that could affect the relevance of ascriptive characteristics in defining national identity as prior a phase of familiarization. In this regard, one can formulate the following expectation: H2: The negative effect of increasing diversity on the relevance of ascriptive characteristics in defining national identity subsides at higher levels of diversity. One can also formulate the argument made in Hypothesis 2 the other way around, with the idea that immigration instigates threat responses. In this scenario, one can perceive increasing diversity from initially low levels as threatening, resulting in the increased relevance of ascriptive attributes due to a lack of familiarity. Newman (2013) demonstrates that among 104 US counties, the positive effect of increasing diversity on cultural threat perceptions decreases along with higher levels of diversity. Similarly, Schneider (2008) reveals that increasing diversity has a positive effect on threat perceptions at low levels of diversity and that effect levels off at higher levels of diversity among 20 European countries. Even if those studies do not analyze concepts of national identity, they point to the plausibility of an alternative explanation. In contrast to the expectation formulated in Hypothesis 2, one can conceive the possibilty that increasing diversity—when levels are low—may strengthen the relevance of ascriptive characteristics in defining national identity due to a lack of familiarity. Concerning the discrepancy between these two expectations, we will consider one further factor that could be engaged in determining the relationship between diversity and the relevance of ascriptive attributes. Another diversity-related factor involves the level of increase in diversity. One may perceive rapid changes in demographics due to very high immigrant inflows as threatening because of the intensity of its occurrence (Hooghe et al. 2009; Hopkins 2010). Especially in regions with low levels of diversity, those changes could be effective in promoting the relevance of ascriptive attributes because of less experience with new identities. If true, then we would expect a curve–linear relationship between levels of increase in diversity and the relevance of ascriptive attributes. More specifically, increasing diversity should support Hypothesis 1 in that it would reduce the relevance of ascriptive attributes. At a specific point, high rates of increase in diversity should spark a backlash that reverses the effect and causes an increase in the relevance of ascriptive attributes in regions with lower diversity. Therefore, one can formulate another hypothesis as follows: H3: Increasing diversity will reduce the relevance of ascriptive characteristics in defining national identity, but high levels of increase in diversity will reverse this effect in regions with low levels of diversity. We expect that the formulated hypotheses will apply on both a large and a small scope. 3. Data To test the hypotheses, we used pooled cross-sectional individual data from the ISSP on national identity from 21 countries3 and from 28 districts in West Germany4 surveyed in the years 2003/4 and 2013/14. With regard to the multilevel structure of the analysis, we supplemented the data with further country-level estimates from the Organisation for Economic Co-operation and Development (OECD), the United Nations Population Division, the World Bank, and the Freedom House Index (discussed below). Additional information with regard to the West German districts stem from Federal Statistical Office of Germany, Regional Accounts (VGRdL) provided by the Federal Statistical Office, and the Statistical Offices of the Länder, and German General Social Survey. When available, we used country-level and district-level data in correspondence with the survey year. When these data were not available, we refer to the next year.5 As we were interested in the settled citizens’ views on the relevance of ascriptive attributes in the face of diversity, we only considered respondents when they matched the survey country’s nationality and when their parents possessed the survey country’s nationality at the time when the respondents were born. The data originally consisted of 47,333 cases. Due to missing data, the analysis was conducted based on 41,195 cases. The districts sample consisted of 1690 and 1617 cases, respectively. 4. Operationalization The dependent variable indicating the relevance of the ascriptive characteristics in defining national identity was measured using two items. Respondents were asked about the relative importance they assign to the following factors in identifying who they considered a true member of that nation: having ancestry that matched the nationality of the country, and belonging to the dominant religion of the country. Both criteria can be used to construct a homogenous national identity and thus categorically exclude immigrants (and other minorities) from the imagined national collective in the long run and across generations (Shooman 2015). These criteria are either difficult or impossible for immigrants to achieve. A four-point Likert-type scale measured the perceived importance of each of these criteria. We constructed a mean-based scale, where missing values were excluded and higher values indicated a higher importance of ascriptive attributes (1 = not important at all to 4 = very important; Cronbach’s alpha = 0.67). 4.1 Relevant independent variables As elsewhere (e.g. Wright 2011; Reeskens and Wright 2013), diversity was measured by the share of foreign-born individuals in the observed countries (0.2%–28.3%). For the 19 OECD countries in the analysis, we used estimates from the OECD database.6 For the two non-OECD countries, we used estimates from the United Nations Population Division.7 By contrast, concerning the districts of West Germany, we used the share of individuals that possess a migration background as an indicator for diversity (12.5%–31.9%), where a ‘migration background’ refers to the foreign-born population and their descendants that were born in Germany.8 To the best of our knowledge, a comparable standardized measure internationally was not available on the country level at that time. 4.2 Control variables On the individual level, we controlled for standard demographic variables. We included the respondent’s gender (0 = man; 1 = woman), age (18–97) and education. Due to implausible extreme values (e.g. 82 years of education), the education variable was truncated at the 99.5th percentile, afterwards, ranging from 0 years to 23 years of education. This variable was centered at the country means in order to have the same standard deviation for each country. It was also considered whether the respondent was currently unemployed (0 = no; 1 = yes), as well as marital status (0 = not married; 1 = married) and relative income.9 Because income in each national survey was not uniformly measured the variable was re-categorized into its quintiles for each country. We also considered a non-answer category, as 19.2% of the respondents did not report their income (1 = first quintile; 2 = second quintile; 3 = third quintile; 4 = fourth quintile; 5 = fifth quintile; 6 = otherwise). Additionally, religious affiliation was measured with a dummy variable, indicating whether respondents affiliate with the majority religion or not (0 = no; 1 = yes). Finally, threat perception was another important variable that was measured by a categorical variable indicating whether respondents generally perceive immigrants as threatening for the society (0 = no threat; 1 = threat; 3 = otherwise).10 Aside from the individual-level variables, which also were used for both country- and district-level analysis, we also included country/district-level variables (for summary statistics, see Table b in online supplement). We used GDP per capita as a control variable to account for economic conditions in the countries (US$1011–102,910) or in the West German districts (€19,183–56,066). Strong and increasing economic conditions soften competition (e.g. Semyonov et al. 2006), and therefore may negatively impact the relevance of ascriptive characteristics in defining national identity; GDP per capita estimates were gathered from the World Bank and Regional Accounts (VGRdL).11 Furthermore, we considered conditions of democracy in the observed countries. Strong and increasing conditions of democracy can promote civic elements of national identity (Mansfield and Snyder 2005) and may therefore reduce the relevance of ascriptive attributes. We used the Freedom House Index on civil and political rights.12 Two 7-point scales measured the endorsement of these rights, with higher numerical scores indicating less civil and political freedom. We combined the scales (2–14) whereas higher scores indicating a lower endorsement of democratic values in the countries. 5. Methods We applied multilevel linear regression that considered cross-sectional and longitudinal relationships (Fairbrother 2014). To account for potentially biased standard errors stemming from the complex data structure of countries and years, we employed three-level random intercept models (Raudenbush and Bryk 2002); individuals nested in country-years, and country-years nested within countries. Cross-sectional and longitudinal relationships formulated in the hypotheses are modelled by computing the group means within each country over the two years and subtracting them from the year-specific country estimates. With regard to the diversity variable, the country mean indicates cross-sectional level of diversity and the difference within the countries represents longitudinal changes in diversity. Both terms were included in the regression analysis; the same approach was applied to the district data. 6. Results 6.1 Country-level analysis Figure 1 shows a scatterplot with linear-predicted values by countries and the observed years 2003/4 and 2013/14. It gives an initial impression of the relationship between levels of diversity indicated by the percentage of foreign-born individuals and the importance of ascriptive characteristics in defining national identity. Figure 1. View largeDownload slide Scatterplot of importance of ascriptive attributes and share of foreign-born individuals, by country and years. Note: For underlying data and country abbreviation assignment, see Table c in online supplement. Figure 1. View largeDownload slide Scatterplot of importance of ascriptive attributes and share of foreign-born individuals, by country and years. Note: For underlying data and country abbreviation assignment, see Table c in online supplement. The regression line suggests that the importance of ascriptive attributes decreases along with increasing diversity. Beyond that, both the Philippines and Switzerland appear to be outliers that could influence the overall results. Therefore, we will also conduct models without these countries included. How is the relationship between the relevance of ascriptive attributes and diversity determined in models that are more comprehensive? Model 1 (only country-level variables) and Model 2 (including individual-level variables) in Table 1 reveal that higher diversity is associated with less relevance of ascriptive characteristics in defining national identity. This confirms Hypothesis 1. Moreover, this effect relies on the increase of diversity between the years 2003/4 and 2013/14. Against our expectations in Hypothesis 2 there are no significant interaction effects (Model 3) that indicated an inter-related effect between levels of diversity and changes in levels of diversity in the observed decade. Table 1. Mixed-effects linear regression analysis on the importance of ascriptive characteristics in defining national identity Model 0 Model 1 Model 2 Model 3 Model 4 Year 2013/14 –0.073 0.071 –0.039 0.062 –0.041 0.061 –0.042 0.061 Woman 0.029* 0.012 0.029* 0.012 0.029* 0.012 Age 0.010*** 0.001 0.010*** 0.001 0.010*** 0.001 Years of education –0.112*** 0.012 –0.112*** 0.012 –0.112*** 0.012 Relative income     Lowest quintile (Ref.)     Second quintile –0.041* 0.016 –0.041* 0.016 –0.041* 0.016     Middle quintile –0.059** 0.022 –0.059** 0.022 –0.059** 0.022     Fourth quintile –0.107** 0.033 –0.107** 0.033 –0.107** 0.033     Highest quintile –0.142*** 0.039 –0.142*** 0.039 –0.142*** 0.039     Otherwise –0.021 0.025 –0.021 0.025 –0.021 0.025 Unemployed 0.000 0.022 0.000 0.022 0.000 0.022 Married 0.022 0.014 0.022 0.014 0.022 0.014 Majority religion 0.484*** 0.042 0.484*** 0.042 0.484*** 0.042 Threat perceptions     No threat (Ref.)     Threat 0.324*** 0.057 0.324*** 0.057 0.324*** 0.057     Otherwise 0.107*** 0.032 0.107*** 0.032 0.107*** 0.032 GDP per capita (1000$) –0.008** 0.003 –0.007* 0.003 –0.007* 0.003 –0.007* 0.003 Change in GDP per capita 0.004 0.003 0.005 0.003 0.005 0.003 0.005 0.004 Democracy scores 0.079* 0.036 0.072 0.039 0.072 0.039 0.065* 0.032 Change in democracy scores –0.031 0.086 –0.009 0.060 –0.007 0.060 0.003 0.059 Share of foreign–born (SFB) –0.003 0.011 –0.012 0.012 –0.012 0.012 –0.029 0.018 Change in SFB –0.041* 0.017 –0.040** 0.013 –0.036 0.022 –0.047 0.025 SFB x Change in SFB 0.000 0.002 0.001 0.002 Change in SFB² –0.074* 0.034 SFB x Change in SFB² 0.006* 0.003 Constant 2.507*** 0.087 2.622*** 0.165 1.824*** 0.193 1.826*** 0.195 1.966*** 0.209 Variance components     Var (Country) 0.138*** 0.069*** 0.070*** 0.070*** 0.061***     Var (Year) 0.026*** 0.016*** 0.014*** 0.014*** 0.014*** Statistics     Df 4 11 24 25 27     AIC 104,544.43 104,534.24 95,346.62 95,346.61 95,344.16     BIC 104,579.01 104,629.33 95,528.17 95,528.15 95,525.70     Log likelihood –52,268.22 –52,256.12 –47,652.31 –47,652.30 –47,651.08 N (countries) 21 21 21 21 21 N (country–years) 42 42 42 42 42 N (individuals) 41,195 41,195 41,195 41,195 41,195 Model 0 Model 1 Model 2 Model 3 Model 4 Year 2013/14 –0.073 0.071 –0.039 0.062 –0.041 0.061 –0.042 0.061 Woman 0.029* 0.012 0.029* 0.012 0.029* 0.012 Age 0.010*** 0.001 0.010*** 0.001 0.010*** 0.001 Years of education –0.112*** 0.012 –0.112*** 0.012 –0.112*** 0.012 Relative income     Lowest quintile (Ref.)     Second quintile –0.041* 0.016 –0.041* 0.016 –0.041* 0.016     Middle quintile –0.059** 0.022 –0.059** 0.022 –0.059** 0.022     Fourth quintile –0.107** 0.033 –0.107** 0.033 –0.107** 0.033     Highest quintile –0.142*** 0.039 –0.142*** 0.039 –0.142*** 0.039     Otherwise –0.021 0.025 –0.021 0.025 –0.021 0.025 Unemployed 0.000 0.022 0.000 0.022 0.000 0.022 Married 0.022 0.014 0.022 0.014 0.022 0.014 Majority religion 0.484*** 0.042 0.484*** 0.042 0.484*** 0.042 Threat perceptions     No threat (Ref.)     Threat 0.324*** 0.057 0.324*** 0.057 0.324*** 0.057     Otherwise 0.107*** 0.032 0.107*** 0.032 0.107*** 0.032 GDP per capita (1000$) –0.008** 0.003 –0.007* 0.003 –0.007* 0.003 –0.007* 0.003 Change in GDP per capita 0.004 0.003 0.005 0.003 0.005 0.003 0.005 0.004 Democracy scores 0.079* 0.036 0.072 0.039 0.072 0.039 0.065* 0.032 Change in democracy scores –0.031 0.086 –0.009 0.060 –0.007 0.060 0.003 0.059 Share of foreign–born (SFB) –0.003 0.011 –0.012 0.012 –0.012 0.012 –0.029 0.018 Change in SFB –0.041* 0.017 –0.040** 0.013 –0.036 0.022 –0.047 0.025 SFB x Change in SFB 0.000 0.002 0.001 0.002 Change in SFB² –0.074* 0.034 SFB x Change in SFB² 0.006* 0.003 Constant 2.507*** 0.087 2.622*** 0.165 1.824*** 0.193 1.826*** 0.195 1.966*** 0.209 Variance components     Var (Country) 0.138*** 0.069*** 0.070*** 0.070*** 0.061***     Var (Year) 0.026*** 0.016*** 0.014*** 0.014*** 0.014*** Statistics     Df 4 11 24 25 27     AIC 104,544.43 104,534.24 95,346.62 95,346.61 95,344.16     BIC 104,579.01 104,629.33 95,528.17 95,528.15 95,525.70     Log likelihood –52,268.22 –52,256.12 –47,652.31 –47,652.30 –47,651.08 N (countries) 21 21 21 21 21 N (country–years) 42 42 42 42 42 N (individuals) 41,195 41,195 41,195 41,195 41,195 * p < 0.05; ** p < 0.01; *** p < 0.001. Unstandardized coefficients with robust standard errors. Table 1. Mixed-effects linear regression analysis on the importance of ascriptive characteristics in defining national identity Model 0 Model 1 Model 2 Model 3 Model 4 Year 2013/14 –0.073 0.071 –0.039 0.062 –0.041 0.061 –0.042 0.061 Woman 0.029* 0.012 0.029* 0.012 0.029* 0.012 Age 0.010*** 0.001 0.010*** 0.001 0.010*** 0.001 Years of education –0.112*** 0.012 –0.112*** 0.012 –0.112*** 0.012 Relative income     Lowest quintile (Ref.)     Second quintile –0.041* 0.016 –0.041* 0.016 –0.041* 0.016     Middle quintile –0.059** 0.022 –0.059** 0.022 –0.059** 0.022     Fourth quintile –0.107** 0.033 –0.107** 0.033 –0.107** 0.033     Highest quintile –0.142*** 0.039 –0.142*** 0.039 –0.142*** 0.039     Otherwise –0.021 0.025 –0.021 0.025 –0.021 0.025 Unemployed 0.000 0.022 0.000 0.022 0.000 0.022 Married 0.022 0.014 0.022 0.014 0.022 0.014 Majority religion 0.484*** 0.042 0.484*** 0.042 0.484*** 0.042 Threat perceptions     No threat (Ref.)     Threat 0.324*** 0.057 0.324*** 0.057 0.324*** 0.057     Otherwise 0.107*** 0.032 0.107*** 0.032 0.107*** 0.032 GDP per capita (1000$) –0.008** 0.003 –0.007* 0.003 –0.007* 0.003 –0.007* 0.003 Change in GDP per capita 0.004 0.003 0.005 0.003 0.005 0.003 0.005 0.004 Democracy scores 0.079* 0.036 0.072 0.039 0.072 0.039 0.065* 0.032 Change in democracy scores –0.031 0.086 –0.009 0.060 –0.007 0.060 0.003 0.059 Share of foreign–born (SFB) –0.003 0.011 –0.012 0.012 –0.012 0.012 –0.029 0.018 Change in SFB –0.041* 0.017 –0.040** 0.013 –0.036 0.022 –0.047 0.025 SFB x Change in SFB 0.000 0.002 0.001 0.002 Change in SFB² –0.074* 0.034 SFB x Change in SFB² 0.006* 0.003 Constant 2.507*** 0.087 2.622*** 0.165 1.824*** 0.193 1.826*** 0.195 1.966*** 0.209 Variance components     Var (Country) 0.138*** 0.069*** 0.070*** 0.070*** 0.061***     Var (Year) 0.026*** 0.016*** 0.014*** 0.014*** 0.014*** Statistics     Df 4 11 24 25 27     AIC 104,544.43 104,534.24 95,346.62 95,346.61 95,344.16     BIC 104,579.01 104,629.33 95,528.17 95,528.15 95,525.70     Log likelihood –52,268.22 –52,256.12 –47,652.31 –47,652.30 –47,651.08 N (countries) 21 21 21 21 21 N (country–years) 42 42 42 42 42 N (individuals) 41,195 41,195 41,195 41,195 41,195 Model 0 Model 1 Model 2 Model 3 Model 4 Year 2013/14 –0.073 0.071 –0.039 0.062 –0.041 0.061 –0.042 0.061 Woman 0.029* 0.012 0.029* 0.012 0.029* 0.012 Age 0.010*** 0.001 0.010*** 0.001 0.010*** 0.001 Years of education –0.112*** 0.012 –0.112*** 0.012 –0.112*** 0.012 Relative income     Lowest quintile (Ref.)     Second quintile –0.041* 0.016 –0.041* 0.016 –0.041* 0.016     Middle quintile –0.059** 0.022 –0.059** 0.022 –0.059** 0.022     Fourth quintile –0.107** 0.033 –0.107** 0.033 –0.107** 0.033     Highest quintile –0.142*** 0.039 –0.142*** 0.039 –0.142*** 0.039     Otherwise –0.021 0.025 –0.021 0.025 –0.021 0.025 Unemployed 0.000 0.022 0.000 0.022 0.000 0.022 Married 0.022 0.014 0.022 0.014 0.022 0.014 Majority religion 0.484*** 0.042 0.484*** 0.042 0.484*** 0.042 Threat perceptions     No threat (Ref.)     Threat 0.324*** 0.057 0.324*** 0.057 0.324*** 0.057     Otherwise 0.107*** 0.032 0.107*** 0.032 0.107*** 0.032 GDP per capita (1000$) –0.008** 0.003 –0.007* 0.003 –0.007* 0.003 –0.007* 0.003 Change in GDP per capita 0.004 0.003 0.005 0.003 0.005 0.003 0.005 0.004 Democracy scores 0.079* 0.036 0.072 0.039 0.072 0.039 0.065* 0.032 Change in democracy scores –0.031 0.086 –0.009 0.060 –0.007 0.060 0.003 0.059 Share of foreign–born (SFB) –0.003 0.011 –0.012 0.012 –0.012 0.012 –0.029 0.018 Change in SFB –0.041* 0.017 –0.040** 0.013 –0.036 0.022 –0.047 0.025 SFB x Change in SFB 0.000 0.002 0.001 0.002 Change in SFB² –0.074* 0.034 SFB x Change in SFB² 0.006* 0.003 Constant 2.507*** 0.087 2.622*** 0.165 1.824*** 0.193 1.826*** 0.195 1.966*** 0.209 Variance components     Var (Country) 0.138*** 0.069*** 0.070*** 0.070*** 0.061***     Var (Year) 0.026*** 0.016*** 0.014*** 0.014*** 0.014*** Statistics     Df 4 11 24 25 27     AIC 104,544.43 104,534.24 95,346.62 95,346.61 95,344.16     BIC 104,579.01 104,629.33 95,528.17 95,528.15 95,525.70     Log likelihood –52,268.22 –52,256.12 –47,652.31 –47,652.30 –47,651.08 N (countries) 21 21 21 21 21 N (country–years) 42 42 42 42 42 N (individuals) 41,195 41,195 41,195 41,195 41,195 * p < 0.05; ** p < 0.01; *** p < 0.001. Unstandardized coefficients with robust standard errors. Although Model 4 reveals significant interaction effects between shares of foreign-born population and levels of change in these effects vanish when outlier countries, Philippines on the one side and Switzerland on the other, are excluded from the analysis (Table A1 in Appendix). In the end, Hypothesis 3 cannot be confirmed either; the longitudinal effect of diversity is nevertheless robust, even if the outlier countries are dropped. Beyond that, the cross-sectional effect of GDP is also robust across both analyses. Here, higher GDP is associated with less relevance of ascriptive characteristics in defining national identity. This pattern fits into the group threat scenario, in which strong competition (indicated by low GDP per capita) results in exclusionary reactions towards those perceived of as ‘others.’ After removing the outlier countries, there is also an effect of democratic condition in the countries (Table A1 in Appendix). As expected, this effect indicates that the relevance of ascriptive attributes is higher among less democratic countries. 6.2 District-level analysis The scatterplot with linear predicted values by West German districts and the observed years 2004 and 2014 gives a further impression of the relationship between levels of diversity indicated by the shares of individuals with migration background and the importance of ascriptive characteristics in defining national identity (Fig. 2). It exhibits the same pattern as observed at the country level. The importance of ascriptive attributes decreases as diversity across West-German districts increases. How does this pattern play out in the random-intercept models? Figure 2. View largeDownload slide Scatterplot of importance of ascriptive attributes and share of individuals with migration background, by district and years. Note: District codes were used as provided by German General Social Survey. For underlying data and district code assignment, see Table d in online supplement. Figure 2. View largeDownload slide Scatterplot of importance of ascriptive attributes and share of individuals with migration background, by district and years. Note: District codes were used as provided by German General Social Survey. For underlying data and district code assignment, see Table d in online supplement. In contrast to the results of the country-level analysis, the district-level analysis reveals a cross-sectional effect indicating that ascriptive attributes have less relevance in districts with high numbers of individuals with a migration background (Table A2 in Appendix). This is in line with Hypothesis 1. Moreover, the analysis reveals an interaction effect between levels of diversity and change in diversity between 2004 and 2014; Figure 3 illustrates this relationship. Increasing diversity in the observed decade only has an effect in districts with low shares of individuals with a migration background. Here, the relevance of ascriptive attributes declines along with increasing diversity, and an increase by two standard deviations in the share of individuals with migration background removes differences between lesser and more highly diverse districts regarding the importance of ascriptive characteristics in defining national identity. This pattern is in line with Hypothesis 2, which suggests that increasing diversity produces diminishing marginal effects. In other words, highly diverse regions are not sensitive towards changes in diversity when it comes to ascriptive concepts of national identity. Finally, against our expectations formulated in Hypothesis 3, we do not observe a non-linear relationship between levels of diversity and levels of increase in diversity regarding the relevance of ascriptive characteristics in defining national identity. Beyond that, GDP has no effect on the district-level. Figure 3. View largeDownload slide Linear prediction of importance of ascriptive attributes with 95% confidence intervals, by share of individuals with migration background and change in share between 2004 and 2014. Note: For underlying model, see Model 2 in Table A2 in Appendix. Figure 3. View largeDownload slide Linear prediction of importance of ascriptive attributes with 95% confidence intervals, by share of individuals with migration background and change in share between 2004 and 2014. Note: For underlying model, see Model 2 in Table A2 in Appendix. 7. Conclusion In our analysis, we examined the effects of immigration-related diversity on the relevance of ascriptive characteristics in defining national identity (i.e. national identities that are exclusive towards some groups by definition). In contrast to many studies that utilize increasing diversity as a threat to settled citizens, we conceptualized increasing diversity and diversification as a factor of growing experience and familiarity with ‘others’, which should yield a reduced relevance of ascriptive attributes. Our findings support this relationship, both on the country-level and on the district-level based on ISSP data from 2003/4 and 2013/14. Increasing immigration-related diversity is associated with a declining relevance of ascriptive characteristics in defining national identity. Across 21 countries, this effect relies on an increase of diversity in the observed decade. Whereas it relies on an inter-related effect of existing levels of diversity and increase of diversity across 28 West German districts, indicating diminishing marginal effects of increasing diversity in highly diverse regions. These findings suggest that the relationship between diversity and the relevance of ascriptive characteristics in defining national identity is complex and dynamic. An important factor explaining the different patterns between country-level and district-level findings might be the difference in average diversity between both samples, which in turn also results from the measures used. For the country-level analysis, we used the share of foreign-born individuals in the observed countries as an indicator of diversity (often used in other studies). At the district-level, the corresponding indicator was the presence of individual’s migration background (i.e. including immigrants and their descendants) in order to measure ‘levels of diversity’; time and geographic restrictions should be taken into account as further limitations regarding these results. Although data was included from two time points (2003/4 and 2013/14), a trend could not be discerned. Additionally, the timeline could be convenient for perceiving diversity and diversification positively with regard to inclusive concepts of national identity (e.g. cultural globalization; Hall 1991). There were also inconvenient times in which immigration-related diversity evolved out of various power constellations (e.g. slavery or colonialism). Even under those conditions, diversity can question established ascriptive concepts of national identity (Bhabha 1994). Nonetheless, when powerful actors such as states systematically agitate against particular groups and persecute them for the sake of an imagined pure national identity, diversity could also be exterminated (e.g. the systematic persecution and murder of the Jews in Nazi Germany). The results cannot be generalized and need to be regarded in the context of the country/ district samples, as the analysis was only focused on these areas; needless to say, relationships might look different in other contexts. For example, at the local level (e.g., neighborhoods), the relationship between immigration-related diversity and the relevance of ascriptive characteristics in defining national identity might be evolving under more conflictual conditions (e.g. Elias and Scotson 1965). However, the argument that familiarization is a key factor in this process would seem to also hold under these conditions, as long as conflicts were not characterized by extremity (see Cairns et al. 2006); furthermore, initial conflicts can be understood as encounters with a new situation that the public needs to address. In doing so, diversity becomes more and more visible as a part of societal life (Landry and Wood 2008; Wessendorf 2013). We examined the relevance of ascriptive characteristics in defining national identity with a more general approach and with regard to immigration-related diversity within a settled citizens and newcomers’ framework. However, both diversity and the importance of ascriptive attributes can have many facets (Vertovec 2007; Harris et al. 2016). Majority and minority groups among the settled population may have their own conceptions of national identity (e.g. Devos and Banaji 2005), and react differently to diversity changes. Research on intergroup prejudice in multi-ethnic settings indicates that some groups tend to have more resentment towards other groups when they are living in neighborhoods with a higher percentage of their own group (Oliver and Wong 2003: 577). In this respect, a stark outnumbering of some minority groups over others may increase the relevance of specific ascriptive attributes among those minority groups. This suggests, on the one hand, that an influx in immigration might be associated with growing diversity and conceived of as positive by the general population; on the other hand, it might be associated with decreasing diversity and negative perception among the settled local minority group. Further research is needed to address these points. In general, our analysis has demonstrated that immigration-related diversity could reduce the relevance of ascriptive characteristics in defining national identity. Funding There is no funding to declare. Conflict of interest statement. The authors declare that they have no conflict of interest. Supplementary data Supplemental data is available at Migration Studies online. Footnotes 1. It is important to note that supporting inclusive national identities does not mean that individuals will be inclusive with regard to other domains. For example, the presence of immigrant groups settled in a country for two or more generations might have helped to change concepts of national identity to be more inclusive. At the same time, large parts of those groups supporting inclusive national identities may be sensitive to anti-immigration policies (Shackle 2016; Valdes 2016). 2. It is important to note that Wright’s analyses were based on data of the years 1995/96 and 2003/4. We are using data of the years 2003/4 and 2013/14 because one central criteria of belonging to national identity, namely having same ancestry, was not available in 1995. Therefore, those analyses cannot compare reliably with the analysis in this article. Primarily, we are interested in elaborating on further mechanisms that could affect the relevance of ascriptive characteristics in defining national identity. 3. We considered West and East Germany as distinct countries due to their different regime and migration histories. Unfortunately, we had to remove Latvia and Taiwan from our analysis. For Taiwan, GDP values were not available. In Latvia, the dependent variable was not surveyed in 2003. We also excluded Israel from the analysis because it is a demographic outlier. Israel’s population growth and subsequent development have been mainly because of extensive immigration (Friedlander and Goldscheider 1978) and high fertility rates (DellaPergola et al. 2014). 4. Districts were comprised of contemporary administrative districts and former administrative districts. Due to the small account in some districts (< 20), we put three districts together with their neighbor districts (see Table D in online supplement). The information for district assignment was collected from the German General Social Survey (see <http://www.gesis.org/en/allbus/allbus-home/> accessed 1 July 2017). 5. For an overview of the reference years and sources, see Table a in the online supplement. 6. See <https://data.oecd.org/migration/foreign-born-population.htm> accessed 12 January 2017. Estimates for West and East Germany were from the Federal Statistical Office of Germany (see <https://www.destatis.de/EN/Homepage.html> accessed 12 January 2017). For Japan and Korea, we used the share of foreigners because data on the foreign-born population was not available. 7. See <http://www.un.org/en/development/desa/population/> accessed 12 January 2017. As data on the foreign-born population was not available for the Philippines, we took the share of foreigners. 8. See <https://www.destatis.de/EN/Homepage.html> accessed 26 June 2017. 9. We calculated relative income by dividing the household income by the square root of the household population. 10. This variable was created on the basis of a 5-point Likert-type agree/disagree scale employed on the following four items: ‘Immigrants increase crime rates,’ ‘Immigrants are generally good for [country’s] economy,’ ‘Immigrants take jobs away from people who were born in [country],’ ‘Immigrants improve [country’s nationality] society by bringing new ideas and cultures.’ First, we constructed a summary (additive) scale (Cronbach’s alpha = .71), and then we re-categorized the scale at the 75th percentile quartile and considered an ‘otherwise’ category (where the respondent ‘can’t choose’ or refused to answer) so as not to produce more missing values (10.0%). 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Immigration, diversity and the relevance of ascriptive characteristics in defining national identity across 21 countries and 28 West-German districts

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© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com
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Abstract

Abstract The relevance of ascriptive characteristics in defining national identity—i.e. attributes that categorically exclude certain groups from national identity—depends on an individual’s perception of immigration-related diversity. Increasing diversity can stand for both a growing threat as well as growing familiarity. In contrast to many other studies, we conceptualize increasing diversity as a factor of growing familiarity, reducing the relevance of ascriptive attributes. We tested this familiarization thesis using multilevel models based on ISSP data of 21 countries and 28 West German districts surveyed in 2003/4 and 2013/14. We exploit cross-sectional and longitudinal effects. Our findings reveal that increasing diversity reduces the relevance of ascriptive characteristics in defining national identity. At the country level, this relationship relies on longitudinal effects and at the district level, on an interaction of cross-sectional and longitudinal effects. We discuss the results and make suggestions for future research. 1. Introduction In his article ‘Race in the Modern World,’ Appiah (2015: 1) argues that ‘identities rooted in the reality or the fantasy of shared ancestry, in short, remain central in politics, both within and between nations. In this new century, as in the last, the color line and its cousins are still going strong.’ He points to a social phenomenon which could be readily observed in the recent election campaigns in Europe and the USA, in which ethnic/race and religious categories have been important components used to mobilize citizens (Betz and Meret 2009; Gökarıksel and Smith 2016). In this context, national identities can effectively be used to demarcate in- and out-groups within a society, and this demarcation is negotiated both politically and societally (Wimmer 2008). However, when ancestry becomes relevant in defining national identity, descent determines who counts as part of that identity and who does not. Some groups are categorically excluded from that national identity as a result, and becoming part of that identity remains unachievable. Although civic and meritocratic principles are supposed to determine membership into the settled mainstream in modern democratic nation-states (Alba and Nee 2003) descent-based definitions of national identity can be prevalent among the population. For example, people may more swiftly associate the label ‘American’ with those of European descent rather than with others (Devos and Ma 2013). Lines determining in- and out-groups may not initially result in intergroup bias such as hostility toward out-group members (Brewer 1999); however, they could for this purpose. Moreover, the exclusionary definition of national identity yields greater political mobilization than its inclusive counterpart, as a recently published study reveals (Helbling et al. 2016). Radical right-wing politicians are appealing to constituents by sketching multiculturalism and diversity as a threat and appealing to older homogenous identities (Pirro 2014; Loch and Norocel 2015). In this vein, we are mainly interested in how different patterns of immigration-related diversity are associated with the relevance of ascriptive characteristics in defining national identity among settled citizens. For this purpose, we employ country-level and district-level ISSP data from 2003/4 and 2013/14 in order to conduct a multilevel analysis accounting for the cross-sectional and longitudinal characteristics of the data. In this article, immigration-related diversity is in general defined as an occurrence of new identity categories that enter a society through immigration. It also implies the process of diversification, whereby new categories combine with the existing ones (Crisp and Hewstone 2007). Therefore, an increase in immigration also means an increase of diversity and diversification, which can manifest in several ways (e.g. one-way assimilation or hybridization; see Canan 2015). 2. Theoretical frame and research 2.1 National identity and diversity One method of conceptualizing national identity is to apply the ethnic–civic distinction (Kohn 1944; Brubaker 1992). Ethnic concepts of national identity emphasize descent as well as cultural aspects, such as language and customs. By contrast, civic concepts emphasize the legal-political community in which citizens are equal under the law, and membership not determined by descent (Smith 1991). Some criticized the civic–ethnic distinction as too ambiguous for the purposes of empirical analysis (Shulman 2002). More specifically, cultural categories such as language may fall under either concept (Brubaker 1999). Furthermore, ethnic concepts containing cultural categories may suggest openness, but remain nontheless exclusive because they are based on descent (Kymlicka 1999). Wright (2011) proposes a more general distinction that divides the concept of national identity into ascriptive and achievable aspects. While ascriptive characteristics are those that are categorically exclusive, achievable characteristics are those that can be attained. This distinction, seen as more appropriate in the context of migration and integration, recognizes the opportunity for immigrants to achieve membership. In this light, we define the ascriptive characterization of national identity as one that excludes certain groups based on unachievable or hard-to-achieve attributes in the long-term and across generations; for example, skin color or religious affiliation. The question is then: under which conditions do settled citizens regard ascriptive characteristics as relevant in defining national identity? Threat, or the perception of threat, is a very powerful factor in explaining intergroup bias in the context of diversity. One can divide theoretical concepts addressing threat perceptions into two general camps. One group suggests that threat perceptions evolve in the context of competition for scarce resources, and result in exclusionary reactions towards out-groups (e.g. Blumer 1958; Sherif 1966; Blalock 1967). The other group maintains that individuals have a concept of themselves, which is linked to the evaluative connotations of the social categories or groups to which individuals perceive themselves to belong (Tajfel and Turner 1986). A positive social identity—stemming from intergroup comparisons with relevant other groups—helps individuals to maintain their self-concept. Accordingly, social identities are connected to values worthy of protection and perceived threat to those identities result in exclusionary reactions towards out-groups (Branscombe et al. 1999). More than likely, both forms of threat perception are at play when it comes to immigration-related diversity (Sniderman et al. 2004). Against this backdrop, increasing diversity is usually conceptualized as a threat to an individual’s own group resulting in higher anti-immigrant prejudice (Quillian 1995; Sides and Citrin 2007); whereas, factors such as intergroup contact can ease this relationship (Schlueter and Scheepers 2010). This sort of thinking can easily be applied to beliefs or imaginations of national identity; in the face of increasing diversity ‘mainstream populations might respond by adopting a view of their nation drawn along more ascriptive, immigrant-exclusive lines’ (Wright 2011: 842). Yet, the alternative idea of increasing diversity as a source of experience and familiarization might be more suitable in analyzing concepts of national identity, since the empirical factum of diversity and diversification might not be easily ignored.1 The following section outlines this argument and presents the formulated hypotheses. 2.2 Diversity, familiarity and national identity National identity defines a social identity that can enhance self-concept (Tajfel and Turner 1986). It is relatively abstract and therefore can be highly contentious regarding criteria of belonging (Onorato and Turner 2002). In this context, increasing diversity can help to change those criteria to be more inclusive as it enables experience and familiarity with those initially considered ‘others’. More concretely, high levels of diversity means that substantial diversification is ongoing among immigrant groups by adopting characteristics of the native population and combining them with their own characteristics (Canan and Foroutan 2016). Diversification can result in recognition of ‘togetherness’ and reduce intergroup bias, as former out-group members become similar to the in-group (Crisp and Hewstone 2007). Moreover, re-categorization into a common group identity can take place (Gaertner and Dovidio 2000). A broader population can experience diversity by intergroup contact (Pettigrew and Tropp 2006), simple day-to-day encounters (Zajonc 2001; Blokland and Nast 2014), representations (e.g. in politics or media, see Bodenhausen et al. 1995), and societal narratives comprising norms and rights (Foroutan 2016). Along this line, one can formulate the following hypothesis: H1: The higher the levels of diversity, the less important ascriptive characteristics will be in defining national identity. This expectation contradicts Wright (2011), who suggests that high levels of diversity represent a potential threat resulting in higher relevance of ascriptive attributes; his analysis of 16 European countries seems to confirm this relationship (p < 0.1). Moreover, the rapid growth in diversity indicated by the increased share of the foreign-born population is clearly associated with a higher relevance of ascriptive characteristics in defining national identity. However, Wright does not account for potential interactions and non-linear relationships, which would be reasonable with the argument of increasing familiarity.2 Moreover, it can be insightful to look at both the differences between individuals living in regions with different shares of immigrants (between or cross-sectional effects) as well as the differences between individuals exposed to different levels of increase in the shares within regions (within or longitudinal effects) (Fairbrother 2014). How might inter-acting and non-linear patterns appear? First, existing levels of diversity and changes in diversity levels may represent inter-related factors affecting the relevance of ascriptive characteristics in defining national identity. In this respect, increasing diversity over time may be largely effective in decreasing the relevance of ascriptive attributes, especially when the initial levels of existing diversity are low and as familiarization progresses. This argument for the diminishing marginal effects of increasing diversity on the relevance of ascriptive attributes assumes that diversity establishes itself (i.e. social praxis) at a specific point (see Wessendorf 2013), and that society is not sensitive towards changes in diversity that could affect the relevance of ascriptive characteristics in defining national identity as prior a phase of familiarization. In this regard, one can formulate the following expectation: H2: The negative effect of increasing diversity on the relevance of ascriptive characteristics in defining national identity subsides at higher levels of diversity. One can also formulate the argument made in Hypothesis 2 the other way around, with the idea that immigration instigates threat responses. In this scenario, one can perceive increasing diversity from initially low levels as threatening, resulting in the increased relevance of ascriptive attributes due to a lack of familiarity. Newman (2013) demonstrates that among 104 US counties, the positive effect of increasing diversity on cultural threat perceptions decreases along with higher levels of diversity. Similarly, Schneider (2008) reveals that increasing diversity has a positive effect on threat perceptions at low levels of diversity and that effect levels off at higher levels of diversity among 20 European countries. Even if those studies do not analyze concepts of national identity, they point to the plausibility of an alternative explanation. In contrast to the expectation formulated in Hypothesis 2, one can conceive the possibilty that increasing diversity—when levels are low—may strengthen the relevance of ascriptive characteristics in defining national identity due to a lack of familiarity. Concerning the discrepancy between these two expectations, we will consider one further factor that could be engaged in determining the relationship between diversity and the relevance of ascriptive attributes. Another diversity-related factor involves the level of increase in diversity. One may perceive rapid changes in demographics due to very high immigrant inflows as threatening because of the intensity of its occurrence (Hooghe et al. 2009; Hopkins 2010). Especially in regions with low levels of diversity, those changes could be effective in promoting the relevance of ascriptive attributes because of less experience with new identities. If true, then we would expect a curve–linear relationship between levels of increase in diversity and the relevance of ascriptive attributes. More specifically, increasing diversity should support Hypothesis 1 in that it would reduce the relevance of ascriptive attributes. At a specific point, high rates of increase in diversity should spark a backlash that reverses the effect and causes an increase in the relevance of ascriptive attributes in regions with lower diversity. Therefore, one can formulate another hypothesis as follows: H3: Increasing diversity will reduce the relevance of ascriptive characteristics in defining national identity, but high levels of increase in diversity will reverse this effect in regions with low levels of diversity. We expect that the formulated hypotheses will apply on both a large and a small scope. 3. Data To test the hypotheses, we used pooled cross-sectional individual data from the ISSP on national identity from 21 countries3 and from 28 districts in West Germany4 surveyed in the years 2003/4 and 2013/14. With regard to the multilevel structure of the analysis, we supplemented the data with further country-level estimates from the Organisation for Economic Co-operation and Development (OECD), the United Nations Population Division, the World Bank, and the Freedom House Index (discussed below). Additional information with regard to the West German districts stem from Federal Statistical Office of Germany, Regional Accounts (VGRdL) provided by the Federal Statistical Office, and the Statistical Offices of the Länder, and German General Social Survey. When available, we used country-level and district-level data in correspondence with the survey year. When these data were not available, we refer to the next year.5 As we were interested in the settled citizens’ views on the relevance of ascriptive attributes in the face of diversity, we only considered respondents when they matched the survey country’s nationality and when their parents possessed the survey country’s nationality at the time when the respondents were born. The data originally consisted of 47,333 cases. Due to missing data, the analysis was conducted based on 41,195 cases. The districts sample consisted of 1690 and 1617 cases, respectively. 4. Operationalization The dependent variable indicating the relevance of the ascriptive characteristics in defining national identity was measured using two items. Respondents were asked about the relative importance they assign to the following factors in identifying who they considered a true member of that nation: having ancestry that matched the nationality of the country, and belonging to the dominant religion of the country. Both criteria can be used to construct a homogenous national identity and thus categorically exclude immigrants (and other minorities) from the imagined national collective in the long run and across generations (Shooman 2015). These criteria are either difficult or impossible for immigrants to achieve. A four-point Likert-type scale measured the perceived importance of each of these criteria. We constructed a mean-based scale, where missing values were excluded and higher values indicated a higher importance of ascriptive attributes (1 = not important at all to 4 = very important; Cronbach’s alpha = 0.67). 4.1 Relevant independent variables As elsewhere (e.g. Wright 2011; Reeskens and Wright 2013), diversity was measured by the share of foreign-born individuals in the observed countries (0.2%–28.3%). For the 19 OECD countries in the analysis, we used estimates from the OECD database.6 For the two non-OECD countries, we used estimates from the United Nations Population Division.7 By contrast, concerning the districts of West Germany, we used the share of individuals that possess a migration background as an indicator for diversity (12.5%–31.9%), where a ‘migration background’ refers to the foreign-born population and their descendants that were born in Germany.8 To the best of our knowledge, a comparable standardized measure internationally was not available on the country level at that time. 4.2 Control variables On the individual level, we controlled for standard demographic variables. We included the respondent’s gender (0 = man; 1 = woman), age (18–97) and education. Due to implausible extreme values (e.g. 82 years of education), the education variable was truncated at the 99.5th percentile, afterwards, ranging from 0 years to 23 years of education. This variable was centered at the country means in order to have the same standard deviation for each country. It was also considered whether the respondent was currently unemployed (0 = no; 1 = yes), as well as marital status (0 = not married; 1 = married) and relative income.9 Because income in each national survey was not uniformly measured the variable was re-categorized into its quintiles for each country. We also considered a non-answer category, as 19.2% of the respondents did not report their income (1 = first quintile; 2 = second quintile; 3 = third quintile; 4 = fourth quintile; 5 = fifth quintile; 6 = otherwise). Additionally, religious affiliation was measured with a dummy variable, indicating whether respondents affiliate with the majority religion or not (0 = no; 1 = yes). Finally, threat perception was another important variable that was measured by a categorical variable indicating whether respondents generally perceive immigrants as threatening for the society (0 = no threat; 1 = threat; 3 = otherwise).10 Aside from the individual-level variables, which also were used for both country- and district-level analysis, we also included country/district-level variables (for summary statistics, see Table b in online supplement). We used GDP per capita as a control variable to account for economic conditions in the countries (US$1011–102,910) or in the West German districts (€19,183–56,066). Strong and increasing economic conditions soften competition (e.g. Semyonov et al. 2006), and therefore may negatively impact the relevance of ascriptive characteristics in defining national identity; GDP per capita estimates were gathered from the World Bank and Regional Accounts (VGRdL).11 Furthermore, we considered conditions of democracy in the observed countries. Strong and increasing conditions of democracy can promote civic elements of national identity (Mansfield and Snyder 2005) and may therefore reduce the relevance of ascriptive attributes. We used the Freedom House Index on civil and political rights.12 Two 7-point scales measured the endorsement of these rights, with higher numerical scores indicating less civil and political freedom. We combined the scales (2–14) whereas higher scores indicating a lower endorsement of democratic values in the countries. 5. Methods We applied multilevel linear regression that considered cross-sectional and longitudinal relationships (Fairbrother 2014). To account for potentially biased standard errors stemming from the complex data structure of countries and years, we employed three-level random intercept models (Raudenbush and Bryk 2002); individuals nested in country-years, and country-years nested within countries. Cross-sectional and longitudinal relationships formulated in the hypotheses are modelled by computing the group means within each country over the two years and subtracting them from the year-specific country estimates. With regard to the diversity variable, the country mean indicates cross-sectional level of diversity and the difference within the countries represents longitudinal changes in diversity. Both terms were included in the regression analysis; the same approach was applied to the district data. 6. Results 6.1 Country-level analysis Figure 1 shows a scatterplot with linear-predicted values by countries and the observed years 2003/4 and 2013/14. It gives an initial impression of the relationship between levels of diversity indicated by the percentage of foreign-born individuals and the importance of ascriptive characteristics in defining national identity. Figure 1. View largeDownload slide Scatterplot of importance of ascriptive attributes and share of foreign-born individuals, by country and years. Note: For underlying data and country abbreviation assignment, see Table c in online supplement. Figure 1. View largeDownload slide Scatterplot of importance of ascriptive attributes and share of foreign-born individuals, by country and years. Note: For underlying data and country abbreviation assignment, see Table c in online supplement. The regression line suggests that the importance of ascriptive attributes decreases along with increasing diversity. Beyond that, both the Philippines and Switzerland appear to be outliers that could influence the overall results. Therefore, we will also conduct models without these countries included. How is the relationship between the relevance of ascriptive attributes and diversity determined in models that are more comprehensive? Model 1 (only country-level variables) and Model 2 (including individual-level variables) in Table 1 reveal that higher diversity is associated with less relevance of ascriptive characteristics in defining national identity. This confirms Hypothesis 1. Moreover, this effect relies on the increase of diversity between the years 2003/4 and 2013/14. Against our expectations in Hypothesis 2 there are no significant interaction effects (Model 3) that indicated an inter-related effect between levels of diversity and changes in levels of diversity in the observed decade. Table 1. Mixed-effects linear regression analysis on the importance of ascriptive characteristics in defining national identity Model 0 Model 1 Model 2 Model 3 Model 4 Year 2013/14 –0.073 0.071 –0.039 0.062 –0.041 0.061 –0.042 0.061 Woman 0.029* 0.012 0.029* 0.012 0.029* 0.012 Age 0.010*** 0.001 0.010*** 0.001 0.010*** 0.001 Years of education –0.112*** 0.012 –0.112*** 0.012 –0.112*** 0.012 Relative income     Lowest quintile (Ref.)     Second quintile –0.041* 0.016 –0.041* 0.016 –0.041* 0.016     Middle quintile –0.059** 0.022 –0.059** 0.022 –0.059** 0.022     Fourth quintile –0.107** 0.033 –0.107** 0.033 –0.107** 0.033     Highest quintile –0.142*** 0.039 –0.142*** 0.039 –0.142*** 0.039     Otherwise –0.021 0.025 –0.021 0.025 –0.021 0.025 Unemployed 0.000 0.022 0.000 0.022 0.000 0.022 Married 0.022 0.014 0.022 0.014 0.022 0.014 Majority religion 0.484*** 0.042 0.484*** 0.042 0.484*** 0.042 Threat perceptions     No threat (Ref.)     Threat 0.324*** 0.057 0.324*** 0.057 0.324*** 0.057     Otherwise 0.107*** 0.032 0.107*** 0.032 0.107*** 0.032 GDP per capita (1000$) –0.008** 0.003 –0.007* 0.003 –0.007* 0.003 –0.007* 0.003 Change in GDP per capita 0.004 0.003 0.005 0.003 0.005 0.003 0.005 0.004 Democracy scores 0.079* 0.036 0.072 0.039 0.072 0.039 0.065* 0.032 Change in democracy scores –0.031 0.086 –0.009 0.060 –0.007 0.060 0.003 0.059 Share of foreign–born (SFB) –0.003 0.011 –0.012 0.012 –0.012 0.012 –0.029 0.018 Change in SFB –0.041* 0.017 –0.040** 0.013 –0.036 0.022 –0.047 0.025 SFB x Change in SFB 0.000 0.002 0.001 0.002 Change in SFB² –0.074* 0.034 SFB x Change in SFB² 0.006* 0.003 Constant 2.507*** 0.087 2.622*** 0.165 1.824*** 0.193 1.826*** 0.195 1.966*** 0.209 Variance components     Var (Country) 0.138*** 0.069*** 0.070*** 0.070*** 0.061***     Var (Year) 0.026*** 0.016*** 0.014*** 0.014*** 0.014*** Statistics     Df 4 11 24 25 27     AIC 104,544.43 104,534.24 95,346.62 95,346.61 95,344.16     BIC 104,579.01 104,629.33 95,528.17 95,528.15 95,525.70     Log likelihood –52,268.22 –52,256.12 –47,652.31 –47,652.30 –47,651.08 N (countries) 21 21 21 21 21 N (country–years) 42 42 42 42 42 N (individuals) 41,195 41,195 41,195 41,195 41,195 Model 0 Model 1 Model 2 Model 3 Model 4 Year 2013/14 –0.073 0.071 –0.039 0.062 –0.041 0.061 –0.042 0.061 Woman 0.029* 0.012 0.029* 0.012 0.029* 0.012 Age 0.010*** 0.001 0.010*** 0.001 0.010*** 0.001 Years of education –0.112*** 0.012 –0.112*** 0.012 –0.112*** 0.012 Relative income     Lowest quintile (Ref.)     Second quintile –0.041* 0.016 –0.041* 0.016 –0.041* 0.016     Middle quintile –0.059** 0.022 –0.059** 0.022 –0.059** 0.022     Fourth quintile –0.107** 0.033 –0.107** 0.033 –0.107** 0.033     Highest quintile –0.142*** 0.039 –0.142*** 0.039 –0.142*** 0.039     Otherwise –0.021 0.025 –0.021 0.025 –0.021 0.025 Unemployed 0.000 0.022 0.000 0.022 0.000 0.022 Married 0.022 0.014 0.022 0.014 0.022 0.014 Majority religion 0.484*** 0.042 0.484*** 0.042 0.484*** 0.042 Threat perceptions     No threat (Ref.)     Threat 0.324*** 0.057 0.324*** 0.057 0.324*** 0.057     Otherwise 0.107*** 0.032 0.107*** 0.032 0.107*** 0.032 GDP per capita (1000$) –0.008** 0.003 –0.007* 0.003 –0.007* 0.003 –0.007* 0.003 Change in GDP per capita 0.004 0.003 0.005 0.003 0.005 0.003 0.005 0.004 Democracy scores 0.079* 0.036 0.072 0.039 0.072 0.039 0.065* 0.032 Change in democracy scores –0.031 0.086 –0.009 0.060 –0.007 0.060 0.003 0.059 Share of foreign–born (SFB) –0.003 0.011 –0.012 0.012 –0.012 0.012 –0.029 0.018 Change in SFB –0.041* 0.017 –0.040** 0.013 –0.036 0.022 –0.047 0.025 SFB x Change in SFB 0.000 0.002 0.001 0.002 Change in SFB² –0.074* 0.034 SFB x Change in SFB² 0.006* 0.003 Constant 2.507*** 0.087 2.622*** 0.165 1.824*** 0.193 1.826*** 0.195 1.966*** 0.209 Variance components     Var (Country) 0.138*** 0.069*** 0.070*** 0.070*** 0.061***     Var (Year) 0.026*** 0.016*** 0.014*** 0.014*** 0.014*** Statistics     Df 4 11 24 25 27     AIC 104,544.43 104,534.24 95,346.62 95,346.61 95,344.16     BIC 104,579.01 104,629.33 95,528.17 95,528.15 95,525.70     Log likelihood –52,268.22 –52,256.12 –47,652.31 –47,652.30 –47,651.08 N (countries) 21 21 21 21 21 N (country–years) 42 42 42 42 42 N (individuals) 41,195 41,195 41,195 41,195 41,195 * p < 0.05; ** p < 0.01; *** p < 0.001. Unstandardized coefficients with robust standard errors. Table 1. Mixed-effects linear regression analysis on the importance of ascriptive characteristics in defining national identity Model 0 Model 1 Model 2 Model 3 Model 4 Year 2013/14 –0.073 0.071 –0.039 0.062 –0.041 0.061 –0.042 0.061 Woman 0.029* 0.012 0.029* 0.012 0.029* 0.012 Age 0.010*** 0.001 0.010*** 0.001 0.010*** 0.001 Years of education –0.112*** 0.012 –0.112*** 0.012 –0.112*** 0.012 Relative income     Lowest quintile (Ref.)     Second quintile –0.041* 0.016 –0.041* 0.016 –0.041* 0.016     Middle quintile –0.059** 0.022 –0.059** 0.022 –0.059** 0.022     Fourth quintile –0.107** 0.033 –0.107** 0.033 –0.107** 0.033     Highest quintile –0.142*** 0.039 –0.142*** 0.039 –0.142*** 0.039     Otherwise –0.021 0.025 –0.021 0.025 –0.021 0.025 Unemployed 0.000 0.022 0.000 0.022 0.000 0.022 Married 0.022 0.014 0.022 0.014 0.022 0.014 Majority religion 0.484*** 0.042 0.484*** 0.042 0.484*** 0.042 Threat perceptions     No threat (Ref.)     Threat 0.324*** 0.057 0.324*** 0.057 0.324*** 0.057     Otherwise 0.107*** 0.032 0.107*** 0.032 0.107*** 0.032 GDP per capita (1000$) –0.008** 0.003 –0.007* 0.003 –0.007* 0.003 –0.007* 0.003 Change in GDP per capita 0.004 0.003 0.005 0.003 0.005 0.003 0.005 0.004 Democracy scores 0.079* 0.036 0.072 0.039 0.072 0.039 0.065* 0.032 Change in democracy scores –0.031 0.086 –0.009 0.060 –0.007 0.060 0.003 0.059 Share of foreign–born (SFB) –0.003 0.011 –0.012 0.012 –0.012 0.012 –0.029 0.018 Change in SFB –0.041* 0.017 –0.040** 0.013 –0.036 0.022 –0.047 0.025 SFB x Change in SFB 0.000 0.002 0.001 0.002 Change in SFB² –0.074* 0.034 SFB x Change in SFB² 0.006* 0.003 Constant 2.507*** 0.087 2.622*** 0.165 1.824*** 0.193 1.826*** 0.195 1.966*** 0.209 Variance components     Var (Country) 0.138*** 0.069*** 0.070*** 0.070*** 0.061***     Var (Year) 0.026*** 0.016*** 0.014*** 0.014*** 0.014*** Statistics     Df 4 11 24 25 27     AIC 104,544.43 104,534.24 95,346.62 95,346.61 95,344.16     BIC 104,579.01 104,629.33 95,528.17 95,528.15 95,525.70     Log likelihood –52,268.22 –52,256.12 –47,652.31 –47,652.30 –47,651.08 N (countries) 21 21 21 21 21 N (country–years) 42 42 42 42 42 N (individuals) 41,195 41,195 41,195 41,195 41,195 Model 0 Model 1 Model 2 Model 3 Model 4 Year 2013/14 –0.073 0.071 –0.039 0.062 –0.041 0.061 –0.042 0.061 Woman 0.029* 0.012 0.029* 0.012 0.029* 0.012 Age 0.010*** 0.001 0.010*** 0.001 0.010*** 0.001 Years of education –0.112*** 0.012 –0.112*** 0.012 –0.112*** 0.012 Relative income     Lowest quintile (Ref.)     Second quintile –0.041* 0.016 –0.041* 0.016 –0.041* 0.016     Middle quintile –0.059** 0.022 –0.059** 0.022 –0.059** 0.022     Fourth quintile –0.107** 0.033 –0.107** 0.033 –0.107** 0.033     Highest quintile –0.142*** 0.039 –0.142*** 0.039 –0.142*** 0.039     Otherwise –0.021 0.025 –0.021 0.025 –0.021 0.025 Unemployed 0.000 0.022 0.000 0.022 0.000 0.022 Married 0.022 0.014 0.022 0.014 0.022 0.014 Majority religion 0.484*** 0.042 0.484*** 0.042 0.484*** 0.042 Threat perceptions     No threat (Ref.)     Threat 0.324*** 0.057 0.324*** 0.057 0.324*** 0.057     Otherwise 0.107*** 0.032 0.107*** 0.032 0.107*** 0.032 GDP per capita (1000$) –0.008** 0.003 –0.007* 0.003 –0.007* 0.003 –0.007* 0.003 Change in GDP per capita 0.004 0.003 0.005 0.003 0.005 0.003 0.005 0.004 Democracy scores 0.079* 0.036 0.072 0.039 0.072 0.039 0.065* 0.032 Change in democracy scores –0.031 0.086 –0.009 0.060 –0.007 0.060 0.003 0.059 Share of foreign–born (SFB) –0.003 0.011 –0.012 0.012 –0.012 0.012 –0.029 0.018 Change in SFB –0.041* 0.017 –0.040** 0.013 –0.036 0.022 –0.047 0.025 SFB x Change in SFB 0.000 0.002 0.001 0.002 Change in SFB² –0.074* 0.034 SFB x Change in SFB² 0.006* 0.003 Constant 2.507*** 0.087 2.622*** 0.165 1.824*** 0.193 1.826*** 0.195 1.966*** 0.209 Variance components     Var (Country) 0.138*** 0.069*** 0.070*** 0.070*** 0.061***     Var (Year) 0.026*** 0.016*** 0.014*** 0.014*** 0.014*** Statistics     Df 4 11 24 25 27     AIC 104,544.43 104,534.24 95,346.62 95,346.61 95,344.16     BIC 104,579.01 104,629.33 95,528.17 95,528.15 95,525.70     Log likelihood –52,268.22 –52,256.12 –47,652.31 –47,652.30 –47,651.08 N (countries) 21 21 21 21 21 N (country–years) 42 42 42 42 42 N (individuals) 41,195 41,195 41,195 41,195 41,195 * p < 0.05; ** p < 0.01; *** p < 0.001. Unstandardized coefficients with robust standard errors. Although Model 4 reveals significant interaction effects between shares of foreign-born population and levels of change in these effects vanish when outlier countries, Philippines on the one side and Switzerland on the other, are excluded from the analysis (Table A1 in Appendix). In the end, Hypothesis 3 cannot be confirmed either; the longitudinal effect of diversity is nevertheless robust, even if the outlier countries are dropped. Beyond that, the cross-sectional effect of GDP is also robust across both analyses. Here, higher GDP is associated with less relevance of ascriptive characteristics in defining national identity. This pattern fits into the group threat scenario, in which strong competition (indicated by low GDP per capita) results in exclusionary reactions towards those perceived of as ‘others.’ After removing the outlier countries, there is also an effect of democratic condition in the countries (Table A1 in Appendix). As expected, this effect indicates that the relevance of ascriptive attributes is higher among less democratic countries. 6.2 District-level analysis The scatterplot with linear predicted values by West German districts and the observed years 2004 and 2014 gives a further impression of the relationship between levels of diversity indicated by the shares of individuals with migration background and the importance of ascriptive characteristics in defining national identity (Fig. 2). It exhibits the same pattern as observed at the country level. The importance of ascriptive attributes decreases as diversity across West-German districts increases. How does this pattern play out in the random-intercept models? Figure 2. View largeDownload slide Scatterplot of importance of ascriptive attributes and share of individuals with migration background, by district and years. Note: District codes were used as provided by German General Social Survey. For underlying data and district code assignment, see Table d in online supplement. Figure 2. View largeDownload slide Scatterplot of importance of ascriptive attributes and share of individuals with migration background, by district and years. Note: District codes were used as provided by German General Social Survey. For underlying data and district code assignment, see Table d in online supplement. In contrast to the results of the country-level analysis, the district-level analysis reveals a cross-sectional effect indicating that ascriptive attributes have less relevance in districts with high numbers of individuals with a migration background (Table A2 in Appendix). This is in line with Hypothesis 1. Moreover, the analysis reveals an interaction effect between levels of diversity and change in diversity between 2004 and 2014; Figure 3 illustrates this relationship. Increasing diversity in the observed decade only has an effect in districts with low shares of individuals with a migration background. Here, the relevance of ascriptive attributes declines along with increasing diversity, and an increase by two standard deviations in the share of individuals with migration background removes differences between lesser and more highly diverse districts regarding the importance of ascriptive characteristics in defining national identity. This pattern is in line with Hypothesis 2, which suggests that increasing diversity produces diminishing marginal effects. In other words, highly diverse regions are not sensitive towards changes in diversity when it comes to ascriptive concepts of national identity. Finally, against our expectations formulated in Hypothesis 3, we do not observe a non-linear relationship between levels of diversity and levels of increase in diversity regarding the relevance of ascriptive characteristics in defining national identity. Beyond that, GDP has no effect on the district-level. Figure 3. View largeDownload slide Linear prediction of importance of ascriptive attributes with 95% confidence intervals, by share of individuals with migration background and change in share between 2004 and 2014. Note: For underlying model, see Model 2 in Table A2 in Appendix. Figure 3. View largeDownload slide Linear prediction of importance of ascriptive attributes with 95% confidence intervals, by share of individuals with migration background and change in share between 2004 and 2014. Note: For underlying model, see Model 2 in Table A2 in Appendix. 7. Conclusion In our analysis, we examined the effects of immigration-related diversity on the relevance of ascriptive characteristics in defining national identity (i.e. national identities that are exclusive towards some groups by definition). In contrast to many studies that utilize increasing diversity as a threat to settled citizens, we conceptualized increasing diversity and diversification as a factor of growing experience and familiarity with ‘others’, which should yield a reduced relevance of ascriptive attributes. Our findings support this relationship, both on the country-level and on the district-level based on ISSP data from 2003/4 and 2013/14. Increasing immigration-related diversity is associated with a declining relevance of ascriptive characteristics in defining national identity. Across 21 countries, this effect relies on an increase of diversity in the observed decade. Whereas it relies on an inter-related effect of existing levels of diversity and increase of diversity across 28 West German districts, indicating diminishing marginal effects of increasing diversity in highly diverse regions. These findings suggest that the relationship between diversity and the relevance of ascriptive characteristics in defining national identity is complex and dynamic. An important factor explaining the different patterns between country-level and district-level findings might be the difference in average diversity between both samples, which in turn also results from the measures used. For the country-level analysis, we used the share of foreign-born individuals in the observed countries as an indicator of diversity (often used in other studies). At the district-level, the corresponding indicator was the presence of individual’s migration background (i.e. including immigrants and their descendants) in order to measure ‘levels of diversity’; time and geographic restrictions should be taken into account as further limitations regarding these results. Although data was included from two time points (2003/4 and 2013/14), a trend could not be discerned. Additionally, the timeline could be convenient for perceiving diversity and diversification positively with regard to inclusive concepts of national identity (e.g. cultural globalization; Hall 1991). There were also inconvenient times in which immigration-related diversity evolved out of various power constellations (e.g. slavery or colonialism). Even under those conditions, diversity can question established ascriptive concepts of national identity (Bhabha 1994). Nonetheless, when powerful actors such as states systematically agitate against particular groups and persecute them for the sake of an imagined pure national identity, diversity could also be exterminated (e.g. the systematic persecution and murder of the Jews in Nazi Germany). The results cannot be generalized and need to be regarded in the context of the country/ district samples, as the analysis was only focused on these areas; needless to say, relationships might look different in other contexts. For example, at the local level (e.g., neighborhoods), the relationship between immigration-related diversity and the relevance of ascriptive characteristics in defining national identity might be evolving under more conflictual conditions (e.g. Elias and Scotson 1965). However, the argument that familiarization is a key factor in this process would seem to also hold under these conditions, as long as conflicts were not characterized by extremity (see Cairns et al. 2006); furthermore, initial conflicts can be understood as encounters with a new situation that the public needs to address. In doing so, diversity becomes more and more visible as a part of societal life (Landry and Wood 2008; Wessendorf 2013). We examined the relevance of ascriptive characteristics in defining national identity with a more general approach and with regard to immigration-related diversity within a settled citizens and newcomers’ framework. However, both diversity and the importance of ascriptive attributes can have many facets (Vertovec 2007; Harris et al. 2016). Majority and minority groups among the settled population may have their own conceptions of national identity (e.g. Devos and Banaji 2005), and react differently to diversity changes. Research on intergroup prejudice in multi-ethnic settings indicates that some groups tend to have more resentment towards other groups when they are living in neighborhoods with a higher percentage of their own group (Oliver and Wong 2003: 577). In this respect, a stark outnumbering of some minority groups over others may increase the relevance of specific ascriptive attributes among those minority groups. This suggests, on the one hand, that an influx in immigration might be associated with growing diversity and conceived of as positive by the general population; on the other hand, it might be associated with decreasing diversity and negative perception among the settled local minority group. Further research is needed to address these points. In general, our analysis has demonstrated that immigration-related diversity could reduce the relevance of ascriptive characteristics in defining national identity. Funding There is no funding to declare. Conflict of interest statement. The authors declare that they have no conflict of interest. Supplementary data Supplemental data is available at Migration Studies online. Footnotes 1. It is important to note that supporting inclusive national identities does not mean that individuals will be inclusive with regard to other domains. For example, the presence of immigrant groups settled in a country for two or more generations might have helped to change concepts of national identity to be more inclusive. At the same time, large parts of those groups supporting inclusive national identities may be sensitive to anti-immigration policies (Shackle 2016; Valdes 2016). 2. It is important to note that Wright’s analyses were based on data of the years 1995/96 and 2003/4. We are using data of the years 2003/4 and 2013/14 because one central criteria of belonging to national identity, namely having same ancestry, was not available in 1995. Therefore, those analyses cannot compare reliably with the analysis in this article. Primarily, we are interested in elaborating on further mechanisms that could affect the relevance of ascriptive characteristics in defining national identity. 3. We considered West and East Germany as distinct countries due to their different regime and migration histories. Unfortunately, we had to remove Latvia and Taiwan from our analysis. For Taiwan, GDP values were not available. In Latvia, the dependent variable was not surveyed in 2003. We also excluded Israel from the analysis because it is a demographic outlier. Israel’s population growth and subsequent development have been mainly because of extensive immigration (Friedlander and Goldscheider 1978) and high fertility rates (DellaPergola et al. 2014). 4. Districts were comprised of contemporary administrative districts and former administrative districts. Due to the small account in some districts (< 20), we put three districts together with their neighbor districts (see Table D in online supplement). The information for district assignment was collected from the German General Social Survey (see <http://www.gesis.org/en/allbus/allbus-home/> accessed 1 July 2017). 5. For an overview of the reference years and sources, see Table a in the online supplement. 6. See <https://data.oecd.org/migration/foreign-born-population.htm> accessed 12 January 2017. Estimates for West and East Germany were from the Federal Statistical Office of Germany (see <https://www.destatis.de/EN/Homepage.html> accessed 12 January 2017). For Japan and Korea, we used the share of foreigners because data on the foreign-born population was not available. 7. See <http://www.un.org/en/development/desa/population/> accessed 12 January 2017. As data on the foreign-born population was not available for the Philippines, we took the share of foreigners. 8. See <https://www.destatis.de/EN/Homepage.html> accessed 26 June 2017. 9. We calculated relative income by dividing the household income by the square root of the household population. 10. This variable was created on the basis of a 5-point Likert-type agree/disagree scale employed on the following four items: ‘Immigrants increase crime rates,’ ‘Immigrants are generally good for [country’s] economy,’ ‘Immigrants take jobs away from people who were born in [country],’ ‘Immigrants improve [country’s nationality] society by bringing new ideas and cultures.’ First, we constructed a summary (additive) scale (Cronbach’s alpha = .71), and then we re-categorized the scale at the 75th percentile quartile and considered an ‘otherwise’ category (where the respondent ‘can’t choose’ or refused to answer) so as not to produce more missing values (10.0%). 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Migration StudiesOxford University Press

Published: Mar 27, 2018

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