Migrant poverty and social capital: The impact of intra- and interethnic contacts

Accepted Manuscript Title: Migrant Poverty and Social Capital: The Impact of Intra- and Interethnic Contacts Author: Boris Heizmann Petra B¨ohnke PII: DOI: Reference:

S0276-5624(16)30127-5 http://dx.doi.org/doi:10.1016/j.rssm.2016.08.006 RSSM 309

To appear in:

Research in Social Stratification and Mobility

Received date: Revised date: Accepted date:

27-11-2015 27-7-2016 7-8-2016

Please cite this article as: Heizmann, Boris., & B¨ohnke, Petra., Migrant Poverty and Social Capital: The Impact of Intra- and Interethnic Contacts.Research in Social Stratification and Mobility http://dx.doi.org/10.1016/j.rssm.2016.08.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

 

Migrant Poverty and Social Capital: The Impact of Intra- and Interethnic Contacts Boris Heizmann* and Petra Böhnke**  * GESIS ‐ Leibniz Institute for the Social Sciences  ** Universität Hamburg    Corresponding author contact data:    Dr. Boris Heizmann  Data Archive for the Social Sciences (DAS)  GESIS ‐ Leibniz Institute for the Social Sciences      Unter Sachsenhausen 6‐8  D‐50667 Cologne  Germany    [email protected]

Second author contact data:

Prof. Dr. Petra Böhnke Universität Hamburg  Welckerstraße 8  D‐20354 Hamburg  

[email protected]

  A previous version of this paper was presented at the XXXIII. Sunbelt Social Networks Conference 2013 of the  International Network for Social Network Analysis (INSNA) in Hamburg, Germany. We would like to thank the  participants of this session for valuable comments and suggestions.   

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Highlights:    

We analyze how various forms of migrant social capital influence the prevalence of poverty. We distinguish between contacts to natives and to immigrants. Having contacts with both groups is most beneficial to be protected from poverty. The disadvantage associated with not having any native contacts depends on language proficiency.

Previous research on immigrant economic incorporation has predominantly focused on dimensions of labor market access, while income poverty and its determinants have not yet received as much attention. The present study sets out to address this gap, and it has a particular focus on the relative utility of intra- and interethnic contacts. Applying social capital considerations, we investigate to what extent German first generation immigrants’ relationships in terms of the ethnic composition of their friendships and family size influence their likelihood of income poverty, net of various other factors. We furthermore ask whether the returns on interethnic contacts are dependent on immigrants’ host country language proficiency, a pivotal type of cultural competence. Using the German Socio-Economic Panel Study, we find that both types of social relationships help to reduce poverty, which diverges from previous findings for labor market outcomes. Moreover, the utility of interethnic relationships varies according to language proficiency. These results illustrate the complex interrelations between cultural, social and economic integration, and they help to advance our understanding about the potential benefits of intra- and interethnic social capital by showing that both are useful in averting immigrant income poverty.

Introduction Due to long-standing immigration inflows in many countries, issues of immigrant incorporation have become a main topic on the political agenda and in social science research. A central aspect of these debates refers to the economic incorporation of immigrants, usually with a focus on immigrant-native differences in the labor market. In this context, research has focused on outcomes like labor market participation, self-employment, occupational prestige or earnings (Chiswick, 1978; Constant & Massey, 2003; Gorodzeisky & Semyonov, 2014; Heizmann, Busch-Heizmann, & Holst, 2015; Kalter, Granato, & Kristen, 2010; Kanas, Chiswick, van der Lippe, & van Tubergen, 2012; Kogan, 2006; Kogan, Kalter, Liebau, & Cohen, 2011; Lancee, 2010; Sanders & Nee, 1987; Van Tubergen, Maas, & Flap, 2004). Although migrants are one of the societal groups most likely to be affected by poverty in many countries, income poverty as a central socio-economic status category and dimension of social inequality has not yet received the same level of attention (Blume, Gustafsson, Pedersen, & Verner, 2007; Galloway & Aaberge, 2005a; Lewin, Stier, & Caspi-Dror, 2006; Picot, Hou, & Coulombe, 2008). In Germany, 12.3 percent of the native population lived in 2  

 

poverty in 2012 while the corresponding figure for the first generation immigrants is 26.9 percent, and almost a third (32.8) of the non-national immigrants are poor (Federal Statistical Office, 2013). Even those second generation immigrants with a university entrance diploma are twice as likely to be poor as their native peers (Beauftragte der Bundesregierung für Migration, 2014). These differences point to persistent disadvantages for immigrants because poverty affects many dimensions of the well-being of all household members living under such circumstances. Immigrant poverty is therefore a particularly salient issue from a sociopolitical point of view, and it is distinct from individual labor market participation as such. Therefore we need to know more about the accompanying mechanisms that can account for its persistence and consequences. One particular factor influencing the economic incorporation of immigrants is that of social capital, understood in this specific context as the extent to which immigrants interact with host society members and with members of their own ethnic groupi. Conceptually, these two forms of contacts refer to bonding and bridging social capital (Putnam, 2000). Whereas in social capital research bonding and bridging sometimes refer to tie strength, in the context of immigrant incorporation research, this distinction runs along ethnic group boundaries, referring to intra- and interethnic contacts rather than tie strengths. Consequently, these two types of social capital are considered primary explaining factors for immigrants’ economic incorporation (Behtoui & Neergaard, 2010; Brandt, 2006; Esser, 2009; Kanas, et al., 2012; Lancee, 2012; Lancee & Hartung, 2012). Although at times criticized as a narrow and simplistic classification of social capital (Geys & Murdoch, 2008; Patulny & Svendsen, 2007), important aspects of social connectedness can be specified with this distinction, particularly if questions of economic, social and political integration of immigrants are regarded: Bridging social capital depicts the idea that two networks of – in our case – different ethnic backgrounds are bridged by individuals (Burt, 2001), providing access to a wider variety of helpful resources and information. Both types of social capital thus refer to 3  

 

central aspects of the social integration of immigrants, and their relationship to poverty risks is the primary focus and contribution of the present paper. As we will elaborate below, the issue of immigrant embeddedness into these two contexts is controversial, and recent research comparing both types of contacts emphasizes the benefits of bridging social capital for individual labor market access (De Vroome & Van Tubergen, 2010; Esser, 2009; Kalter & Kogan, 2014; Kanas, et al., 2012; Lancee, 2010, 2012; Lancee & Hartung, 2012). However, it is not clear whether this applies to poverty as well. To overcome poverty, different support structures can be utilized which are based on resources other than mere job-related information, i.e. financial, material or emotional support which may not be as relevant for labor market integration as such. Furthermore, as noted above, poverty as a household concept has a much broader character than individual labor market access as such because it indicates a lack of material means that affects whole households in many ways. Moreover, as the observation of “working poor” exemplifies, labor market integration does not guarantee immunity against poverty. Our contribution thus seeks to assess the relative importance of contacts with majority members and contacts with members of ones’ own ethnic group for household income poverty. Finally, we will investigate whether this impact is dependent on another central aspect of immigrant incorporation. Scholars of social capital and immigrant incorporation alike have provided theoretical arguments that as individuals accumulate host country competences such as language proficiency, the advantages they gain from their contacts may change as well (de Souza Briggs, 2005; Esser, 2001; Woolcock & Narayan, 2000). Thus, we also attempt to elucidate whether the relationship between these two forms of social capital and income poverty depends on immigrants’ host country language proficiency. First, we will briefly outline the concept of social capital and its significance for poverty. This is followed by the application of social capital theory to the field of immigrant incorporation. The pivotal distinction of different types of social capital – intra- versus 4  

 

interethnic contacts – will also be outlined in greater detail here. Then we will present our data and analytical approach to these questions, after which we commence our analyses. In a first step, we will document the incidence of immigrant poverty as well as poverty differences between the immigrant and the native population in Germany. In the multivariate analyses for first-Generation immigrants our focus will be on the relative importance of the two different types of social capital and its dependency on language proficiency. We will conclude the article by highlighting the substantial implications of our results for the scholarly discussion about the benefits of social capital as well as for the conceptualization of immigrants’ integration.

Poverty and the Role of Social Capital   Income poverty is a central dimension of social inequality and can be understood as an outcome of several underlying disadvantages. Since income poverty is usually defined as a household-level concept, the primary factors influencing poverty refer to the various income resources generated within that household and to the distribution of those resources amongst household members. Thus both employment as well as the household structure influence an individuals’ likelihood of being poor. Education and further migration-related factors including age at arrival, the years spent in the host country, and the origin group were also identified as important factors (Blume, et al., 2007; Galloway & Aaberge, 2005b; Lewin, et al., 2006; Tucci & Wagner, 2005; Verwiebe, 2010). We will incorporate these aspects into our analyses, but we focus on the role of social networks in explaining poverty risks. The notion of the usefulness of social relations and the related embedded resources in the form of social capital has opened up a broad field of inquiry in which numerous benefits of such relations are identified (Portes, 1998). At the same time, the concept has remained elusive, which can be explained by its application to different analytical levels and different 5  

 

foci, ranging from a resource-focused perspective (Bourdieu, 1986; Coleman, 1988) over approaches dealing with network characteristics (Burt, 2001; Lin, 2001) to the view of social capital as an aggregate community asset (Putnam, 2000). An influential definition of social capital was provided by Bourdieu (1986, p. 248f.): “Social capital is the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition(…)”. As Bourdieu furthermore suggests, the value of these resources is determined by the size of the network as well as the resources located within that network, although the latter aspect is notoriously difficult to measure (Lin, 1999a). Similarly, Lin defines social capital as resources that are accessed through networks, with broad implications for economic, social and psychological well-being (Lin, 2001). Both authors argue that the quality and the quantity of a network and its resources are important for a better understanding of social inequality and mobility patterns. The key to understanding the effect of social capital on poverty lies in the idea that the different forms of capital can be transformed from one to another (Bourdieu (1986). For instance, cultural capital in the form of education and social capital such as information provided by friends can help to find a job and therefore be transformed into economic capital (Brandt, 2006; De Graaf & Flap, 1988; Drever & Spieß, 2006; Freitag, 2000; Granovetter, 1973; Lin, 1999b). Granovetter (1973) suggests that the mechanism behind this is based on useful “weak ties”, relationships defined by lower emotional closeness and contact frequencies, but better access to non-redundant information; however, the empirical evidence for this mechanism is ambiguous (Mouw, 2003; Wegener, 1991). Poverty can also be overcome by other resources accessed through social relationships, i.e. direct financial, material and psychological assistance which may either increase income directly or provide psychologically relevant emotional support. Such forms of non-monetary support may help to increase labor market productivity and thus income in 6  

 

ways that are difficult to measure directly. Moreover, the amount of help available with regard to navigating income-relevant host country institutions, e.g. gaining access to government transfers, may depend on the existence of social relationships. In this sense, especially family ties are an important source of solidarity, financial and material transfers, and mental well-being. Consequently, social relationships can have an impact that goes far beyond employment, since the resources provided may not merely help to “buy” a job, but they may help to lift households above the poverty threshold through various other forms of assistance. For immigrants the respective ethnic community may also provide such support, but the question whether co-ethnic networks reduce or actually reproduce poverty refers to a central debate in scholarship on immigrant incorporation. We now discuss the possible implications of different types of social capital for the immigrant incorporation process.

The Social Capital of Immigrants: Intra- versus Interethnic Social Capital

The impact of immigrants’ social capital on poverty can be further elaborated by turning to social capital concepts that focus on network structures (Burt, 2001; Lin, 2001). Burt’s (2001) concept of a bridge between two networks emphasizes that different social groups can be connected by individual ties across network boundaries, an idea that provides the tools necessary to broach the distinct situation in which immigrants find themselves as potential members of two different societies. In contrast to classic assimilation theory (Gordon, 1964), newer conceptualizations of immigrant incorporation do not view the context of origin and the receiving context in the host society as mutually exclusive options (Alba & Nee, 2003; Berry, 1997; Esser, 2001; Portes & Rumbaut, 2001). The usefulness of social ties is thus not necessarily restricted to host country contacts: Immigrants can have access to the network of their own context of 7  

 

origin, but also to the network of the non-migrant population. Accordingly, the social capital of immigrants can typically be divided into intra- and interethnic, or bonding and bridging social capital (Putnam, 2000). The former refers to contacts within one’s own ethnic group, and the latter refers to contacts to members of the native populationii. These two forms of contacts – intra- and interethnic – can be combined into four idealtypical modes of immigrant social integration (Berry, 1997; Esser, 2001). Table 1 shows the different configurations. Table 1 about here Defined in this way, Assimilation denotes the case in which only contacts with host country members exist and no interaction between immigrants and members of their own respective ethnic group occurs. Separation labels the idea that there are only contacts within the own ethnic group, while contacts to host country members remain scarce at the most. Multiple Integration is characterized by a state in which contacts to both groups are sustained. Marginality refers to a form of isolation in which neither form of contact exists, with the immigrant being a “Marginal Man” (Park, 1928) who lives in between both societies. This is arguably the least probable of the four types of social integration displayed in Table 1. There is an enduring debate about the utility of intra- versus interethnic contacts. Several authors emphasize the idea that ethnic social capital has a variety of beneficial effects that help individual immigrants to advance economically (Berry, 1997; Berry, Phinney, Sam, & Vedder, 2006; Elwert, 1982; Levanon, 2014; Portes & Rumbaut, 2001; Portes & Sensenbrenner, 1993). Ethnic communities or economic enclaves, for example, are supposed to provide better economic opportunities (Waldinger, 1993; Wilson & Portes, 1980; Zhou, 2004) than secondary or low-wage labor markets dominated by host country employers. Intraethnic economic solidarities thus can help to overcome the disadvantages that supposedly originate from a segmented labor market which is dominated by discriminatory employers belonging to the ethnic majority (Bonacich, 1972). Especially with regard to poverty, 8  

 

important forms of financial and emotional support may require a certain level of interpersonal solidarity that does not easily cross ethnic boundaries (McPherson, SmithLovin, & Cook, 2001). Such support is then more likely to be mobilized through kinship or ethnic solidarity than through interethnic contacts. In this sense, immigrant family ties constitute a distinct form of ethnic social capital (Coleman, 1988; Nauck & Kohlmann, 1998; Nee & Sanders, 2001). The opposite point of view dismisses this notion and stresses the necessity of acquiring bridging social capital. Sometimes the futility or hazards of focusing on bonding or ethnic capital are emphasized (Borjas, 1992; Esser, 2001; Haug, 2007; Sanders & Nee, 1987). Bridging social capital – contacts with host country members – is supposed to be more beneficial since it more often entails valuable resources and information relevant for labor market progress (Behtoui & Neergaard, 2010; de Souza Briggs, 2005; Putnam, 2000; Woolcock & Narayan, 2000), but also for education and social mobility (Klein, 2010). Although ethnic communities are seen as opportunity structures for immigrants, they are often viewed as inhibiting overall economic advancement, exemplified by the notion of an “ethnic mobility trap” (Wiley, 1967). The more moderate position argues that immigrant communities are of less importance for the success of overall incorporation than contacts to the host society, and that assimilation still is a major trajectory for immigrants – at least in the long run (Alba, 2008; Alba & Nee, 2003; Esser, 2009). Taking a middle ground, a network-oriented perspective would suggest that Multiple Integration as a form of embeddedness into two different networks would be the most advantageous condition since it allows access to more diverse resources. More precisely, individuals who effectively establish contacts with both networks are able to bridge different social contexts, which has been hypothesized to be a favorable position with regard to access to resources and information from both networks (Burt, 2001; Lin, 1999a).

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Recent empirical evidence on the relative economic usefulness of these different types of immigrant social capital is available only for labor market issues. It appears that contacts with the native population indeed are helpful for a variety of labor market indicators such as earnings and employment probabilities, while the evidence for intra-ethnic contacts suggests that they are less important or provide no benefits at all (De Vroome & Van Tubergen, 2010; Esser, 2009; Kalter & Kogan, 2014; Kanas, et al., 2012; Lancee, 2010, 2012; Lancee & Hartung, 2012). This literature thus suggests that interethnic contacts are more important than intra-ethnic contacts. Whether this also holds true for income poverty will be established below. In summary, there are contradictory positions with regard to the relative risks and opportunities associated with different types of social capital. On the one hand, emphasis is placed on the ethnic community as an additional and important source of support; on the other hand, ethnic social capital is seen as a trap that should be avoided in favor of an orientation towards the host society. For bridging social capital (i.e. Assimilation, but also Multiple Integration) we thus hypothesize a poverty-abating influence. For bonding social capital the debate outlined above provides contradictory hypotheses concerning the effect on poverty, either pointing to benefical, poverty-reducing effects of intra-ethnic contacts, or suggesting that such contacts lead to a higher risk of poverty. The network-based view would suggest the hypothesis that Multiple Integration is the most advantageous mode of social integration. These two views on the utility or disutility of intra-ethnic social capital, however, can be reconciled by acknowledging the often neglected interrelations between different aspects of immigrant integration: The usefulness of social relations may depend on other acquired host-country-related competences, especially language proficiency.

Immigrant Social Capital and the Importance of Language Proficiency 10  

 

The utility of the different forms of social capital may change as an individual progresses through his or her incorporation trajectory (Woolcock & Narayan, 2000). On the one hand, bonding social capital within one’s own ethnic group provides solidarity, information and also material support which is useful in order to “get by” (de Souza Briggs, 2005) in the beginning, and may have prompted the migration decision in the first place (Massey, et al., 1998). On the other hand, bridging social capital provides further valuable information that facilitates “getting ahead” (de Souza Briggs, 2005). One of the most fundamental immigrant skills pertaining to interethnic social capital is host country language proficiency, and this dimension was repeatedly stressed as the key to immigrant economic advancement (Chiswick, 1978; Chiswick & Miller, 1995; Esser, 2001) and acquisition of bridging social capital (Martinovic, van Tubergen, & Maas, 2014; Ryan, 2011; Ryan, Sales, Tilki, & Siara, 2008). On the one hand, under conditions of low language proficiency, immigrants’ host country contacts may be restricted to interactions with individuals that cannot provide helpful resources. On the other hand, without a certain level of host country language proficiency, even well-off host country contacts may refrain from sharing information or resources that could make a difference with regard to poverty. Since language has long been a central marker of cultural difference (Brubaker, 2013), a low language proficiency signals a lack of acculturation, resulting in social distance and foreignness (Alba & Nee, 2003, p. 42). This implies that mutual trust and expectations of reciprocity are less developed under such circumstances (McPherson, et al., 2001). As a consequence, relevant information or resources may not be made accessible to the immigrant when interactions are characterized by this aspect of cultural distance. In Bourdieusian (1986) terminology, a lack of cultural capital may hamper the development and use of social capital even when interethnic relationships do exist. This supposed relationship between language proficiency and the acquisition,

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maintenance and use of social capital thus refers to the interrelations between different forms of immigrant incorporation (Esser, 2001). We consequently suspect that language proficiency has an influence on the consequences that the different modes of social integration outlined above have for poverty. This will be tested via interactions in the models below. We hypothesize a poverty-reducing impact of interethnic contacts (i.e. Assimilation or Multiple Integration) as compared to having only intra-ethnic contacts, but this should only be observable for individuals who have sufficient language proficiency to actually gain resources from these contacts. In contrast, for individuals with lower language proficiency, we hypothesize that a focus on their own network in the form of Separation is more useful or at least not detrimental with regard to their risk of income poverty.

Data, Operationalization and Methods

In order to approach these issues empirically, we will use data from the German Socio-Economic Panel Study, or SOEP (Wagner, Frick, & Schupp, 2007). This is a largescale annual household panel survey conducted in Germany since 1984. Due to an oversampling of immigrants, detailed income data as well as several measures of immigrant social capital, this survey provides a suitable basis for our research questions. Our target sample for the multivariate analyses comprises adult foreign-born respondents (i.e. first generation immigrants). Second generation immigrants have not been taken into consideration because several of the migration-related factors we employ are either not available or do not vary enough for second-generation immigrants, most importantly language proficiency. Our results thus only apply to immigrants who have come to Germany from another country.

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The dependent variable of income poverty is defined using the modified OECD equivalence scale: persons whose self-reported household equivalent income after public and private transfers in the current month is below 60% of the median monthly household income in the respective year are coded as poor (1), and those who are above this threshold are coded as not poor (0). We employ several social capital measures. Starting in 1991, questions concerning three close acquaintances were collected every five years by the SOEP. For each of these three acquaintances, respondents were asked whether that person is of German origin or nationality, of the same origin or nationality as the respondent, or another origin or nationality. Since these variables do not measure resources and are focused on close ties that may include relativesiii, they certainly provide a less than perfect assessment of immigrant bonding and bridging social capital. Nonetheless, these and similar measures have nevertheless successfully been employed in several previous studies (e.g. Esser, 2009; Kanas, et al., 2012; Lancee, 2012; Lancee & Hartung, 2012; Nannestad, Lind Haase Svendsen, & Tinggaard Svendsen, 2008), and they constitute the best available approximation of the concepts outlined above. We use annual data from 1996 onwards, because several important variables were not available in 1991. Since social capital items are only measured every five years, we impute data from the most recent preceding survey, i.e. for 2002 we use the 2001 value. We thus use all waves from 1996 to 2011, modeling the mean effect of networks across the five-year spans between the measurements. This means that for the majority of the data, the measurement of bonding and bridging social capital precedes the measurement of poverty, which ameliorates the causality and selectivity problem inherent in using social networks as a predictor of social inequality (DiMaggio & Garip, 2012). To be sure, we do not assume that networks remain constant, but we do consider the measure of e.g. 1996 as the best available proxy for the following years. As an additional check with regard to this causality issue, we will compute 13  

 

the final model and figure omitting the waves in which networks and poverty were measured simultaneously. The sample size comprises 3,342 persons and 20,246 person-years after listwise deletion of cases with missing values. As Burt (2000) suggests, network processes often involve a diffusion of information and resources over time, rather than having an immediate effect. Unfortunately, the timespan between the measurements in the database used does not permit us to determine the exact time of when network composition changes actually occurred: for example, the network composition measured in 2001 may reflect the situation in 1998 for some of the respondents. For this reason, the data and analyses are necessarily agnostic as to when exactly networks influence poverty risks after these contacts have been established. This prevents the possibility to further investigate to what extent there may be a time lag concerning network effects on poverty. Using these variables we compute our measure of social integration which is similar to the categorization employed by Esser (2009) and displayed in Table 1. Calculating raw shares of both types of contacts would create two strongly negatively correlated variables, since for instance reporting 100% co-ethnic contacts directly implies 0% German contacts and vice versa. Moreover, we would then not be able to depict the consequences of having a mixture of both types of contacts in the form of a Multiple Integration. Following Table 1, the mode of social integration of immigrants that report only German contacts in the survey instrument concerning three acquaintances are categorized as “Assimilation”. Those respondents who report only co-ethnic contacts are coded as “Separation”. Having a mixture of contacts with German on the one hand and or co-ethnic or other nationalities on the other is classified as “Multiple Integration”. Persons being multiply integrated into their own as well as another immigrant group that do not also report German contacts – a case not covered by the binary opposition of immigrants and natives underlying the categories in Table 1 – are labelled as “Multiple Integration (No Germans)”. Having only 14  

 

contacts with persons of origins or nationalities other than their own and the German one is coded as “Marginality”. We will furthermore include the number of entries in this social integration instrument as a predictor, since reporting less than three acquaintances can be seen as an additional indicator of a lack of social capital. The family size outside of one’s own household is used as another indicator of bonding social capital. Our controls include gender, age and education. The age of the respondents is split into age at migration and years since immigration which allows us to use years since immigration as a measure of the time spent in Germany. For education we use the CASMIN classification which is divided into three categories. CASMIN originally consists of 4 numerical categories with several alphabetical subcategories denoted a, b, c, totaling nine categories plus one for persons still in school in the SOEP. We categorize using the numerical values 1 through 3, and persons still in school are assigned the lowest category. The measure of labor market position is based on a detailed variable provided by the SOEP which is recoded into nine categories that cover a wide variety of labor market participation forms, i.e. including such categories as having a blue-collar job, being in education or being retired. Following the literature outlined above, this variable can be a mediating factor between networks and poverty, so that we enter it in the final model. A variable depicting the household type distinguishes between singles and couples with and between households with and without children. It also includes a category for multigenerational households and other arrangements. Several further migration-related dimensions will be accounted for. Language proficiency is measured as a five-level variable, with self-reported ratings in ascending order: “not at all”, “poorly”, “fairly”, “good”, and “very well”. We pool the two groups with the lowest levels of language competence because of the low number of respondents with very low language proficiency. This results in a four-level variable. In addition, an immigrants’ migration intention may be a relevant dimension for poverty: a willingness to leave Germany 15  

 

again may lower the aspirations and outcomes of those immigrants (Bonacich, 1972; Dustmann, 2005). Poverty levels vary between different immigrant groups of origin in Germany (Tucci & Wagner, 2005). We thus will include a categorized country of origin variable in the models. Such a variable also provides at least a proxy measure of the respective ethnic community. Since our focus is not on origin groups as such, we do not intend to explain these differences, but we do acknowledge that they must be accounted for. Table 2 about here Table 2 presents the sample descriptivesiv. Because we are dealing with panel data we perform random effects logistic regression analysesv. We build our models in a block-wise fashion, starting with standard controls and testing the impact of the different forms of social capital. Next we add further relevant dimensions in order to see whether the results first obtained are stable. We then proceed to test whether there is an interaction with language proficiency. In a final step, we introduce current labor market position as a primary mechanism through which social capital influences poverty in order to see to what extent our findings are mediated by this primary determinant of poverty. Since conventional logistic regression coefficients alone are not suitable for such comparisons (Mood, 2010), we will additionally display average marginal effects in the model table, and interactions will be visualized by marginal effects plots.

Descriptive Analyses

Our descriptive analyses begin with a comparison of poverty rates for different types of immigrants as well as the native population in Figure 1. Here we distinguish between natives, second generation immigrants born in Germany or having immigrated until age 6, and first generation immigrants who are divided according to their years since immigration. The 16  

 

median length of stay in Germany in our data set is 18 years, we therefore split the group of first generation immigrants using 18 years as the cutoff value. This results in two groups of first generation immigrants that are of similar size. One can clearly see that all immigrant groups are worse off in terms of poverty rates than those that are native born in the respective year. Second generation immigrants exhibit the second lowest prevalence of poverty. For first generation immigrants, the graph suggests that the length of stay in Germany is of great importance for poverty. The top line refers to immigrants with a duration of stay of less than 18 years which implies the highest levels of poverty for this group. Figure 1 about here Figure 2 presents descriptive evidence for our focal explanatory variable of social integration and our target population of first generation immigrants. Here, we see the estimated levels of poverty for each mode of social integration for those years in which social integration was measured. The categories of Marginality and Multiple Integration (No Germans) are not presented here due to the low number of cases, suggesting that these two types of social integration are indeed rather uncommon phenomena. Figure 2 about here As we can see clearly, Separation is associated with higher levels of poverty, and the difference to the two other groups is significant in the periods before and after 2001. Multiple Integration occupies a middle ground while Assimilation shows the lowest level of poverty, although the difference between both is rather small and not significant. Immigrants without German contacts thus appear to be a group with a particularly high poverty risk, reaching a level of 37 percent in 2011. In summary, the descriptive evidence suggests a considerable variation of poverty prevalence within the group of first generation immigrants, both with regard to the length of stay, but also with regard to the modes of social integration. In order to see whether these 17  

 

differences are indeed due to social integration rather than to other background variables like language proficiencyvi, we now perform multivariate analyses. Multivariate Analyses

We commence our multivariate analysis by running random effects logistic models that explain the occurrence of income poverty (Table 3). Here we also omit the category Marginality due to the low number of cases. The first model includes only gender, age at immigration, education and our social capital variables. For social integration there is no difference between Assimilation and Multiple Integration, yet for Separation we see a highly significant positive effect: immigrants that report only close acquaintances of the same origin or nationality have a higher probability of being poor. This first set of results already shows that having contacts to Germans indeed is associated with a lower probability of poverty, but the mode of social integration does not need to take the form of Assimilation. At the same time, the number of acquaintances reported in the social integration construct is also significantly related to poverty. The higher the number of persons reported, the lower the probability of being poor. Family size as an indicator of bonding social capital significantly influences poverty: having only a very small family network is associated with a higher poverty risk. Together with the results for the modes of social integration, these findings suggest that both bonding and bridging social capital play an important role for the development of poverty. Gender is insignificant, whereas age at migration is a predictor of poverty: the older the immigrant was at the time of entry into Germany, the more likely is he or she to be poor. For education we see the expected results, namely that the higher the education level, the smaller the likelihood of becoming poor. Table 3 about here 18  

 

The next step in our modeling introduces further migration-related covariates as well as household type. After adding these indicators the results for social integration again show that Separation is associated with a higher poverty risk than Multiple Integration. For German language proficiency we find that, as expected, the better the language skills, the lower the risk of poverty. Years since immigration is associated with lower poverty risks. Migration intentions are not significantly related to poverty risks which is probably due to the fact that we have already controlled for several other incorporation dimensions in Model 2. Immigrants with German citizenship have a lower risk of being poor. For the immigrant group variable, we chose the group of Turkish origin as the reference category, since this is one of the largest immigrant groups in Germany. This group also is among those that have the highest poverty risk of all the groups investigated here. At the same time, immigrants from Southern European countries other than Spain, Italy or Greece also have high levels of poverty since they do not differ significantly from the Turkish group, as does the category “other”. The lowest poverty probabilities are found for Spanish, Northern and Western European immigrants as well as those from the U.S., United Kingdom, Australia and Canada. As we can see, these differences are considerable, but the findings we obtained for our social capital indicators are robust against the inclusion of this variable. For household type there are also significant differences. Respondents living in a single person household and single parents are the groups most likely to be poor when compared to households with couples and several generations. In summary, the findings in Model 2 show that several dimensions of immigrant incorporation influence poverty, but the findings for the different forms of social capital remain stable. Model 3 introduces the interaction between social integration and language proficiency. Before we interpret the interaction findings, we will add occupational position in Model 4 in order to account for all variables motivated above. Since average marginal effects 19  

 

cannot be computed for interaction terms, we also display beta coefficients for these models. Persons with low language proficiency are sufficiently present in all types of social integration. After adding labor market position women appear to have have a marginally significantly lower poverty risk. This coefficient change is likely due to the worse labor market position of immigrant women when compared to immigrant men in Germany (e.g. Heizmann, et al., 2015). We still observe a negative impact of years since immigration in the final model. Yet looking at the direct effects and interaction terms of the interaction between social integration modes and language proficiency in Models 3 and 4, these relationships do not change much, which shows that the observed effects are not due to differences in occupational status. Due to the presence of interaction terms, the direct effects for the social integration modes refer to persons with high German language skills. For these respondents, both Assimilation and Separation differ from Multiple Integration with regard to poverty, showing that Multiple Integration yields the lowest level of poverty under conditions of very good language proficiency. As stated above, Model 5 omits the four waves in which networks and poverty were measured simultaneously as a robustness check, resulting in a somewhat stricter test of temporal causality. The results are quite similar to those presented for Model 4, so that we can now turn to the substantive interpretation of these results. While several of the nine interaction terms are significant, the interpretation of these categorical by categorical interaction findings is quite cumbersome. We thus proceed with a graphical display of these findings in order to get a clearer picture of these relationships. In Figure 3 we examine the overall poverty levels for the different combinations of language proficiency and social integration. There we present evidence in the form of predicted poverty rates and the corresponding 95% confidence intervals as average predictive margins for the

20  

 

different combinations of language proficiency and modes of social integration based on Models 4 and 5. The individual random component is held at its mean for this graph. Figure 3 about here As can be seen, for all but the lowest level of language proficiency, persons in the Multiple Integration category have a lower level of poverty than those in the Assimilation and Separation category. We thus add horizontal lines to the subgraphs in order to indicate the upper 95% confidence interval for Multiple Integration in order to facilitate a judgment of whether the advantages of Multiple Integration vis-à-vis Assimilation and Separation are also statistically significant. For respondents with very good language proficiency, the difference between Multiple Integration and Assimilation is significant at the 5% level. For respondents with good language skills the difference between the two is not significant, while for those with fair language skills, it is again significant at the 5% level. We furthermore observe that Multiple Integration differs significantly from Separation for all but the lowest level of language proficiency, which for Assimilation is only the case for those with fair language competence. For respondents with a very good language proficiency, Separation goes along with poverty rates that are about twice as high as those predicted for Multiple Integration, and this ratio gets progressively smaller as language proficiency decreases. Finally, Multiple Integration (No Germans) appears to be a relatively heterogeneous category, sometimes yielding the lowest prediction of poverty, although the relatively large confidence intervals already show that this is a relatively small group. Taken together, the evidence presented in Figure 4 suggests that having a mix of bonding and bridging social capital overall is associated with lower risks of poverty than the other two primary modes of social integration, and that German language proficiency indeed influences these differences. The figure based on Model 5 omitting the waves with

21  

 

contemporaneous measures of poverty and networks yields a relatively similar picture, although the differences for those with a “Good” language proficiency are less pronouncedvii. From the positioning of the five lines we also see the poverty-abating impact of language proficiency. On a final note, the coefficients for the fifteen period dummies (omitted from Table 3) correspond to the findings for first generation immigrants presented in Figure 1, with a generally increasing trend and particularly high poverty risks in the most recent years.

Discussion

Several insights can be inferred from these results. The first is that there is a measurable impact of the mode of social integration of first generation immigrants on their poverty risk. This finding cannot be attributed to the numerous other indicators that we also tested in our models, most notably labor market position. The configuration of types of contacts thus is highly relevant for immigrant economic integration over and above the established findings concerning labor market integration. Concerning the question whether contacts to Germans are more beneficial than contacts to same-ethnic immigrants we can unequivocally say that both types of contacts matter. In terms of close acquaintances we see that having contacts to both groups – the own ethnic group as well as the native group – overall yields the lowest poverty level estimate. Assimilation as we defined it thus does not emerge as the overall best strategy, whereas having access to both types of contacts by being a network bridge entails the most advantageous position, although having a mix of own- and other-group immigrants also appears to be beneficial. The irrelevance of family size found in other studies concerning labor market integration of immigrants referred to above is not found in our analysis which underlines the usefulness of this particular form of bonding social capital regarding poverty.

22  

 

Furthermore, we observed that the usefulness of having native contacts varies according to host country language proficiency. On the one hand, immigrants with less than fair language proficiency do not gain significantly from these contacts, whereas for individuals who have a better language proficiency, these contacts represent a lower risk of poverty in comparison to Separation, and most decisively so for Multiple Integration and very good language proficiency. A focus on intra-ethnic social capital thus is not detrimental per se; but it does represent a disadvantage only for those immigrants who would actually have the language skills to mobilize interethnic social capital. In contrast to previous research on the impact of social capital on the labor market performance of immigrants as referred to above, we see that poverty is more than just a combination of low education, a disadvantaged labor market position and household characteristics. Possessing a diverse network in the form of having access to the host society, to the co-ethnic community and to a family of sufficient size, i.e. being integrated into both societal contexts, appears to provide immigrants with the best means to avoid income poverty, since it combines access to various forms and sources of support that can be relied upon.

Conclusion

Our analyses add important insights to the discussions surrounding immigrants’ intraethnic ties, suggesting that both intra- and interethnic relationships are important. At the same time, the analyses suggest that economic returns to contacts to the native populations in the form of lower poverty risks require some level of language acquisition. Thus, in the long run, as immigrants gain language skills, bridging social capital becomes a more important resource that mitigates poverty. But even when language proficiency is very good, the results expose a mix of bonding and bridging social capital in the form of a Multiple Integration to be most beneficial. A complete lack of contacts with the native population, as denoted by the 23  

 

Separation mode of social integration, is indeed the least promising avenue for avoiding poverty, but most strongly so for those immigrants who possess the language proficiency necessary for useful interethnic contacts. Taken together with the results for family size, we do see a definite payoff of intraand interethnic contacts with regard to income poverty. This latter finding also provides a contrast to previous research that deals with labor market outcomes of immigrants. The family often does not appear to matter for those outcomes, whereas for poverty there is a significant benefit of having a family network to rely on which indicates that the material and emotional support of one’s kin indeed can help in “getting by”. This not only shows that poverty indeed is a relevant additional incorporation dimension that is important to be investigated further. It also shows that intra-ethnic and familial contacts can be economically beneficial, which is in line with incorporation theories that stress the potential benefits of maintaining intra-ethnic relationships. Such relationships should thus not be too readily dismissed as unimportant or detrimental factors in scholarly and public debates. While these analyses contribute to a more complete picture of the consequences of different modes of immigrant social integration, there are certain limitations that need to be discussed. The first limitation concerns the questionnaire items on which we based our assessment of bonding and bridging social capital. Even though these and similar items were used in such a way before, they provide only limited insights into the quantity and quality of networks within which immigrants are located. They also do not depict the actual resources that can be accessed through them. As a consequence, these measures also do not allow us to differentiate the resources that can be mobilized or accessed in different ethnic communities. These problems could be approached by employing a more sophisticated measurement of resources, but such a measure is not available in surveys such as the one we used for this study. A further limitation stems from the fact that these measures are only fielded every five years, which together with our binary dependent variable severely hampers the 24  

 

methodological possibilities to establish the flow of causality between the concepts we applied. This also is true for the related issue of a possible status-driven self-selection into certain types of contacts, which would depict a case in which poverty influences the composition of social capital, rather than vice versa. Since most individuals in databases such as ours are not traced from birth with regard to poverty and networks, this problem of termporal causality appears almost insurmountable, and would require at a minimum a much more frequently fielded assessment of these types of contacts. Yet we tried to reduce this problem by including data waves fielded after the assessment of our primary social capital factors, essentially lagging most of our data. Moreover, given that the analyses omitting the simultaneous measures of networks and poverty yield substantially similar results, we would like to argue that this problem is somewhat less severe in our study, but we cannot decisively prove the temporal flow of causality. A final issue that must be left for future research is the question to what extent network effects unfold immediately or rather with a certain time lag between network contact acquisition and the point in time when actual socioeconomic benefits are derived through those contacts. The data base we used does not enable us to pursue this very important question further, although the findings referred to in endnote vii tentatively suggest that the influence of networks on poverty does not unfold immediately. These issues notwithstanding, the implications of our study for the literature and the debate on the relative benefits of both types of social capital is two-fold. First, the fact that both types of contacts are meaningful for alleviating poverty risks puts into perspective concerns about immigrants devoting efforts to maintain co-ethnic ties. These contacts in fact do provide valuable resources that can reduce individual poverty risks. Second, the findings show that having established contacts to the native population becomes more important once sufficient language skills are obtained. This adds an important process dimension to the discussion. Interactive assimilation in the form of beneficial contacts with the native population is not a simple choice; it is dependent on other forms of incorporation such as 25  

 

language proficiency. In this sense, an initial focus on ones’ own co-ethnic group can be seen as a normal course of events which, as we have seen, does not necessarily entail negative consequences in regard to poverty risks right from the start. Clearly more research is needed in order to shed light on the interrelations of different aspects of immigrant incorporation, for instance with regard to the consequences of relationships across different immigrant groups, which also seem to have beneficial consequences. Another avenue for progress lies in the form of qualitative assessments of incorporation “careers” and biographical profiles. Quantitative research may uncover further intervening factors, as language may not be the only relevant moderator of the relationships we investigated in this study. A clearer picture of these mechanisms may not only help to get a better scholarly understanding of these dynamics, but it may also serve to identify shortcomings in the incorporation process that need to be tackled politically.

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Figures and Tables

Figure 1: Poverty Rates for Natives and Different Immigrant Groups, 1984-2011 40 35 30 25 20 15 10 5 0 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Natives First Generation Immigrants, Years Since Immigration <= 18 First Generation Immigrants, Years Since Immigration > 18 Second Generation Immigrants Source: SOEP v28, weighted, own calculations.

Figure 2: Gross Poverty Rates for First Generation Immigrants by Mode of Social Integration, 1996-2011 50% 40% 30% 20% 10% 0% 1996

2001

2006

Multiple Integration

Assimilation

2011 Separation

Source: SOEP v28, weighted, own calculations. The error bars indicate 95%-confidence intervals.

 

Figure 3: Predicted Income Poverty Rates for Different Combinations of Social Integration Mode and German Language Proficiency (Average Adjusted Predictions Based on Models 4 and 5 in Table 3) 33  

 

Predictions, Language by Social Integration, 95%-CIs Fairly

Good

Multiple Integration Separation Assimilation Multiple Integration (No Germans)

Multiple Integration Separation Assimilation Multiple Integration (No Germans)

Multiple Integration Separation Assimilation Multiple Integration (No Germans)

Very Well

.36 .32 .28 .24

.2 .16 .12 .08 .04

0

Mean Predicted Poverty Probability

.4

Not at all/Poorly

Multiple Integration Separation Assimilation Multiple Integration (No Germans)

Omitting 1996, 2001, 2006 and 2011 waves Fairly

Good

Multiple Integration Separation Assimilation Multiple Integration (No Germans)

Multiple Integration Separation Assimilation Multiple Integration (No Germans)

Multiple Integration Separation Assimilation Multiple Integration (No Germans)

Very Well

.36 .32 .28 .24 .04

.08

.12

.16

.2 0

Mean Predicted Poverty Probability

.4

Not at all/Poorly

Multiple Integration Separation Assimilation Multiple Integration (No Germans)

Source: SOEP v28, waves 1996-2011, own calculations. The horizontal line indicates the upper 95%-confidence boundary for Multiple Integration.

   

 

34  

 

Table 1: The Four Modes of Social Integration Contacts with Ethnic-Community Members?

Contacts with HostCountry Members?

Yes

No

Yes

Multiple Integration

Assimilation

No

Separation

Marginality

Based on Berry (1997) and Esser (2001).

Table 2: Unweighted Sample Descriptives (Multivariate Model Sample) N (Person-Years) 10,124

Woman Education CASMIN: Low Medium High Language Proficiency: Not at all/Poorly Fairly Good Very Well Social Integration: Multiple Integration Assimilation Separation Multiple Integration (No Germans) Size of Family: 0-4 Persons 5-9 Persons 10-19 Persons 20-29 Persons 30 and more Persons Migration Intention Yes German Nationality Yes Country of Origin: Turkey Greece Italy Spain Other Southern Europe Eastern Europe North-, West-, Middle Europe USA, UK, Canada, Australia Other Household Type: Single Person Couple w/o Children Single Parent Couple w/ Children Multi-Generation/Other Occupational Category: Not Working In education – College or Vocational In education – Other Unemployed Retired Blue Collar Employee Self-Employed White-Collar/High-Skilled Employee

Age at immigration Number of Entries in Social Integration Instrument (1 to 3) Years since Immigration Source: SOEP v28, own calculations. Total sample N = 20,246 person-years.

35  

Mean 0.500

SD -

13,986 4,110 2,150

0.691 0.203 0.106

-

2,472 5,525 7,400 4,849

0.122 0.276 0.366 0.240

5,476 4,471 9,563 736

0.271 0.221 0.472 0.036

-

3,917 7,152 5,962 1,955 1,260 6,083 5,594

0.194 0.353 0.295 0.097 0.062 0.301 0.276

-

5,791 1,378 2,325 649 207 5,339 914 182 3,461

0.286 0.068 0.115 0.032 0.010 0.264 0.045 0.009 0.171

-

1,278 4,742 950 12,093 1,183

0.063 0.234 0.047 0.597 0.058

-

3,303 442 306 2,021 2,982 7,718 718 2,756

0.163 0.022 0.015 0.100 0.147 0.381 0.036 0.136

-

20,246 20,246 20,246

23.3 2.80 22.2

12.4 0.52 11.3

 

Table 3: Random Effects Logistic Regression Predicting Income Poverty, Beta Coefficients and Average Marginal Effects

Woman (Ref.: Man) Age at immigration Education (CASMIN, Ref.: Low) Medium High Language Proficiency (Ref.: Very Well) Not at all/Poorly Fairly Good Social Integration Mode (Ref.: Multiple Int.) Assimilation Separation Multiple Int. (No Germans) Social Integration * Language Proficiency Assimilation, Not at all/Poorly Assimilation, Fairly Assimilation, Good Separation, Not at all/Poorly Separation, Fairly Separation, Good Multiple Int. (No Ger.), Not at all/Poorly Multiple Int. (No Ger.), Fairly Multiple Int. (No Ger.), Good Number of Entries in Social Integration Instrument Size of Family (Ref.:5-9 Persons) 0-4 Persons 10-19 Persons 20-29 Persons 30 and more Persons Years since Immigration Migration Intention (Yes/No) German Nationality (Yes/No) Country of Origin (Ref.: Turkey) Greece Italy Spain Other Southern Europe Eastern Europe North-, West-, Middle Europe USA, UK, Canada, Australia Other Household Type (Single Person) Couple w/o Children Single Parent Couple w/ Children Multi-Generation/Other Occupational Category (Ref.: Not Working) In education – College or Vocational In education – Other Unemployed Retired Blue Collar Employee Self-Employed White-Collar/High-Skilled Employee

Model 1 Average Marg. Effects

Model 2 Average Marg. Effects

Beta Coefficients

Model 3 Average Marg. Effects

0.0108 0.0022***

0.0053 0.0023***

0.0733 0.0287***

0.0058 0.0023***

-0.0264*** -0.0730***

-0.0249*** -0.0831***

-0.3065*** -1.5612***

-0.0246*** -0.0829***

-

0.0490*** 0.0367*** 0.0112*

0.9470*** 0.8724*** 0.5850***

0.0506*** 0.0338*** 0.0085

0.0047 0.0260*** 0.0204

0.0114 0.0213*** 0.0191

0.5820*** 0.6962*** 0.5404

0.0088 0.0191*** 0.0185

-

-

-0.1662 -0.6944** -0.6104*** -0.4998* -0.4863** -0.5475** 0.0007 -0.2984 -0.6419

N/A N/A N/A N/A N/A N/A N/A N/A N/A

-0.0130***

-0.0168***

-0.2115***

-0.0167***

0.0323*** 0.0090* -0.0079 0.0017

0.0371*** 0.0083 -0.0116 -0.0053

0.4297*** 0.1161 -0.1589 -0.0712

0.0366*** 0.0089 -0.0111 -0.0051

-

-0.0014*** -0.0043 -0.0448***

-0.0179*** -0.0535 -0.6177***

-0.0014*** -0.0042 -0.0446***

-

-0.1059*** -0.0830*** -0.1389*** -0.0429 -0.0871*** -0.1243*** -0.1214*** -0.0263

-1.4225*** -0.9622*** -2.7707*** -0.4115 -1.0372*** -1.9622*** -1.8460*** -0.2425

-0.1055*** -0.0825*** -0.1384*** -0.0419 -0.0868*** -0.1235*** -0.1203*** -0.0260

-

-0.1674*** -0.0514* -0.1577*** -0.1688***

-1.5362*** -0.3643* -1.4015*** -1.5688***

-0.1695*** -0.0531* -0.1600*** -0.1716***

-

-

-

-

Pseudo-R² (McKelvey & Zavoina) 0.0644 0.1412 0.1436 Observations (Persons) 3,342 3,342 3,342 Observations (Person-Years) 20,246 20,246 20,246 *** p<0.01, ** p<0.05, * p<0.1. Source: SOEP v28, waves 1996-2011, own calculations. Models include dummies for the waves.

36  

 

Table 3: Continued Model 4 Beta Coefficients

Average Marg. Effects

-0.2056* 0.0192***

-0.0170* 0.0016***

-0.2192* 0.0218***

-0.0178* 0.0018***

-0.3130*** -1.2162***

-0.0257*** -0.0775***

-0.3487*** -1.2080***

-0.0279*** -0.0763***

0.7063** 0.8641*** 0.5726***

0.0364*** 0.0294*** 0.0061

0.6507** 0.8255*** 0.6231***

0.0257* 0.0157 -0.0085

0.5524*** 0.6968*** 0.4020

0.0094 0.0164** 0.0113

0.6879*** 0.9222*** 1.0876**

0.0108 0.0226*** 0.0128

-0.0235 -0.7487*** -0.5574** -0.4623 -0.6003** -0.6141*** 0.2245 -0.2960 -0.6983

N/A N/A N/A N/A N/A N/A N/A N/A N/A

-0.0942 -0.9468*** -0.6706** -0.5143 -0.6992** -0.9433*** -0.4896 -1.1807** -1.4328**

N/A N/A N/A N/A N/A N/A N/A N/A N/A

Number of Entries in Social Integration Instrument Size of Family (Ref.:5-9 Persons) 0-4 Persons 10-19 Persons 20-29 Persons 30 and more Persons

-0.2346***

-0.0193***

-0.2691***

-0.0218***

0.3638*** 0.0632 -0.2052 -0.1951

0.0320*** 0.0051 -0.0155 -0.0148

0.4165*** 0.0963 -0.1315 -0.2182

0.0360*** 0.0077 -0.0098 -0.0159

Years since Immigration Migration Intention (Yes/No) German Nationality (Yes/No) Country of Origin (Ref.: Turkey) Greece Italy Spain Other Southern Europe Eastern Europe North-, West-, Middle Europe USA, UK, Canada, Australia Other

-0.0250*** -0.0264 -0.6313***

-0.0021*** -0.0022 -0.0480***

-0.0265*** 0.0449 -0.6905***

-0.0021*** 0.0037 -0.0513***

-1.2835*** -0.7694*** -2.4342*** -0.0283 -0.9289*** -1.7702*** -1.8333*** -0.2806

-0.1008*** -0.0693*** -0.1401*** -0.0031 -0.0802*** -0.1217*** -0.1239*** -0.0286

-1.2555*** -0.6508*** -2.4280*** 0.0414 -0.8324*** -1.6391*** -1.9987*** -0.3052

-0.0957*** -0.0582*** -0.1353*** 0.0044 -0.0710*** -0.1127*** -0.1248*** -0.0298

-1.5526*** -0.2673 -1.2978*** -1.5636***

-0.1616*** -0.0355 -0.1425*** -0.1623***

-1.7553*** -0.3873* -1.3512*** -1.8135***

-0.1790*** -0.0509* -0.1497*** -0.1826***

0.9825*** 0.4281* 0.8124*** 0.0741 0.3153 -1.5803*** -1.6047***

0.1452** 0.0559* 0.1159*** 0.0088 0.0400 -0.1150*** -0.1159***

0.9718** 0.6471** 0.7392*** 0.0556 0.2409 -1.7011*** -1.6047***

0.1438* 0.0894** 0.1043*** 0.0066 0.0302 -0.1215*** -0.1180***

Woman (Ref.: Man) Age at immigration Education (CASMIN, Ref.: Low) Medium High Language Proficiency (Ref.: Very Well) Not at all/Poorly Fairly Good Social Integration Mode (Ref.: Multiple Int.) Assimilation Separation Multiple Int., No Germans Social Integration * Language Proficiency Assimilation, Not at all/Poorly Assimilation, Fairly Assimilation, Good Separation, Not at all/Poorly Separation, Fairly Separation, Good Multiple Int. (No Ger.), Not at all/Poorly Multiple Int. (No Ger.), Fairly Multiple Int. (No Ger.), Good

Household Type (Single Person) Couple w/o Children Single Parent Couple w/ Children Multi-Generation/Other Occupational Category (Ref.: Not Working) In education – College or Vocational In education – Other Unemployed Retired Blue Collar Employee Self-Employed White-Collar/High-Skilled Employee Pseudo-R² (McKelvey & Zavoina) Observations (Persons) Observations (Person-Years)

0.2360 3,342 20,246

37  

Model 5 (Omitting 1996, 2001,2006 and 2011 waves) Beta Average Coefficients Marg. Effects

0.2470 3,015 15,052

 

Notes                                                                    i

The term “ethnic” is used in this article as a shorthand for the context of origin of immigrants which often simply refers to the country of origin. We do acknowledge that ethnicity in the proper sense is a concept that is much more fluid and subject to constant reconstruction by immigrants and natives alike (Wimmer, 2013). ii As noted above, the terms bonding and bridging social capital are sometimes used to denote contacts both within and between networks (of different strata, regions, groups etc.) as well as the proximity or strength of the respective relationship. In the latter sense, bonding social capital would refer to family and friends, whereas bridging social capital is accessible only through emotionally loose relationships, i.e. distant acquaintances. Yet within immigrant incorporation research, bonding social capital usually refers to contacts to members of one’s own ethnic group, whereas bridging contacts indicate ties to the native population, without further qualification to the quality and intensity of these contacts. This is in line with Burt (2001) who formulates tie strength and tie bridging as conceptually independent of one another. We will follow this approach and thus refer to inter-and intra-ethnic contacts rather than the degree of closeness between alter and ego whenever we use the terms bridging and bonding. iii About one third of the total number of ties with origin information reported in this instrument are declared to be relatives of the respondents. iv Variance inflation factors obtained from a linear probability model indicate that there are no multicollinearity problems. v Since our dependent variable is binary, calculating a fixed effects logistic regression model would be possible only for those respondents that report transitions into or out of poverty. Persons that are permanently in or out of poverty would be dropped. In our case this approach would lead to a considerable sample size reduction, removing about 70% of the respondents. This is so because poverty as such is a relatively seldom phenomenon. Under these conditions, a fixed effects logistic regression approach appears to be inefficient and thus is not a feasible alternative. vi  The unweighted shares of persons reporting a good or very well German language proficiency for Separation, Assimilation and Multiple Integration are 46%, 80% and 72%, respectively.  vii We thank one anonymous Reviewer for the suggestion to rerun the analyses for the first, second, third and fourth waves after the measurement of social integration separately in order to have at least a coarse approximation of the issue of a time lag between the measurement of integration and poverty. However, as noted in the data section above, the problem is that we cannot determine when exactly any change in network composition happened as that change may have occurred years before the year in which it was actually measured. Put differently, there is no definite way to determine the time span between the actual event of the network change and the occurrence of poverty with sufficient precision given the available data. Nonetheless, when we follow the advice given, the results obtained from these analyses indeed suggest that the effect of networks does not occur immediately, since using the years directly following the measurement of integration (i.e. 1997, 2002, 2007) does not yield the findings presented in Table 3 with regard to the integration by language coefficients, whereas using the later waves, these results are mirrored to a larger extent. This result pattern is tentative evidence for a certain time lag, but we cannot establish this with certainty given the present restrictions in the data source used. Moreover, the sample sizes are much smaller when the analysis is carried out in this fashion, so that these results are not entirely reliable due to the large number of interaction coefficients and the resulting reduction in cell sizes, some of which are below 30 for the integration by language interactions.

38