Immigrant Naturalization in the Context of Institutional Diversity: Policy Matters, but to Whom?

Authors


Abstract

Why do some immigrants naturalize and others not? While much of the literature emphasizes the importance of country of origin features and individual characteristics, there is surprisingly little systematic research on the relation between citizenship policies in destination countries and citizenship take-up among immigrants. Most research in this field draws on data from single country cases and has limited comparative scope. In this paper we analyze citizenship take-up among first generation immigrants in 16 European countries. We apply an explicit cross-national perspective and argue that immigrant naturalization in Europe is determined not only by country of origin features and individual characteristics, but also by the opportunity structure set by the citizenship laws in the countries of origin and destination. We show that more accessible citizenship policies matter little for immigrants from highly developed countries, particularly those with fewer years of residence, but matter significantly for immigrants from less developed countries. As the composition of immigrant populations and citizenship policies across Europe vary significantly, this comparative design is ideally suited to testing the relative importance of factors related to country of origin, individual background and legal opportunity structure.

Introduction

The question of immigrant naturalization is not a new question in the migration literature. A well developed body of research looks at the determinants of naturalization, mostly but not exclusively in the North American context (North, 1987; Portes and Curtis, 1987; Yang, 1994; Jones-Correa, 2001; Chiswick and Miller, 2008; DeVoretz and Pivnenko, 2004; Bloemraad, 2002; Rallu, 2011; Liebig and Von Haaren, 2011). Typically, these studies look at a range of individual characteristics, such as educational attainment, age at migration, years of residence, family situation and, relating to country of origin, economic development, the political situation and toleration of dual citizenship (for a recent comprehensive overview and analysis, see Chiswick and Miller, 2008).

While these studies have contributed to our understanding of the determination of citizenship take-up among immigrants, their comparative scope is surprisingly limited, from the perspective of migration destination countries. Most studies focus on the North American context, with key contributions looking in particular at the case of the US (Yang, 1994; Jones-Correa, 2001; Chiswick and Miller, 2008; see also DeVoretz and Pivnenko, 2004 on Canada). Some notable exceptions exist, though at best they compare a few countries. In the context of the ‘naturalization gap’ between Canada and the US, for example, important work draws attention to the extent to which naturalization is institutionally encouraged (Bloemraad, 2002; Picot and Hou, 2011). Other studies have investigated the relevance of the citizenship legislation in countries of origin, in particular in relation to toleration of dual citizenship (Jones-Correa, 2001). These examples, however, are exceptions confirming the rule, as we are still a long way from understanding the relationship between country of origin features, individual characteristics and the institutional opportunity structure in which naturalization takes place.

In particular, in Europe, where citizenship policies differ substantially (Vink and De Groot, 2010), we see large differences in citizenship take-up rates, with around 80 per cent of the foreign-born population naturalized after at least ten years residence in the Netherlands and Sweden, but only around 35 per cent of a comparable group in Germany and Switzerland (Liebig and Von Haaren, 2011: 28). The logical question is thus: are these differences in citizenship take-up rates explained by differences in the demographic composition of the immigrant population, or rather by the institutional structure made up of citizenship policies in the countries of origin and destination? We cannot answer this important policy-relevant question without an explicit cross-national comparison. Hence, as both the composition of immigrant populations and citizenship policies across Europe vary significantly, a comparative design provides an ideal set-up for testing a more comprehensive framework which includes, in addition to the individual characteristics and origin country features, aspects of the opportunity structures in destination countries.

Our aim in this article is thus to contribute to the literature on the determinants of citizenship take-up rates among immigrants by making use of the under-explored demographic and institutional diversity provided by European countries. Building on earlier work by Dronkers and Vink (2012), we analyse the relevance of the legal opportunity structure determined by citizenship policies in origin and destination countries, taking into account individual characteristics of immigrants and origin country features. We apply this framework to an empirical analysis of citizenship take-up rates among immigrants in 16 European states.

In the next section we develop our theoretical framework. Subsequently, we discuss data and measurement, followed by a presentation of the analysis. We end with a summary of our findings and a discussion of the policy implications.

Theoretical Framework

Citizenship is a legal status and expresses a relationship between an individual and a state that entails specific legal rights and duties. As for the rights attached to citizenship, the most important right associated with citizenship is the protection by the state and unrestricted access to the territory. Even if alternative permanent residence statuses, such as the green card in the US, provide sufficient security of residence and strong protection against expulsion, ‘naturalization’ ultimately transforms a foreigner into a citizen. Citizenship provides additional privileges, such as diplomatic protection, the right to vote, and access to public sector jobs, to name a few.

Economic studies have shown that citizenship matters in particular for the employability of immigrants and their incomes. Naturalization increases employability as employers take into account the administrative costs of hiring foreigners and verifying rights to work (Bevelander and DeVoretz, 2008; Bratsberg et al., 2002). Naturalization can also be a ‘signalling device’ to employers about the better integration of potential workers, as citizenship is often associated with better language mastery (Liebig and Von Haaren, 2011: 17). Finally, while foreigners may even have the right to participate in political elections, at local or regional level, suffrage in national elections remains largely exclusive to citizens, with a few exceptions (notably Brazilians in Portugal and Irish and British citizens in the UK and Ireland, respectively).

Although the acquisition of citizenship can offer significant benefits, we know that some immigrants naturalize and other do not. Why is that? Yang (1994: 457) argues that immigrants’ perceptions of the costs, benefits and meaning of naturalization are conditioned principally by the socio-economic situation in their countries of origin: insecurity, poor economic conditions and low standards of living may deter immigrants from desiring to return to their homelands. In other words, citizenship provides security, but the utility of naturalization is appreciated differently among immigrant groups, depending on their country of origin context (Jasso and Rosenzweig, 1986: 303; Bueker, 2005; Logan et al., 2012). We thus expect, certainly in developed European countries (as in North America), that the citizenship take-up rate is higher among immigrants from less developed or lower-income countries.

While we expect that origin context is the primary factor in the process of coming to a decision on whether or not to naturalize, we assume that differences will still exist among immigrants in their perceptions of the chances of life improvement secured by citizenship, even within groups coming from countries of origin with relatively similar levels of development. If seen as a life-course event (Tucci, 2011), citizenship take-up is likely to be influenced by expectations and ambitions related to an individual's life situation. For example, we expect that residence matters: the longer an immigrant resides in a country, the higher the expectation of legal incorporation in the host country community. Existing research has shown this to be one of the best individual-level predictors of naturalization (e.g. Bueker 2006: 132; Dronkers and Vink, 2012). Additionally, immigrants who are married and those who have children may also be more strongly motivated to acquire citizenship, as fulfillment either of their own life-course project, or that of their spouse and/or children. Another important individual characteristic which can be assumed to positively affect the ability to qualify for citizenship is language competence. Jasso and Rosenzweig (1986: 305) observe that, for the USA, ‘coming from a country in which English is an official language facilitates naturalization, for which knowledge of the English language is a requirement.’ Yang (1994: 468) confirms these findings. We thus expect that immigrants who speak the language of the host country at home are more likely to acquire citizenship of that country. We thus hypothesize that years of residence, being married and having children, as well as speaking the language of the host country at home positively affect the propensity to naturalize. These four factors are not original, but recur in most micro-level investigations (e.g. Yang, 1994; Chiswick and Miller, 2008; Dronkers and Vink, 2012) and thus require a place in any comprehensive analysis of citizenship take-up among immigrants.

Crucial determinants, in line with the literature, are socio economic factors such as human capital (educational attainment, occupational status) and employment status. There are two key reasons why we would expect that higher levels of human capital would increase the propensity to naturalize (Yang, 1994). First, as to human capital, as better-educated or more highly skilled persons are more likely to qualify for the type of public sector jobs for which citizenship may be a precondition, they are more likely to capitalize on this citizenship bonus and thus to invest in the naturalization process. The same goes for employment: only those immigrants active on the labour market are likely to expect a return on their investment in the naturalization process, for example in terms of wage increase. The second reason is related to the selectivity of the naturalization process, which may deter immigrants who decide not to bother investing in a procedure that looks very complex and is difficult to understand. Less educated or skilled immigrants may be deterred more easily by the seeming complexity of the naturalization process. Hence, following both arguments, we expect that immigrants with higher levels of human capital and employed immigrants are more likely to acquire the destination country citizenship.

Aside from country of origin and individual characteristics, the legal framework set by the citizenship laws in the countries of origin and destination provides the opportunity structure with regard to access to citizenship. In the literature, most research has gone out to citizenship policy in the origin country, particularly with regard to the possibility of retaining one's previous citizenship when acquiring a new citizenship. Whether citizenship can be retained will depend on the combined outcome of the citizenship legislation in both the countries of origin and destination. In order to avoid conflicting allegiance or loyalties, many countries have a rule that implies the loss of the citizenship of origin upon the voluntary acquisition of another. Some countries also require immigrants to renounce their citizenship of origin, if they do not lose it automatically. In Europe, countries such as Austria, Denmark and Norway have a strict renunciation requirement (Vink and De Groot, 2010). We expect that immigrants who can retain their citizenship of origin are more likely to acquire destination country citizenship. It should be noted, however, that the findings in the literature on this point are rather ambiguous (see Jones-Correa, 2001; Mazzolari, 2009; but compare Yang, 1994; Dronkers and Vink 2012; Logan et al., 2012).

However, whereas most studies have stopped here, logically looking only at origin country citizenship policies in the context of mostly single-destination country studies, citizenship policy in the destination context is crucial, particularly in a European context. Citizenship policies set the conditions under which immigrants can naturalize, for example the required years of residence, the requirement to renounce one's previous citizenship, language and civic integration tests and fees. In Europe, we see large differences in terms of residence requirements, varying from three to twelve or more years (until 1999 even fifteen years in Germany) as well as fees, ranging from no costs whatsoever to nearly two thousand euro in Austria (Goodman, 2010). Eligibility criteria such as residence requirements make the acquisition of citizenship a rather more or less realistic prospect within a foreseeable future. We expect that immigrants are more likely to acquire destination country citizenship in countries with a citizenship law that makes citizenship relatively accessible.

In so far as any comparative research has been done on the effects of destination country policies, in Europe but also in the North American context, these have concluded that indeed ‘policy matters’ (Bloemraad, 2002; Reichel, 2011; Dronkers and Vink, 2012). However, as yet no research has been done on the question to whom citizenship policy matters more. We formulate our three final expectations on this question.

First, while the aggregate effect of varying requirements is that strong differences exist in terms of naturalization rates among the foreign-born population, it is intuitive to assume that the inclusiveness of citizenship policy matters in particular to those immigrants who are strongly motivated to naturalize, primarily those immigrants coming from less developed countries. After all, whereas the first group has a ‘valuable’ citizenship to fall back on and will thus continue to have a viable return option, the latter is likely to see citizenship acquisition as part of a life course project aimed at permanent settlement in a new country. While this need not necessarily rule out the idea of return to the home country, acquiring citizenship of the destination country is likely to be perceived as a key precondition for such return to the origin country, given that citizenship guarantees continuous mobility. Hence, these immigrants will be affected more heavily by policies which make destination country citizenship either not accessible within a reasonable period after arrival in the country due to prohibitive residency requirements or difficult or even impossible to acquire due to prohibitive and discretionary assimilation requirements. Hence, we expect the positive relation between citizenship policies in destination countries and naturalization rates to be stronger among immigrants from less developed countries.

Second, from the perspective that institutional variety is likely to play out especially among immigrants with the strongest motivation to naturalize one may presuppose that differences in destination country citizenship policies are particularly significant for immigrants who reside for a relatively short time in the destination country. After all, in so far as strict legal requirements prohibit or deter immigrants from naturalizing, this effect can be expected to fade out once immigrants reside, for example, more than twenty years in the destination country. We thus expect that the inclusiveness of citizenship policy matters more for immigrants from developing countries with fewer years of residence.

Third, with regard to dual citizenship policies, while the option to retain dual citizenship may be expected in general to affect the decision on whether to naturalize, we assume that the absence of the dual citizenship option in particular affects immigrants from highly developed countries. After all, not only are immigrants from less developed countries in general more motivated to naturalize, thus more willing to accept the potential cost of breaking off the legal link with the country of birth, but those from more developed countries also have more to lose, so to say, in terms of the value of citizenship. We thus expect a stronger positive relation between dual citizenship tolerance and naturalization among immigrants from highly developed countries.

Data and Measurement

For our empirical analysis, we used a pooled version of five waves of data collection of the European Social Survey (ESS). Data collection took place between 2002 and 2010. From the 24 countries covered in the five ESS waves we selected 16 countries based on the following criteria: i) the country sample contains a sufficiently large number of immigrants (N = 80 or more); and ii) the country participated in the ESS in at least three of the five waves. This selection narrows down the number of countries in our ESS-based analysis to the following 16 countries: Austria, Belgium, Germany, Denmark, Finland, Spain, France, The United Kingdom, Greece, Ireland, Luxembourg, Switzerland, Netherlands, Norway, Portugal and Sweden.

Our analysis only focuses on foreign-born or ‘first generation’ immigrants because in this article we aim to theorize and measure the explicit decision to naturalize. As shown elsewhere, the question of acquisition of citizenship by the immediate descendants of immigrants is essentially different (Dronkers and Vink, 2012). In order to exclude as much as possible immigrants who may have acquired destination country citizenship by descent, we only include individuals who themselves, and both of whose parents, were born outside the survey country.

Our aim is to analyse the likelihood of naturalization only for eligible first generation immigrants. Even though in some countries (e.g. Belgium and Ireland) immigrants are eligible to acquire citizenship after three years, in most countries eligibility only comes after five or more years of permanent residence. For this reason we only include immigrants who reside for at least five years in the destination country. Although in some countries after five years one may not yet be eligible for naturalization, we aim to capture these policy differences with our citizenship policy indicator (see below), rather than letting the eligible population be defined by national legislation. Moreover, to exclude cases where immigrants arrive at a young age and acquire destination country citizenship by extension of the act of naturalization of their parents (rather than as an individual decision), we only include individuals who were at least 18 years old on arrival. After applying these selection criteria, our pooled dataset contains 7.489 immigrants from 16 European countries.

In the remainder of this section we describe the operationalization of all the variables and indicate which data sources we use (see Table A1 for details). The dependent variable in our analysis is destination country citizenship. This is a dichotomous variable indicating whether the respondent has citizenship of the country where the survey is carried out. Our data do not provide information about the way in which citizenship was acquired, for example via ordinary naturalization or some form of facilitated naturalization. This means that, particularly in countries with extensive provisions for facilitated access to citizenship for ‘co-ethnics’, such as Germany and Greece, the data in our sample might overestimate the naturalization rate for ‘ordinary’ immigrants (the category we are primarily interested in). While we aim to limit this effect by excluding immigrants of whom at least one parent was born in the host country and by controlling for the language spoken at home (in order to detect cultural affinity), there is no variable in our dataset that allows precise identification of this immigrant category.

Table 1 provides a descriptive analysis of the variation in shares of immigrants with destination country citizenship across the 16 European states in our sample. This general overview shows two important points: first, that citizenship take-up rates among first generation immigrants vary substantially among the countries in our sample, ranging from 14 per cent in Luxembourg to 73 per cent in Sweden; second, that immigrants are on average more likely to have destination country citizenship if they are from less developed countries (54%) than if they are from highly developed countries (41%).

Table 1. Share of first generation immigrants with destination country citizenship
CountryHigh HDILow/Medium HDIAll
% with citizenship % of N % with citizenship % of N % with citizenship N
  1. Averages are weighted with the Design weight (DWEIGHT) of the European Social Survey.

Austria54.41 0.36 56.49 0.64 55.73 375
Belgium28.45 0.54 66.67 0.46 46.19 446
Denmark48.31 0.39 54.23 0.61 51.95 231
Finland38.46 0.16 38.24 0.84 38.27 81
France61.29 0.26 61.86 0.74 61.72 478
Germany30.94 0.20 65.41 0.80 58.5 694
Greece13.79 0.06 31.41 0.94 30.3 462
Ireland29.27 0.50 17.91 0.50 23.65 406
Luxembourg17.01 0.53 10.04 0.47 13.74 553
Netherlands49.25 0.28 81.71 0.72 72.52 473
Norway20.33 0.44 74.19 0.56 50.36 278
Portugal18.18 0.06 44.19 0.94 42.62 183
Spain24.56 0.17 26.3 0.83 25.99 327
Sweden59.37 0.51 87.77 0.49 73.15 674
Switzerland45.33 0.61 39.2 0.39 42.94 1281
United Kingdom52 0.32 80.38 0.68 71.3 547
Total40.78 0.39 53.93 0.61 48.84 7489

With regard to the independent variables, we distinguish between individual level variables, on the one hand, and macro-level variables (country of origin and country of destination), on the other (see Table A2 for descriptive statistics for all the variables included in the analysis). All the individual level variables derive directly from the ESS and their coding is largely self-evident. A few variables require additional explanation. ‘Educational attainment’ was originally measured on the ISCED-97 seven-point scale, but the United Kingdom measure of education forces us to reduce this to four dichotomous variables. We collapse the categories of ‘less than primary’ and ‘primary education’; ‘upper secondary’ and ‘post-secondary non-tertiary’ education; and ‘first’ and ‘second stage of tertiary education’. The fourth dummy is lower secondary education. Years of residence’ is represented by the three dummies indicating the years of residence of the respondent: 5–10 years; 10–20 years; 20 or more years. ‘Minority language’ is a dummy that measures whether the respondent speaks a language at home other than one of the official languages of the country of residence. We also include the continuous variable ‘size of community’ that measures the relative size of an immigrant community in a country of destination, calculated as the fraction of the total number of immigrants that reside in a particular destination country.

As for the macro-level variables, we collect our data using the year 2000 as a reference year. We do so because the average year of arrival in the destination country in our sample is 1989 and the average duration of residence before obtaining citizenship in Europe amounts to 10.5 years (Vink and Prokic-Breuer, 2012). In other words, the year 2000 can be seen as the earliest ‘average’ year in which the immigrants in our sample became eligible to naturalize.

At the origin country level, the variable development’ indicates the level of development of the country of origin. We use data from the Human Development Index (HDI), which is a comparative measure of life expectancy, literacy, education, and standard of living for countries worldwide (United Nations Development Programme, 2000). We recode this index in such a way that a high score means a higher development level. Our analysis of development proceeds in two steps. First, in order to measure whether there is a basic difference in citizenship take-up rates between immigrants from highly-developed countries and those from less developed countries, we classify immigrants in two broad origin groups. We code countries from the HDI top quartile as ‘high’ and all other countries as ‘medium/low’. Second, after establishing that there is a basic difference in naturalization rates among immigrants from these two groups, we proceed with a differentiated analysis of these groups separately. In these analyses we use the raw HDI scores to see whether within these broad groups it still matters whether an immigrant comes from a relatively more or less developed country.

For country of destination, our main citizenship policy indicator ‘MIPEX Access to Nationality’ measures the level of legal openness of the destination countries regarding access to citizenship. The Migrant Integration Policy Index (MIPEX) is a measure of the different policies towards the integration of migrants, where higher scores on a scale from 0 to 100 represent more inclusive migrant integration policies (Niessen et al., 2007). We use an adapted version of the MIPEX subscale on ‘access to nationality’ from the 2007 edition of MIPEX, which only includes those naturalization criteria which are relevant for first generation immigrants. The scores on this subscale are based on the following criteria: eligibility, conditions for acquisition, security of status, and dual nationality (see Tabel A1 for details).1 A second destination country variable, Gross Domestic Product (GDP) per capita, is an indicator of economic wealth.

When analysing the association between citizenship policy in the destination country and naturalization rates, we need to look also at two factors related to country of origin, which may affect the legal opportunity structure: dual citizenship and former colony/territory. Both of these independent variables are derived from a combination of country of origin and destination features. ‘Dual citizenship’ indicates whether an immigrant can retain her or his citizenship of the country of origin when acquiring the destination country citizenship. We code this variable as 0 either if the legislation in the country of origin implies that immigrants who voluntarily acquire another citizenship automatically lose their citizenship of origin or if the legislation in the destination country requires an immigrant to renounce her or his citizenship of origin. If neither of these situations applies we code this variable as 1. Following this coding, descriptive statistics indicate that around 42 per cent of the immigrants in our sample are able to retain their citizenship of origin (see Table A2). While our data do not indicate whether immigrants actually keep their citizenship of origin or not, we expect these rules to influence the motivation to naturalize. Finally, ‘former colony/territory’ is a dummy that indicates whether an individual comes from a country that was either a former colony or a former territory of the country of destination. Including this variable ensures a ‘cleaner’ analysis of the relevance of citizenship policy in the destination country for regular immigrants, as immigrants from former colonies or territories may qualify for facilitated access to citizenship.

We use cross-classified hierarchical linear models to analyse the data. This method takes into account the nested structure of our data and allows us to overcome problems deriving from ecological (Robinson, 1950) and individual fallacies (Snijders and Bosker, 1999). In addition, this method correctly calculates standard errors at the contextual levels by taking into account the fact that individuals are nested within both their countries of destination and origin (cf. Van Tubergen et al., 2004). Given the dichotomous nature of our dependent variable (respondents either do or do not have destination country citizenship) we apply logistic multi-level analysis.

Analysis

Table 2 shows the various cross-classified multi-level models constructed to assess the likelihood of having destination country citizenship for immigrants in the 16 European countries of the sample. Models 1a, 2a and 3a are estimated for the overall sample and include, respectively, individual characteristics only (model 1a), individual plus destination country variables (model 2a), and individual, destination country and origin country features (model 3a). Subsequently, we divide the sample in two and repeat the same analysis for immigrants originating from highly developed countries (models 1b, 2b, 3b) and for those originating from medium or under-developed countries (models 1c, 2c, 3c). The coefficients reported are odds ratios, meaning that they can be interpreted as the percentage change in the likelihood of having destination country citizenship.

Table 2. Logistic cross-classified multilevel analysis of determinants of destination country citizenship (odds ratios)
Model1a1b1c2a2b2c3a3b3c
Country of originAllHigh HDIL/M HDIAllHigh HDIL/M HDIAllHigh HDIL/M HDI
  1. Exponentiated coefficients; t statistics in parentheses.

  2. +< 0.10, *< 0.05

  3. Additional controls for ESS waves (ESS 1–5) and other employment categories (unemployed, not seeking job; disabled; retired; housework; other) included in analysis; coefficients available upon request.

Individual level features
Marital status (ref. never married)
 - Married1.659* (5.26)1.603* (3.01)1.711* (4.22)1.661* (5.27)1.604* (3.02)1.718* (4.25)1.751* (5.77)1.720* (3.40)1.721* (4.26)
 - Separated1.431* (3.18)1.557* (2.56)1.265 (1.51) 1.434* (3.20)1.559* (2.57)1.271 (1.54)1.499* (3.57)1.639* (2.81)1.268 (1.53)
 Female 1.448* (5.63)1.723* (5.35)1.301* (2.94)1.448* (5.63)1.724* (5.35)1.300* (2.93)1.453* (5.66)1.732* (5.38)1.313* (3.04)
 Children (ref: never had children)1.045 (0.58)1.082 (0.73)1.023 (0.21)1.045 (0.58)1.081 (0.72)1.023 (0.20)1.040 (0.52)1.073 (0.65)1.022 (0.20)
Years of residence (ref: >20 years)
 - 6 to 10 years0.128* (−18.18)0.118* (−10.13)0.135* (−14.10)0.129* (−18.16)0.118* (−10.11)0.135* (−14.10)0.131* (−17.96)0.125* (−9.80)0.137* (−14.04)
 - 11–20 years0.401* (−10.40)0.269* (−8.74)0.470* (−6.50)0.401* (−10.39)0.270* (−8.72)0.471* (−6.49)0.404* (−10.30)0.269* (−8.67)0.473* (−6.45)
 Age1.021* (5.30)1.014* (2.23)1.023* (4.42)1.021* (5.30)1.014* (2.23)1.023* (4.42)1.021* (5.42)1.015* (2.39)1.024* (4.50)
Education (ref: Elementary)
 - Lower Secondary1.110 (0.97)1.075 (0.43)1.156 (1.00)1.112 (0.98)1.077 (0.44)1.158 (1.02)1.100 (0.88)1.034 (0.20)1.167 (1.07)
 - Secondary1.242* (2.21)0.992 (−0.05)1.423* (2.70)1.244* (2.22)0.996 (−0.03)1.426* (2.71)1.233* (2.12)0.959 (−0.27)1.441* (2.79)
 - Tertiary0.972 (−0.24)0.728+ (−1.78)1.163 (0.98)0.972 (−0.25)0.729+ (−1.77)1.161 (0.96)0.975 (−0.22)0.730+ (−1.75)1.171 (1.02)
 Unemployed (ref: paid work)0.752* (−2.07)1.034 (0.10)0.706* (−2.22)0.753* (−2.06)1.036 (0.11)0.707* (−2.21)0.737* (−2.20)0.891 (−0.33)0.710* (−2.18)
 Socio-economic index1.009* (4.08)1.006+ (1.70)1.013* (4.21)1.009* (4.06)1.006+ (1.70)1.013* (4.18)1.009* (3.96)1.006 (1.64)1.013* (4.21)
 Size of community1.005 (0.95)0.996 (−0.39)0.998 (−0.20)1.005 (0.88)0.996 (−0.38)0.998 (−0.30)1.007 (1.29)0.999 (−0.09)0.999 (−0.12)
 Minority language at home 0.558* (−5.75)0.461* (−3.45)0.576* (−4.56)0.557* (−5.77)0.461* (−3.45)0.575* (−4.58)0.554* (−5.78)0.445* (−3.51)0.574* (−4.59)
 Dual citizenship1.423* (2.44)1.351 (1.19)1.366 (1.45)1.391* (2.28)1.330 (1.13)1.285 (1.16)1.432* (2.47)1.209 (0.77)1.395 (1.52)
 Former colony or territory1.377* (2.02)2.398+ (1.93)1.588* (2.54)1.388* (2.07)2.370+ (1.91)1.599* (2.57)1.188 (1.07)1.112 (0.25)1.562* (2.45)
 High HDI0.173* (−7.34)  0.173* (−7.33)     
Country of destination features
 MIPEX Access to citizenship   1.024* (2.20)1.011 (0.89)1.035* (2.40)1.022+ (1.96)1.010 (0.81)1.033* (2.23)
 GDP per capita   1.000 (0.75)1.000 (−0.09)1.000 (0.23)1.000 (0.69)1.000 (−0.23)1.000 (0.29)
Country of origin features
 HDI      0.958* (−7.10)0.851* (−3.05)0.986* (−1.99)
Variance components
 Origin1.162 (0.21)1.901 (0.64).966 (0.37)1.163 (0.21)1.90 (0.64)0.765 (.016)1.100 (0.20)0.540 (.25)0.691 (.16)
 Destination0.523 (0.20)0.398 (0.18)0.770 (0.17)0.371 (0.14)0.367 (0.18)0.669 (0.26)0.383 (0.15)0.331 (.016)0.731 (0.28)
 Pseudo R20.2510.234.247.2770.242.308.273.392.298
 N 7456 2885 4571 7456 2885 4571 7389 2818 4571

Origin and years of residence as principal determinants

The first major implication of our analysis is that the level of development of an immigrants's country of origin, as well as the number of years the immigrant resides in the destination country, are the principal determinants of the likelihood of him or her having destination country citizenship. As for development of the origin country, the coefficients estimated for the overall sample show that immigrants coming from the group of highly developed countries are about 83 per cent less likely to acquire destination country citizenship (model 1a). This finding remains unchanged when we control for the features of countries of destination (model 2a). In order to check the robustness of these results and measure more precisely the impact of the level of development, in the next step (model 3a), we control for the variation in level of development between countries of origin. Figure 1 illustrates this analysis and shows the relationship between the level of development of the country of origin and the propensity to naturalize. What we see is that when all of the individual background variables are taken into account, the difference in likelihood of obtaining citizenship is hugely influenced by the level of development of the country of origin. Where the predicted probability of having citizenship of those coming from the least developed countries is about 90 per cent, for those coming from the most highly developed countries it is only 25 per cent.

Figure 1.

probability of having destination country citizenship by level of development of origin country

As for years of residence, our analysis suggests that individuals with less than 10 years of residence in the country of destination are on average almost 90 per cent less likely to have citizenship (model 1a). Such a strong relationship between years of residence and having citizenship indicates that the length of time spent in the country of destination is the most important individual-level determinant, as decisive a factor as the level of development of the country of origin.

As for socio-economic factors, we find that the level of education and socio-economic status are both significantly and positively related to the probability of acquiring citizenship. Immigrants with secondary education are around 25 per cent more likely to naturalize than those with only elementary education. At the same time, higher socio-economic status accounts for a difference of about 0.9 per cent for each additional increase in the one unit of socio-economic status. Given that this variable ranges from 1 to 100, this can be seen as a substantial difference. As far as the labour market status is concerned, we observe only that unemployed immigrants are less likely to have destination country citizenship (model 1a). Finally, we find that immigrants who speak a minority language at home are about 45 per cent less likely to have naturalized. All of these findings are in line with our expectations regarding the factors that determine the propensity to naturalize. In addition, they also remain consistent when the features of countries of origin and destination are taken into account (Models 2a and 3a).

As far as the other demographic features are concerned, we find a positive relation between age and the probability of obtaining citizenship. We further observe that immigrants who are married or have been married are more likely to naturalize (between 65% and 75%, see models 1a, 2a and 3a), while we find no significance for having children. Finally, when controlling for gender, we observe that women are about 45 per cent more likely to acquire destination country citizenship. These findings are all in line with our expectations.

Policy matters, but to whom?

Apart from these more traditional factors known from the naturalization literature, our analysis shows that the legal opportunity structure set by the citizenship laws in the country of destination matters. We show this by introducing the indicator that captures the openness of citizenship policy in the destination countries for first generation immigrants (MIPEX access). We observe that an increase of 1 unit on the MIPEX scale leads to a 2.4 per cent increase in the likelihood of having destination country citizenship (model 2a). On a scale that ranges from 29 to 79 in our sample of countries this can be seen as a significant factor. However – this is the major innovative implication of our analysis – once we look separately at the pool of immigrants for developed countries (2b and 3b) and developing countries (2c and 3c), we observe that legal requirements with regard to access to citizenship only play a significant role for immigrants from under-developed countries. The relationship is even more pronounced than in the analysis of all immigrants together (models 2a and 3a), being 3.5 per cent for each additional increase in the MIPEX index.

This finding is illustrated by Figures 2a and 2b, where we portray these differences against the time dimension, length of residence. The steepness of all three lines indicates the degree to which policy matters for three groups of immigrants: i) those that have resided in the country between six and ten years; ii) those that have resided between 10 and 20 years; and iii) those that have resided more than 20 years. Only in the case of immigrants from under-developed countries do we observe a sharp increase for all three groups in the citizenship take-up rates. For immigrants from highly developed countries the positive relation between citizenship policy and naturalization rates is weaker, as indicated by the steepness of the lines.

Figure 2.

predicted probability of having destination country citizenship by mipex access to nationality by years of residence in destination country

We find no evidence in favour of our expectation that the inclusiveness of citizenship policy matters more for immigrants from developing countries with fewer years of residence, whereas immigrants with more than 20 years of residence are more likely to acquire destination country citizenship. We observe that citizenship policy matters equally for all of these immigrants, regardless of length of stay. What we do observe is that immigrants from under-developed countries naturalize much faster than immigrants from highly developed countries, as indicated by the distance between the lines that represent years of residence of the two immigrant groups.

Regarding dual citizenship policy, we find mixed results that only partially confirm our expectations. The results based on the overall sample (models 1a, 2a, 3a) suggest that being allowed to retain one's citizenship of origin positively encourages citizenship take-up. Immigrants who can retain their citizenship of origin are around 40 per cent more likely to acquire destination country citizenship. These results hold even when controlling for whether immigrants come from former colonies or territories, which increases the propensity to naturalize by around 38 per cent. However, once we separate the immigrants into two groups (models 2b/c and 3b/c), we no longer observe a significant relation. In fact we see that former colonies and territories become more significant, especially in the case of immigrants from highly developed countries (model 2b). Because we still observe a very similar coefficient size for dual citizenship in the separate models (about 40%), we tend to conclude that the relationship is positive, but weak at best.

Combined with our first finding, that the level of development of the country of origin is a strong determinant of citizenship take-up (in our view through the motivation to naturalize), the importance of legal openness indicates that it matters not only where immigrants are from but also where they go.

Two different paths to citizenship

The last major implication of our analysis is that the level of development of the country of origin also conditions the relevance of almost all of the individual-level features for the propensity to naturalize. This finding suggests that there might be two different stories of naturalization. To investigate this point further, we examine whether these differences hold only with regard to the citizenship policies domain, or if they spill over into other domains.

We find that the role of length of residence and socio-economic features too are conditioned on the level of development of the country of origin. Immigrants from less developed countries naturalize much faster than immigrants from highly developed countries, also when all individual features are taken into account together with the legal opportunity structure. Similarly, we find that the previously observed relationship between higher educational attainment and higher propensity to naturalize only holds for immigrants from medium to under-developed countries. There, we observe those immigrants who have secondary education are about 42 per cent more likely to naturalize than those with only primary education (models 2c and 3c).

As far as socio-economic status is concerned, we observe a stronger relationship with citizenship acquisition for immigrants coming from developing countries (1.3% compared to 0.6%). In fact, this relationship is twice as strong as in the case of immigrants from developed countries. Because the range of this indicator spans from 1 to 100, this can be seen as a substantial difference. Being unemployed has a negative relationship with the probability of having citizenship only for immigrants from developing countries.

Finally, we also observe differences when other features are considered. However, these differences are much less pronounced than what we find for the length of residence and socio-economic factors. With respect to language proficiency, we observe that the negative relationship between speaking a minority language at home and the probability of being naturalized is more pronounced for immigrants from highly developed countries (about 54%). In addition, while immigrants from less and more developed countries who are married are equally likely to naturalize, female immigrants from high HDI countries have a higher propensity to naturalize. This might point at the observation that among immigrants from developed countries, citizenship is more likely to abide by traditional Western family standards, where the citizenship status of the women traditionally follows that of the man (Vink and De Groot, 2010).

Conclusion and Discussion

The most important implication of this article is that the level of development of the country of origin is a crucial factor in understanding the relationships between on the one hand, citizenship policies and, on the other, individual-level features and citizenship take-up rates in Europe. To arrive at this conclusion, our analysis first showed that demand for citizenship is influenced primarily by where immigrants are from. The level of human development of countries of origin accounts for the vast difference among immigrants in their propensity to naturalize. Immigrants in Europe coming from medium and under-developed countries are on average much more likely to apply for citizenship than those originating from highly developed countries. These findings are in line with the literature and can be understood in terms of the perceived payoff attached to citizenship. Acquiring destination-country citizenship has a much higher potential pay-off for immigrants originating from low-income countries than for those coming from developed and more prosperous societies. In this context, securing residence status in a country which offers a vast increase in security and life chances, is of crucial importance.

Because large differences exist between immigrants in their motivation to naturalize, the impact of citizenship policies varies for these two groups. In line with this notion, we have shown that the legal framework set by the citizenship laws in the countries of origin and destination accounts for a difference in naturalization rates, yet only for immigrants from less developed countries. In fact, not only are these immigrants twice as likely to naturalize in countries with very open citizenship policies, but they are also the ones particularly affected by these policies.

Second, we have shown that this origin factor is also related to the role of individual characteristics in immigrants’ decisions to naturalize. Our differentiated analyses of citizenship take-up among two immigrant groups, from highly and from medium/under-developed countries, show that different determinants play a role for different groups. Socio-economic features such as human capital and employment status indeed play significant roles in the take-up of citizenship, but only for immigrants from less developed countries. While we can hypothesize about the underlying dynamic, further research would be needed to investigate whether the importance of human capital for this group is because citizenship acquisition has a higher payoff for them or because they are better able to succeed in understanding and managing the naturalization procedure.

As for immigrants coming from highly developed countries, they are not only less likely to naturalize, but whether or not they do so also seems to depend on few factors. If immigrants from highly developed countries naturalize at all, then years of residence play a crucial role in the process. For these immigrants, socio-economic and demographic features only play a marginal difference in their decision to naturalize, compared to the relevance of the time spent in the country of destination.

In other words, we conclude that not only does it matter where an immigrant is from, in terms of the propensity to naturalize, but it also matters significantly where an immigrant goes, in terms of the institutional context of the citizenship policy in the destination country. However, crucially, while institutional diversity clearly affects naturalization rates among immigrants, this relation is conditioned by the level of development of the origin countries of immigrants. Hence, for the question of how much it matters where one goes, it matters significantly where one is from.

Acknowledgements

Research for this article has been co-financed by a grant from the European Fund for the Integration of Third Country Nationals of the European Union, as part of the research project Access to citizenship and its impact on immigrant integration (ACIT), coordinated by the EUDO CITIZENSHIP Observatory. Earlier versions of the article were papers presented at the Annual Conference of the Dutch/Flemish Political Science Associations (Amsterdam, May 2012), the International Conference on European Social Survey (Nicosia, November 2012) and the Maastricht Research Group on Politics and Culture of Europe (Maastricht, December 2012). We are grateful for the constructive feedback from all project participants, conference discussants and the anonymous reviewers.

Note

  1. 1

    While unfortunately there is no citizenship policy indicator available for the reference year of 2000 which covers all 16 countries of this study, recent comparative studies of citizenship policy indices have shown that comparative differences between citizenship policies are relatively stable over time and also relatively robust across different measurements (Koopmans et al, 2012: 1219; cf. Helbling 2013). Own analysis also shows that there is a strong correlation (r = .88) between, for example, the adjusted MIPEX access to nationality indicator for first generation immigrants from 2007 and that of 2010 (N = 27), as well as between MIPEX 2007 and both the ‘Nationality Acquisition’ indicators for first generation immigrants from 2002 (r = .75) and 2008 (r = .85) of the Citizenship Rights dataset (N = 10) from Koopmans et al (2012).

Appendix

Table A1. Operationalization and data sources
VariableOperationalization/Source (Variables are derived directly from the ESS, unless stated otherwise)
Citizenship

Dummy: 1 = Individual has citizenship of country of destination; 0 = Individual does not have citizenship of country of destination.

Source: ESS

FemaleDummy: 1 = Individual is a woman; 0 = Individual is a man
AgeAge of the respondent (in years).
Marital status
 - SingleDummy: 1 = Individual has never been married; 0 = Otherwise.
 - MarriedDummy: 1 = Individual is married; 0 = Otherwise
 - SeparatedDummy: 1 = Individual is separated; 0 = Otherwise.
 Never had childrenDummy: 1 = Individual never had children, 0 = Individual has or has had children
 Years of residence Years of residence in the host country.
 - 6–10Dummy: 1 = Individual has been a resident of the country of destination between 6 and 10 years; 0 = Otherwise.
 - 11–20Dummy: 1 = Individual has been a resident of the country of destination between 11 and 20 years; 0 = Otherwise.
- > 20 yearsDummy: 1 = Individual has been a resident of the country of destination for more than 20 years; 0 = Otherwise.
Education
 - ElementaryDummy: 1 = Individual has finished primary education; 0 = Otherwise.
Corresponds to ISCED category 1 which comprising primary education that begins at ages 5–7 years and lasts about 5 years.
 - Lower secondaryDummy: 1 = Individual has finished lower secondary education; 0 = Otherwise.
This corresponds to ISCED category 2, comprising the first stage of secondary education. The first stage begins at the age of 11 or 12 and lasts about three years.
 - SecondaryDummy: 1 = Individual has finished upper secondary education/ post-secondary non-tertiary education, 0 = Otherwise
This corresponds to ISCED categories 3 and 4. The ISCED category 3 comprising second stages of secondary education, which begins at the age of 14 of 15 and also lasts about three years. ISCED category 4 comprises education that begins at the age of 17 or 18 and leads to an award not equivalent to a first university degree.
 - TertiaryDummy: 1 = Individual has finished tertiary education, 0 = Otherwise
This corresponds to ISCED (International Standard Classification of Education) categories 5 and 6, comprising education which begins at the age of 17 or 18, lasts about three, four or more years, and lead to a university or postgraduate university degree or the equivalent.
 Economic statusRespondent labour market status in the past 7 days.
 - EmployedDummy: 1 = Individual is employed; 0 = Otherwise.
 - Unemployed (job seeking)Dummy: 1 = Individual is unemployed and looking for a job; 0 = Otherwise.
 - Unemployed (not job seeking)Dummy: 1 = Individual is unemployed and not looking for a job; 0 = Otherwise.
 - In educationDummy: 1 = Individual is in full time education; 0 = Otherwise.
 - DisabledDummy: 1 = Individual is disabled; 0 = Otherwise.
 - RetiredDummy: 1 = Individual is retired; 0 = Otherwise.
 - HouseworkDummy: 1 = Individual is engaged in housework; 0 = Otherwise.
 - OtherDummy: 1 = Individual's main activity is other than the ones named above; 0 = Otherwise.
 Socio-economic IndexRespondent's Socio-Economic Status derived from the International Standard Classification of Occupations, ISCO-88.
 Size of communityThe relative size of an immigrant community in a country of destination, calculated as the fraction of the total number of immigrants in a particular destination country.
 Minority languageDummy: 1 = Individual speaks minority language at home; 0 =  Individual speaks the language of the country of destination (native) at home
 MIPEX access to nationalityMigrant Integration Policy Index, adjusted version of indicator ‘access to nationality’, which comprises the following indicators that are relevant specifically for first generation immigrants: 94–96; 99–105; 106–109; 113. Scores range between 0 and 100 (0 =  very restrictive; 100 = very open). Source: Niessen et al (2007) and dataset available on www.mipex.eu, reference year: 2007.
 GDP per capitaGross domestic product of the country of destination per capita in current US dollars. Sources: World Bank national accounts data and OECD National Accounts data, reference year: 2000.
 HDIHuman development index. A composite index measuring average achievement in three basic dimensions of human development—a long and healthy life, knowledge and a decent standard of living. Range: 0 (very low) to 100 (very high). This index is rescaled from its original range (0 to 1). Sources: HDRO calculations based on data from UNDESA (2000), Barro and Lee (2000), UNESCO Institute for Statistics (2000), World Bank (2000a) and IMF (2000), reference year: 2000.
 Dual citizenshipDummy: 1 =  citizenship legislation in both country of origin and country of destination allow retaining citizenship of origin; 0 =  citizenship legislation in either country of origin or country of destination does not allow retaining citizenship of origin. Sources: Ministerie van Justitie (2000), EUDO Citizenship Observatory (www.eudo-citizenship.eu), reference year: 2000.
 Former colony or territory Dummy: 1 = Individual's country of origin was a former colony or territory of country of destination after 1800. 0 = there is no colonial or territorial history between the individual's countries of origin and destination. Source: own coding.
Table A2. Summary statistics
Variable No. observationsMeanStd. Dev.MinMax
  1. Source: European Social Survey, waves 20022010 (pooled version).

Citizenship74890.490.5001
Female74890.530.5001
Age745649.2214.602197
Marital status
 - Married74890.640.4801
 - Separated74890.220.4101
 Has or has had children74890.290.4601
Years of residence
 - 6–10 years74890.250.4301
 - 11–20 years74890.270.4401
 - > 20 years74890.480.3901
Education
 - Elementary74890.190.3201
 - Lower secondary education74890.180.3801
 - Secondary education74890.350.4801
 - Tertiary74890.280.4501
Economic status
 - Unemployed (looking for a job)74890.050.2301
 - Unemployed (not looking for job)74890.020.1401
 - In education74890.020.1301
 - Disabled74890.030.1801
 - Retired74890.180.3801
 - Housework74890.130.3301
 - Other74890.010.1001
 Socio-economic index748940.4217.3616100
 Size of community74899.3810.890.0749.82
 Minority language at home 74890.360.4801
 MIPEX Access to citizenship748954.2714.092979
 GDP per capita748928654.568839.151207043660
 HDI742071.6414.6522.491.3
 Dual citizenship74890.280.4501
 Former colony or territory74890.110.3101
ESS Round
 - ESS Round274890.210.4101
 - ESS Round374890.160.3701
 - ESS Round474890.200.4001
 - ESS Round574890.230.4201

Ancillary