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KYKLOS, Vol. 64 – August 2011 – No. 3, 342–365

Integration, Social Networks and Economic Success of Immigrants: A Case Study of the Turkish Community in Berlin Alexander M. Danzer and Hulya Ulku

I. INTRODUCTION Germany holds the largest foreign population of all European countries. However, until the adoption of a new immigration law in 2005, official political statements refused to perceive Germany as a country of immigration and neglected the need for a comprehensive integration policy.1 Recent efforts to bring integration onto the political agenda were fueled by fears of immense social costs brought about by the deepening of ‘parallel societies’.2 These fears manifested themselves in public debates about the generally low educational attainments and apparently violent tendencies of some young immigrants. In this paper, we ask whether integration is an economically rational strategy for migrant households, of whom many are located on the lower part of the income distribution in Germany. By reconsidering integration in terms of opportunity

Alexander M. Danzer, Department of Economics, University of Munich (LMU) and IZA Bonn. Mailing address: LMU Mu¨nchen, Geschwister-Scholl-Platz 1, 80539 Mu¨nchen, Germany. E-mail: [email protected]. Hulya Ulku, Institute for Development Policy and Management (IDPM), University of Manchester. Mailing address: IDPM, Arthur Lewis Building, Oxford Road, Manchester M13 9PL, UK. E-mail: [email protected]. This paper is part of a wider research led by the second author and funded by the Nuffield Foundation concerning the economic behavior and integration of Turkish households in Berlin. We would like to thank the Nuffield Foundation for their financial support. We also thank Oded Stark, Ira N. Gang, Uma Kothari, Sam Hickey, Tanja Mu¨ller, Thankom Arun, Ingrid Tucci, Natalia Weisshaar and the participants of the DIW Research Seminar, IZA Summer School and World Economic Congress as well as the editors of this journal and the anonymous referees for their helpful comments. We are grateful to the research assistants Zeycan Yesilkaya, Fatma Goksu, Cagri Kahveci, Beyhan Yildirim, Deniz Erkan, RutMaria Gollan and Alper Yenilmez for their vigorous and excellent work on interviews and data entry. 1. In 2004 about 500 million Euro of the Federal budget were earmarked for integration measures (OECD 2007: 210). However, no comprehensive integration policy was formulated. 2. For instance, von Loeffelholz (2001) has estimated the foregone macroeconomic benefits from nonintegration of ethnic minorities at one to two percent of GDP in Germany, mostly due to high unemployment among low-skilled migrants. This stands in contrast to early cost-benefit analyses of the guest worker migration under the assumption of full employment (Blitz 1977). The term ‘parallel societies’ was coined by the sociologist Wilhelm Heitmeyer with respect to the integration deficits of immigrants in Germany.

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INTEGRATION, SOCIAL NETWORKS AND ECONOMIC SUCCESS OF IMMIGRANTS

costs, we show that remaining in an ethnic network might be the optimal economic strategy for less well-endowed households. We employ newly developed data collected from 590 Turkish households residing in Berlin to analyze the determinants of the integration of Turkish immigrants3 into the German polity, society and economy and the impact of this integration on income generation. We account for three different forms of integration to assess their relative importance in economic success. As distinct from the existing literature, we take into account the role of local and transnational networks on both integration and the economic success of immigrants. Specifically, we aim at providing an empirical analysis of the following questions: 1. What determines integration? 2. Does integration promote the economic success of immigrants? 3. Do ethnic and transnational networks affect integration and income? 4. Do the impacts of ethnic or transnational networks for gaining economic success differ by integration status? 5. Do the integration and network channels of income generation differ over the distribution of migrants’ unobserved abilities? Our study contributes to the rapidly growing literature on the economic success of immigrants and the impact of their integration choices on their economic performance in four ways. The first novelty of the paper is the use of an up to date and comprehensive dataset on the Turkish population in Berlin collected in mid-2007. This allows us to distinguish the effects of many different characteristics such as sub-ethnic characteristics, familial, local and transnational networks, and social links to the home country. The second contribution of this study is that we combine the ‘ethnic identity’ literature with the ‘network formation’ literature on immigrant populations in the analysis of the determinants of economic success. In particular, by using an endogenous switching regression model we provide an analysis of the joint impact of integration as well as local ethnic, local familial and transnational networks on the economic success of immigrants, and investigate their effect over the distribution of immigrants’ unobserved characteristics. Third, and distinct from the existing literature on migrants in Germany that mainly use national level data, our data allows us to explicitly take into account the interactions of the above mentioned variables as they prevail at the local level. Finally, our analysis focuses exclusively on Turkish migrants. To the best of our knowledge, there is no study providing an economic analysis of the determinants and the interaction between integration and economic success in the context of Turkish immigrants, the largest migrant group in Germany. 3. By immigrant we mean either a migrant or a descendent of a migrant. The recruitment of guest workers from Turkey was initiated in 1961 but stopped in 1973 as a consequence of the economic crisis. Subsequent immigration continued in the framework of family reunification.

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ALEXANDER M. DANZER/HULYA ULKU

The main findings of our analysis are in line with those of the existing literature. Personal characteristics such as education, being female head of household, years since migration, and being born in Germany are positively associated with integration, while local ethnic and familial, or transnational networks have no impact – with the exception that local ethnic network promotes social integration. We find that, among three dimensions of integration (political, social and economic integration) only political integration has a strong positive impact on economic success. However, the degree of integration, which is measured as the combination of all three dimensions, has a consistently positive impact on economic success, suggesting that full integration is important for promoting income levels. We also find that local ethnic and familial networks are positively associated with economic achievements of the unintegrated migrants, while maintaining a transnational ethnic network is negatively correlated. When investigating the effect of integration and networks over the distribution of unobserved ability, it turns out that integration is a positive determinant of economic success in upper quantiles only. Economically less-able Turkish immigrants do not receive an economic integration premium, but benefit from local ethnic networks. Given that Berlin holds the largest and most heterogeneous Turkish population in Germany (Scho¨nwa¨lder and So¨hn 2007) and that data collection was carried out using a random sampling methodology, our findings can, to some extent, be generalized to the Turkish population residing in Germany. We would also like to stress the limitations of our analysis. Given that we use cross-sectional data, inter-temporal analysis taking into account unobservable characteristics of immigrants is beyond the scope of this paper. Further, we do not deliver an analysis of endogenous ethnic enclave formation. The remainder of the paper is structured as follows: In Section II we give an overview of the theoretical background of our analysis and a review of the relevant literature, followed by Section III which introduces the new dataset and the methodology employed. In Section IV we present descriptive and regression results, before concluding with policy relevant implications in Section V.

II. THE ECONOMICS OF IMMIGRATION AND INTEGRATION Until recently the economic literature on migration and integration has been dominated by neoclassical approaches implying a cost-benefit calculation of migrants. In recent years the topic has attracted new attention in the economics of ethnicity (Zimmermann 2007), which argues that ethnicity and culture may impact people’s preferences and behavior. Owing to both strands of literature,

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our paper asks whether ethnicity may mobilize economic opportunities for immigrants. Generally speaking, we argue that an immigrant chooses between integration into the host country – with access to the labor market – and remaining in an ethnic network – with access to ethnic goods, ethnic labor market niches and informal insurance. In this paper we reformulate the issue of integration in economic terms and conduct an in-depth analysis of the interrelationships between integration and economic success with a special focus on the role of ethnic networks. This issue has been largely ignored in the economic and political debate in Germany.

1. Integration of Migrants The literature on integration of immigrants faces the problem of how to define the multidimensional concept of integration and how to measure an appropriate outcome variable. Due to data limitations, most previous publications have focused on subjective integration measures such as self-assessed assimilation (except for citizenship) (Dustmann 1996; Constant et al. 2009). In our paper we understand integration as the membership in a society with access to its political, economic and social resources, and measure these three dimensions using objective indicators. In the literature, social and political integration are mainly associated with exposure to the host country and the consequent habituation to new tastes and rules (Dustmann 1996). An underlying assumption of this approach is that integration is an exogenous process. Integration efforts have hardly been explained by incentive structures or networks (Fan and Stark 2007; DeVoretz 2008). We believe that integration becomes attractive for an immigrant if it promises economic success, i.e. opens up labor market chances or prospects for the immigrant’s children. Where labor market discrimination prevails, the payoffs from integration are expected to be small. Empirical studies focus on three key factors of integration: time exposure, geographic exposure and social exposure. Years since migration are often used to measure the exposure to the host culture and are generally positively associated with integration (Dustmann 1996; Constant and Massey 2002). In several studies age at migration and pre-migration education in the home country (Constant et al. 2009) are used as proxies for adaptability to the host country. Similarly, place of residence matters for integration as homogenous enclaves offer fewer incentives but also fewer opportunities for integration (Chiswick and Miller 1996; Danzer and Yaman 2010).4 Borjas (1995), for instance, found slow convergence of human capital endowments of immigrant 4. Yang (1994) argues that information about naturalization is easily shared in ethnic enclaves thus fostering integration.


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groups towards natives due to the intergenerational transmission of human capital inside ethnic enclaves.5 We understand social exposure as established contacts to host country institutions (Yang 1994). Children in school age, for instance, have been found to improve parents’ integration (Dustmann 1996). Having close German friends fosters integration (Constant et al. 2009) while transnational family ties significantly reduce it (Constant and Massey 2002). The relationship between ethnic networks and integration has received much attention in sociology. The proponents of social capital theory argue that membership in horizontal networks can improve social trust and thus foster political integration of immigrants (compare Coleman 1990; Putnam 2000). Haug (2003) finds that social integration into Germany, which she proxies by inter-ethnic friendships, is higher among men and later migration cohorts. Berger et al. (2004) investigate the determinants of political integration among ethnic communities in Berlin and argue that better educated and cross-ethnic network members are better integrated. In a comparable study for Amsterdam, Tillie (2004) finds that membership in the own ethnic network can increase integration. We argue that integration is a choice coming at a certain cost. Less well endowed immigrants might find it advantageous to use ethnic networks rather than integration for the generation of income. Different from the previous literature we consider the determination of integration and income jointly – and test whether networks can substitute for missing integration.

2. Economic Success of Migrants Much of the literature on the economic success of immigrants is concerned with their labor market performance in comparison to the native population or to earlier immigrant cohorts (Borjas 1994). Traditionally, the economic success of immigrants has been studied against the background of human capital theory and segmented labor market theory. However, recent developments have added the concepts of ethnicity and integration to this literature. Human capital theory relates the success of migrants to their investment strategy into destination specific human capital after arrival. Chiswick (1978) argues that migrants lose on economic status upon arrival in the destination country but can improve their disadvantaged position by acquiring human capital for the destination labor market. Empirically the most prevalent positive determinants of economic success are human capital (Chiswick and 5. Drever (2004) found that integration is not generally lower in ethnic enclaves in Germany. However, Danzer and Yaman (2010) show that living in an ethnic enclave in Germany has a causal negative effect on language skills.

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DebBurman 2004), language proficiency (Espenshade and Fu 1997) and labor market experience (Chiswick et al. 1997). For Germany, the economic success of immigrants is well documented, especially in the fields of employment (Kogan 2004) and self-employment (Constant and Zimmermann 2006). Segmented labor market theory argues that less well endowed migrants tend to be employed in the labor intensive sector of the economy where they might never catch up with natives (Piore 1979). The evidence of the economic failure of migrants suggests that discrimination in access to specific occupations causes a (persistent) wage gap. However, after controlling for occupational status, the empirical findings of this literature are similar to those of the human capital approach (Constant and Massey 2005 for Germany; Adsera and Chiswick 2007 for Europe). According to the economics of ethnicity, ethnic and social variety may be economically beneficial as heterogeneous societies are endowed with diverse preferences, abilities and problem solving strategies (Alesina and La Ferrara 2005). However, variety can enhance productivity only through social interaction. Communication with friends and colleagues from the host country makes information on labor market opportunities available. As noted in the literature, sequential interaction can also build up trust and foster economic performance (Lorenz 1999). Although this literature links various economic indicators to integration, the latter is rarely examined as a determinant of economic success. Among the few such studies, Dustmann (1996) found that subjective assimilation is insignificant in determining economic success. More objective measures of integration seem to play a significant but weak role in determining economic behavior (Zimmermann 2007). However, in most of this literature integration remains exogenous and is not placed within an individual’s utility maximization (except for Fan and Stark 2007). We argue that integration promotes income on average, given that it coincides with human capital enhancements and a wider array of opportunities, but we also acknowledge that a significant part of the migrant population may prefer ethnic networking rather than integration as the latter may be costly. We believe that ethnic networks are an important determinant of the economic behavior of migrants as well as their integration efforts. Ethnic networks can be advantageous for their members: trading inside the enclave implies lower transaction costs (Lazear 1999), vacancies are efficiently filled (Topa 2001), discrimination is absent (Borooah and Mangan 2007), and the demand for ethnic goods can be easily met. But ethnic networks might involve negative human capital externalities, limited labor market opportunities or specific welfare use cultures (Borjas and Hilton 1996; Bertrand et al. 2000). Remaining in the ethnic network might lead migrants on lower incomegenerating paths, especially if they work predominantly in a segmented labor r 2011 Blackwell Publishing Ltd.

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market (Piore 1979).6 Following from this, an immigrant will integrate into the host society only if (i) the costs are smaller than the expected gains from integration, and if (ii) the gains from integrating minus the foregone gains from remaining in the ethnic network are positive (compare Yang 1994; DeVoretz 2008). Evaluating the gains and costs from integrating and networking results in the question whether ethnic networks can substitute for integration, an issue that has recently gained attention in the sociological literature (Fong and Ooka 2002). In sum, the findings of the existing literature on integration and economic success suggest that both are mainly driven by demographic features of migrants (such as time spent in the host country, age, language proficiency, education level and labor market experience), characteristics of households, exposure to social and cultural life in the host country, and social networks. Although the majority of studies acknowledge the inter-linkages between integration and economic success, very few have studied these two variables simultaneously. Thus our paper provides a joint analysis of the determinants of integration and economic performance and takes into account the impact of local and transnational networks of the migrants on both integration and economic success. III. DATA AND METHODOLOGY

1. Data We employ a new dataset collected during May/June 2007 from 590 Turkish households residing in one of eight major districts of Berlin which hold 98.2% of the Turkish population of the city. In addition to standard variables, the information on immigrants’ social networks, their familial linkages in the host and home country, and behavioral choices are covered in our data in greater detail than in the GSOEP data (which has been predominantly used for the study of immigrants in Germany so far).7 Berlin has been chosen as the focal point of the study since it holds the largest Turkish population in Europe outside Turkey and is the most cosmopolitan city in Germany, enabling us to cover households from different socio-economic backgrounds. The data collection followed a stratified random sampling strategy with respondents being chosen with probability proportional to size of the Turkish community in the districts. The dataset comprises detailed information on the 6. Constant and Massey (2005) show that ethnic discrimination is more prevalent in the access to the German labor market rather than in wage setting. Muslims face especially high levels of prejudices (Borooah and Mangan 2007). 7. See Ulku (2010) for more details on data, methodology and sampling.

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head of household and all household members. However, our dataset also has some limitations. First, it covers one city only and such restricts the scope for generalizations. Second, the sampling framework might potentially lead to an under-representation and self-selection of women as they might be more reluctant to respond to our survey. We aimed to reduce this problem by hiring a gender-balanced group of Turkish interviewers. Third, the dataset is a cross section survey thus we cannot track immigrants over time.

2. Variables In this section we discuss issues of operationalizing the concepts of interest, namely different forms of integration, economic success and ethnic networks, and provide an overview of the variables used in the multivariate analysis. We consider three dimensions of integration: political, social and economic integration. Under political integration we understand the process under which a migrant receives access to political and social rights. A good measure of this integration is citizenship which grants voting rights unavailable to nonGermans. In our sample, almost 40 percent of respondents hold German citizenship (Table 1). Social integration comprises social connections with the host country and is proxied with a variable counting the number of close German households who were ready to lend money to the respondent if he/she found him-/herself in serious financial troubles. Having German friends reflects access and contact to the people; it confirms knowledge of and trust in the natives. Economic integration is proxied by ‘having a German boss or German employee’ as these might increase the likelihood of economic integration. Thus, four variables are used as a proxy for different types of integration: (i) a binary political dimension outcome (citizenship), (ii) a binary social integration outcome (having close German friends), (iii) a binary outcome proxying economic integration (having a German boss or German employee), (iv) an index variable for the degree of integration consisting of the summation of (i) to (iii), ranging from zero (non-integrated) to three (integrated in all dimensions). We measure economic success as per adult equivalent household income and analyze it at the household rather than individual level, arguing that resources are shared in households and that labor decisions are taken interdependently. Thus, economic success of an individual consists of their own net monthly income plus the (pooled) net monthly income of other household members. Net income refers to the income after tax, social security and pension contributions. The sample average total monthly net household income (not per adult equivalent) is 1,856 h (Table 1). The explanatory variables used in the analysis of integration comprise demographic characteristics of the head of household, household characteristics, r 2011 Blackwell Publishing Ltd.

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ALEXANDER M. DANZER/HULYA ULKU Table 1 Means and Frequencies of Main Variables for Full Sample Variable

German Citizenship Close German Friends German Boss German Employees German Education Female Head of HH Own House in Germany Fixed Assets in Turkey Born in Germany Married Return Plan Full Time Employed Own Business Unemployed

% in Total Sample

Variable

Mean Stand. Dev.

39.66 18.31 33.22 3.73 47.29 15.25 9.83 58.47 16.10 72.37 42.71 35.76 11.36 18.64

Income Age Years of Education Time Spent in Germany Number of Close Turkish Friends in Germany Number of Close Turkish Friends in Turkey Number of Household Members Number of Working Household Members Number of Family Members in Germany Number of Close Family Members in Turkey Children/Spouse in Turkey Number of Foreigners in the Family Frequency of Visits to Turkeya Integration Indexb

1856 1033 41.95 12.22 10.87 3.81 25.20 10.52 4.47 7.11 1.98 5.46 3.25 1.62 1.16 0.87 11.52 11.85 2.83 2.75 0.20 0.88 0.33 0.76 10.09 2.31 0.98 0.88

a Frequency of visits to Turkey is an index variable taking on values between 0 for no visit and 13 for the most frequent visit, bIntegration index takes on values between 0 for no integration and 3 for the highest degree of integration, i.e. being integrated in all three types of integration: social, political, and economic. Source: Authors’ calculations.

financial conditions social ties to Turkey and networks which include familial networks in Germany and local and transnational networks. We measure familial networks in Germany by the number of family members in Germany, which include parents, siblings, aunt/uncles and cousins. Local and transnational networks are proxied by having close Turkish friends in Germany and Turkey who could provide financial help to the household in difficult times. We expect local and familial networks in Germany to promote economic success in Germany and the transnational networks to impede it, as the earlier variables shift the focal point of economic and social activities to Germany while the latter shift it to Turkey. We have also taken into account the ethnic and religious backgrounds of the migrants as cultural differences among these groups may affect integration differently.

3. Econometric Modeling To estimate the determinants of integration and economic success of the Turkish migrants we employ Seemingly Unrelated Regression (SUR) analysis and Full Information Maximum Likelihood (FIML) Regressions. SUR analysis enables us to estimate the income (Y) and all four types of integration (I) equations simultaneously taking into account the correlations between the

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error terms of the following two equations ln Yi ¼ aIi þ bXi þ e1i

ð1Þ

Ii ¼ aYi þ bXi þ e2i

ð2Þ

Although SUR analysis is useful to estimate the direct impact of integration variables on income, it does not correct for the potential endogeneity between income and integration. In order to achieve this, we employ FIML estimation – also referred to as endogenous switching models. The basic idea behind this method is that immigrants either belong to an integrated or non-integrated group with the counterfactual state being unobserved. As our interest concerns not only the interaction between integration variables and income, but also how the coefficients of covariates X in equation (1) differ by integration status we can estimate a switching regime with two-step least squares. As the two-step procedure yields inconsistent and inefficient estimates, Maddala (1983) has proposed a methodology to solve the equation system simultaneously by FIML estimation. The base for the income regressions in both integration states is the ‘criterion function’ according to which individuals are sorted into integrated and non-integrated groups of immigrants: Ii ¼ 1 if dXi þ ui > 0 Ii ¼ 0 if dXi þ ui 0

ð3Þ

The error term ui and the error terms from the two income equations (e11i and e12i for the two integration states) are assumed to have a trivariate normal distribution (Lokshin and Sajaia 2004). Identification of the criterion function stems from the familial relationships to Turkey, while the income equation is identified through the number of working age adults. Once the group membership is determined, the income equations can be estimated for both groups without bias. In order to assess the association of income with integration and the networks at different levels of unobserved ability of immigrants, we also conduct quantile regression analyses at different quantiles of the error distribution of the income equation. A simple approach to investigate whether integration has a stronger impact on income for less- or more-able immigrants is to apply a semi-parametric quantile regression model over the error distribution. We estimate the relationship conditional on the explanatory variables Qy ðYi jXi Þ at different quantiles y rather than at the sample mean as in OLS, which results in lower sensitivity to outliers (Koenker and Hallock 2001). r 2011 Blackwell Publishing Ltd.

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IV. EMPIRICAL ANALYSIS This section provides an econometric analysis of the determinants of integration and income and the inter-linkages between these two variables. Before showing multivariate results, we provide descriptive statistics of the main features of integrated and unintegrated immigrants. As seen from the unconditional means of Table 2, better-integrated persons are younger, more likely to be female, unmarried and have lived longer in Germany. Being born or having received a degree in Germany as well as having higher incomes and education levels is significantly more common among the better-integrated immigrants. Table 3 shows results for the level of integration and ethnic networks by income quintiles. Integration indicators are positively associated with income while local Table 2 Means and Frequencies of Main Variables by the Degree of Integration Fully NonIntegrated Integrated

Household Income Per Capita Income Income Per Adult Equivalent Age Years of Education Time Spent in Germany Number of Close Turkish Friends in Germany Number of Close Turkish Friends in Turkey

Mean

Mean

2213 982 1194

1597 634 787

39.4 13.9 29.2 4.5

42.9 10.0 22.8 4.1

1.4

1.7

Fully NonIntegrated Integrated %

%

Male German Education Born in Germany

52.9 85.3 38.2

77.5 30.7 7.0

Married Return Plans Turk Alevite

61.8 14.7 76.5 32.4

76.0 44.0 83.0 20.5

Fully Integrated: If the respondent has all of: German citizenship, close German friends, German boss/German employee. Non-Integrated: If the respondent does not have any of the above. Note: Total numbers of observations of fully integrated are 34 while non-integrated are 200. Source: Authors’ calculations.

Table 3 Integration and Ethnic Networks by Income Quantile

Quantile 1 Quantile 2 Quantile 3 Quantile 4 Quantile 5 Total

German Citizenship

Close German Friends

German Boss/ Employee

Close Turkish Friends in Germany

Close Turkish Friends in Turkey

Local Family Network

33.9% 30.1% 40.7% 46.0% 51.0% 39.7%

14.8% 17.1% 17.9% 22.1% 21.6% 18.3%

30.4% 35.8% 39.0% 40.7% 39.2% 37.0%

4.7 4.3 4.3 4.0 5.0 4.5

2.0 1.7 1.7 2.4 2.2 2.0

12.2 11.0 11.7 9.4 13.2 11.5

Source: Authors’ calculations.

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INTEGRATION, SOCIAL NETWORKS AND ECONOMIC SUCCESS OF IMMIGRANTS Table 4 Integration and Ethnic Networks by Immigrant Generation German Close German Close Turkish Close Turkish Local Citizenship German Boss/ Friends in Friends in Family Friends Employee Germany Turkey Network First Generation Second Generation

34.8% 66.7%

16.4% 30.1%

36.2% 40.9%

4.5 4.1

2.0 1.8

10.6 16.1

Source: Authors’ calculations.

and inter-national networks are u-shaped in income. Table 4 reports the integration and economic success indicators for first and second generation immigrants. Immigrants of the second generation seem better integrated than their parents’ generation. Yet, the differences are only significant in the political and social sphere. The second generation’s relatively disappointing economic integration may be rooted in their weak educational success (Riphahn 2003), and may offer one explanation for their earnings gap to natives (Hammarstedt 2009).

1. Analysis of the Determinants of Integration The findings of the baseline SUR analysis are reported in columns 2, 4, 6, and 8 in Table 5. As seen from the table, being female is a positive determinant of all integration variables. Education and age are significantly positive for three out of four types of integration and the age effect is characterized by non-linearities as indicated by the significantly negative quadratic. Time spent in Germany and being born in Germany have a positive impact on all integration variables except for social integration, and holding a German degree has significant impact only on the degree of full integration. The weak impact of German schooling on integration confirms earlier findings from Dustmann (1996) and may be related to the poor educational prospects of migrants in the German educational system (OECD 2007). Marital status and being from Turkish ethnic background have no association with any of the integration variables, while being from Alevite subreligious group is positively associated with political and social integration and negatively associated with economic integration. None of the network variables are significant, with the exception that having local ethnic networks in Germany promotes social integration but reduces economic integration. Finally, household size has a significant positive impact only on political integration and the degree of full integration, and income has a positive impact on political, economic and full integration while having no impact on social integration. r 2011 Blackwell Publishing Ltd.

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354

Alevite

Married

Female

Age squared

Age

Yrs of education

Education in Germ.

Born in Germany

Time in Germany

Income (log), AE

Integration index

Social Integration

Economic Integration

Political Integration

Table 5

0.002 (0.57) 2 0.019 (0.22) 0.072 (1.40) 0.020 (3.53) 0.010 (0.85) 2 0.000 (0.91) 2 0.111 (2.23) 0.008 (0.15) 2 0.085 (2.03)

0.169 (4.32)

(1)

Income

0.209 (4.26) 0.009 (2.54) 0.514 (4.62) 0.072 (1.16) 0.010 (1.54) 0.037 (2.75) 2 0.000 (2.89) 0.127 (2.19) 2 0.046 (0.77) 0.131 (2.70)

Political integration (2)

0.003 (1.03) 0.060 (0.69) 0.082 (1.59) 0.022 (3.92) 0.016 (1.38) 2 0.000 (1.46) 2 0.096 (1.92) 2 0.003 (0.05) 2 0.065 (1.54)

0.035 (0.90)

(3)

Income

0.102 (2.01) 0.010 (2.49) 0.240 (2.09) 0.061 (0.96) 0.013 (1.82) 0.019 (1.36) 2 0.000 (1.68) 0.109 (1.83) 0.068 (1.09) 2 0.002 (0.04)

Economic integration (4)

0.003 (1.17) 0.066 (0.77) 0.082 (1.59) 0.022 (3.94) 0.016 (1.34) 2 0.000 (1.44) 2 0.095 (1.90) 0.003 (0.06) 2 0.063 (1.50)

0.034 (0.69)

(5)

Income

SUR Regression of Income (log) Using Different Integration Variables

0.028 (0.69) 2 0.004 (1.28) 2 0.008 (0.09) 0.069 (1.39) 0.010 (1.85) 0.034 (3.15) 2 0.000 (2.96) 0.090 (1.93) 2 0.064 (1.31) 2 0.064 (1.62)

Social integration (6)

0.003 (0.99) 0.019 (0.22) 0.056 (1.08) 0.019 (3.41) 0.007 (0.62) 2 0.000 (0.76) 2 0.123 (2.46) 0.005 (0.10) 2 0.069 (1.67)

0.095 (4.26)

(7)

Income

0.444 (5.13) 0.005 (0.73) 0.538 (2.73) 0.281 (2.58) 0.024 (1.97) 0.093 (3.93) 2 0.001 (3.65) 0.365 (3.57) 2 0.052 (0.48) 0.079 (0.92)

Integration Index (8)




464

0.119 (2.62) 2 0.170 (12.06) 0.230 (10.03) 0.003 (1.75) 0.008 (1.49) 2 0.009 (1.68) 2 0.001 (0.31) 2 0.005 (0.80) 2 0.003 (0.55) 0.014 (1.21) 2 0.028 (1.09) 0.046 (0.26) 2 0.007 (0.21) 464

2 0.074 (1.35) 0.042 (2.34)

Political integration (2)

464

0.110 (2.42) 2 0.168 (11.87) 0.236 (10.12) 0.003 (1.73) 0.008 (1.43) 2 0.010 (1.87)

(3)

Income

0.001 (0.41) 2 0.011 (1.68) 0.006 (0.87) 2 0.014 (1.12) 2 0.004 (0.15) 0.306 (1.69) 0.029 (0.88) 464

0.003 (0.05) 0.011 (0.58)

Economic integration (4)

464

0.111 (2.44) 2 0.168 (11.90) 0.237 (10.23) 0.003 (1.79) 0.007 (1.26) 2 0.010 (1.86)

(5)

Income

2 0.002 (1.38) 0.015 (2.81) 0.004 (0.73) 2 0.007 (0.74) 2 0.009 (0.42) 2 0.113 (0.79) 2 0.010 (0.39) 464

2 0.004 (0.09) 2 0.002 (0.13)

Social integration (6)

464

0.114 (2.52) 2 0.170 (12.02) 0.224 (9.68) 0.003 (1.69) 0.007 (1.30) 2 0.009 (1.77)

(7)

Income

2 0.000 (0.02) 0.001 (0.10) 2 0.001 (0.09) 2 0.001 (0.04) 2 0.044 (0.95) 0.200 (0.64) 0.048 (0.85) 464

2 0.104 (1.08) 0.087 (2.75)

Integration Index (8)

income. Source: Authors’ calculations.

Absolute value of z statistics in parentheses significant at 10%; significant at 5%; significant at 1%. Note: income refers to per adult equivalent (AE)

Observations

Parents in Turkey

Spouse in Turkey

Children in Turkey

Siblings in Turkey

Trans-national ethnic network

Local ethnic network

Local family network

Number of working HH members

Household size

Turk

(1)

Income

Table 5. (Contd)


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The findings of the endogeneity robust FIML estimation of the determinants of integration are reported in columns 3, 6 and 9 of Table 6. Similar to the SUR results, time spent in Germany, being born in Germany, and being a female head of household are positive. Age is a nonlinear determinant of belonging to the group of politically and economically integrated migrants but does not affect social integration. Likewise, years of education continue to be an important determinant of all types of integration. Different from the SUR analysis, German education becomes significant for political integration. Familial networks in Germany and transnational networks in Turkey have no significant impact on any form of integration, while local ethnic networks are significantly positive only for social integration. In addition, marital status, size of household, being from Turkish ethnic group, and having parents in Turkey are not significant in any of the regressions, and having siblings and children in Turkey are only significant in the political integration with positive and negative signs respectively. Moreover, having a spouse in Turkey has a positive effect on economic integration and a negative effect on social integration. These findings show that years of education and being female are the common determinants of all forms of integration, although the latter is marginally insignificant in social integration. The former finding is common to several studies for Germany (Dustmann 1996; Constant et al. 2009), while the latter further adds to the mixed results of this literature. Time spent and being born in Germany are all important determinants of all types of integration (except for social integration), which confirms the important role of habituation in the host country (i.e. Dustmann 1996). Age has a strong nonlinear relationship with political integration and the degree of full integration, and a weak non-linear relationship with social and economic integration. In terms of the relationship between networks and integration, the results show that neither transnational networks nor familial networks in Germany have any significant impact on any integration variables, while having strong Turkish networks in Germany have a positive impact on social integration only. In addition, all forms of integration are independent of marital status and being from a particular Turkish ethnic group, while only political integration is positively related to being from Alevite sub-religious group. We have expected this positive impact from being Alevite but can hardly disentangle whether Alevites tend to value integration comparatively high or whether their past political isolation in Turkey has pushed them into integration.

2. Impact of Integration on Economic Success After assessing the determinants of integration, this section provides an analysis of the relationship between different forms of integration and income

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Local family network

Household size

Turk

Alevite

Married

Female

Age squared

Age

German education

Yrs of education

Born in Germany

Time in Germany

2 0.119 (1.22) 0.014 (2.02) 0.029 (0.46) 0.001 (0.05) 2 0.000 (0.11) 2 0.098 (1.37) 0.070 (1.14) 2 0.140 (2.69) 0.044 (0.66) 2 0.180 (9.82) 0.003 (1.67)

(1)

0.177 (1.47) 0.043 (4.11) 0.196 (2.05) 0.073 (1.78) 2 0.001 (1.64) 2 0.026 (0.28) 2 0.076 (0.79) 0.127 (1.52) 0.268 (3.02) 2 0.157 (5.92) 0.003 (0.75)

(2)

DV: income Politically Politically Unintegrated Integrated

Regression 1

0.022 (2.26) 1.353 (4.12) 0.043 (2.15) 0.416 (2.29) 0.146 (3.08) 2 0.002 (3.03) 0.309 (1.85) 2 0.146 (0.83) 0.329 (2.27) 2 0.161 (0.95) 0.036 (0.68) 0.001 (0.23)

(3)

DV: Political Integration

0.000 (0.00) 2 0.095 (0.80) 0.007 (0.84) 0.058 (0.81) 2 0.006 (0.38) 0.000 (0.36) 2 0.135 (1.79) 2 0.057 (0.78) 2 0.110 (2.08) 0.014 (0.22) 2 0.169 (9.28) 0.004 (2.06)

(4) 2 0.002 (0.34) 0.043 (0.26) 0.032 (3.55) 0.033 (0.36) 0.042 (1.95) 2 0.000 (1.93) 2 0.142 (1.57) 2 0.021 (0.23) 0.059 (0.87) 0.243 (3.22) 2 0.147 (5.39) 2 0.001 (0.30)

(5)

DV: income Economically Economically Unintegrated Integrated

Regression 2

0.029 (2.63) 0.775 (2.46) 0.050 (2.51) 0.248 (1.37) 0.060 (1.36) 2 0.001 (1.61) 0.292 (1.72) 0.209 (1.19) 0.012 (0.09) 0.057 (0.35) 0.012 (0.24) 0.003 (0.56)

DV: Economic Integration (6)

Regression 3

0.004 (1.01) 0.066 (0.64) 0.012 (1.74) 0.059 (0.90) 2 0.005 (0.34) 0.000 (0.26) 2 0.141 (2.14) 0.078 (1.25) 2 0.065 (1.44) 0.082 (1.43) 2 0.163 (10.13) 0.005 (2.68)

(7)

0.007 (0.99) 2 0.192 (0.78) 0.025 (1.48) 2 0.023 (0.13) 2 0.001 (0.01) 2 0.000 (0.02) 2 0.121 (0.98) 2 0.017 (0.14) 0.305 (2.61) 0.243 (2.47) 2 0.201 (5.48) 0.002 (0.52)

(8)

DV: income Socially Socially Unintegrated Integrated

FIML Estimation of Income (log) Using Different Integration Variables

Table 6

0.000 (0.03) 0.330 (0.95) 0.059 (2.54) 0.348 (1.43) 0.134 (1.56) 2 0.001 (1.54) 0.293 (1.57) 2 0.195 (0.91) 2 0.278 (1.62) 0.017 (0.10) 2 0.037 (0.56) 2 0.007 (1.15)

(9)

DV: Social Integration


357

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0.013 (1.35) 2 0.017 (1.56) 0.263 (6.10)

(2)

0.074 (2.44) 2 0.233 (2.60) 0.132 (0.29) 2 0.023 (0.31)

2 0.014 (0.79) 2 0.015 (0.86)

(3)

Rho0: 2 0.63 (se: 0.21) Rho1: 0.85 (se: 0.10) Wald test of independence, Chi square: 14.37 (p 5 0.000)

0.008 (1.21) 2 0.005 (1.04) 0.198 (5.25)

(1)

DV: Political Integration

Regression 2

0.009 (0.80) 2 0.009 (1.11) 0.197 (2.98)

(5)

Rho0: 2 0.75 (se: 0.19) Rho1: 2 0.61 (se: 0.21) Wald test of independence of equations: 9.26 (p 5 0.01)

0.012 (1.65) 2 0.014 (2.01) 0.245 (7.68)

(4)

DV: income Economically Economically Unintegrated Integrated

2 0.040 (1.24) 2 0.006 (0.08) 1.151 (2.89) 0.145 (1.63)

2 0.024 (1.35) 0.008 (0.44)

DV: Economic Integration (6)

Regression 3

0.000 (0.01) 2 0.019 (1.67) 0.163 (2.51)

(8)

2 0.017 (0.37) 2 0.105 (1.01) 2 3.915 (1.81) 0.101 (0.95)

0.062 (3.50) 0.010 (0.57)

(9)

DV: Social Integration

Rho0: 2 0.83 (se: 0.18) Rho1: 2 0.75 (se: 0.38) Wald test of independence of equations: 4. 73 (p 5 0.09)

2 0.003 (0.37) 2 0.013 (2.01) 0.251 (7.42)

(7)

DV: income Socially Socially Unintegrated Integrated

Number of observations: 464. Robust z statistics in parentheses significant at 10%; significant at 5%; significant at 1% Note: Time in Germany was removed from the income equation as the model did not converge when it is included in the regression. Note: Income refers to per adult equivalent (AE) income. Source: Authors’ calculation.

Diagnostic Tests

Parents in Turkey

Spouse in Turkey

Children in Turkey

Local ethnic network Trans-national ethnic network Number of working HH members Siblings in Turk.

Regression 1

DV: income Politically Politically Unintegrated Integrated

Table 6. (Contd)




using SUR and FIML regressions. We measure economic success by the log transformation of per adult equivalent income. Columns 1, 3, 5 and 7 of Table 5 report the findings for the SUR analysis of the determinants of income. It becomes evident that among the three integration indicators only political integration seems to have a positive effect on income. However, the degree of full integration is also an important determinant of income. Among the individual specific variables only years of education and being female are significant, while on the household level both the number of household members and working age adults are significant with the expected signs. Familial networks have a positive impact, transnational networks have a negative impact and local networks have no impact on income. In terms of the remaining variables of interest, we observe that being from Turkish ethnic background has a positive impact on income, while being Alevite has a negative impact. The overall findings provide support for studies revealing a positive effect of education and host country education on income (Chiswick and DebBurman 2004), and a negative impact of being female (Constant and Massey 2005; Bu¨chel and Frick 2005). However, neither being born nor time spent in Germany have any significant impact on income, which is in contrast to international studies such as Duleep and Regets (1997) and Constant and Massey (2005). The difference might stem from our choice of the dependent variable, since studies using income instead of wages find less or no impact of years since migration (Bu¨chel and Frick 2005). Although our results from the SUR estimation are consistent with previous studies, these results might be biased if income and integration are endogenous. The FIML model not only improves the efficiency of the estimators but also yields unbiased coefficients in the presence of endogeneity, given that our exclusion restrictions hold. Table 6 reports the findings that assess the impact of political, social and economic integration on income.8 Columns 1, 4, and 7 in Table 6 report the findings for the unintegrated group and columns 2, 5 and 8 report the findings for the integrated group. In all three regressions, rho0 indicates the correlation between the error term from the income equation of the unintegrated group and the error term from the criterion function, while rho1 shows the correlation between the error term from the income equation of the integrated group and the criterion function. Thus the value and sign of rhos are of special interest as they provide information about the impact of integration on income. Regression 1 of Table 6 shows the results of the FIML analysis of the impact of political integration on income. As seen from the bottom part of Regression 1, 8. We have not included the degree of full integration into our FIML model as it requires a binary selection variable.


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rho0 is negative and significant while rho1 is positive and significant, implying that unobservable characteristics of those migrants who are politically integrated are positively correlated with income (e.g. ability). In other words, an integrated immigrant earns more than a randomly chosen immigrant from the sample. Regarding the impact of other variables on income within politically integrated and unintegrated groups, the table shows that education promotes income in both groups, though the magnitude of this impact is three times higher in the integrated group. Interestingly, having German education yields an income premium only in the latter group. Another interesting finding is that the impact of familial networks is significant only in the unintegrated group, suggesting that they function as a substitute for integration in promoting income. The control variables such as size of household and the number of working household members are significant in both groups with expected signs. The findings for the relationship between income and economic integration are reported in Regression 2 of Table 6. As observed from the rhos in Regression 2, unobservables of both integrated and unintegrated groups are negatively correlated to income, though the unintegrated group is more disadvantaged (larger negative value of rho0). The underlying unobservable factor might be associated with the discrimination of immigrants in the labor market. Another explanation might be found in specific job affiliations with German employers, such as low-skilled and low-paid manual work. Years of education, age, and age squared are significant only in the integrated group with expected signs. Consistent with the findings of the other two integration variables, having familial networks promotes income only for the unintegrated group. In addition to the familial contacts, local networks also have a positive impact on income in the unintegrated group, while transnational networks have a negative impact. In addition, similar to the findings in social integration, the female heads of households earn less in the economically unintegrated group. Finally, Regression 3 in Table 6 presents the findings of the relationship between income and social integration. Rho0 is significant with a negative sign while rho1 is insignificant, suggesting that socially unintegrated migrants earn less than a randomly chosen migrant from the sample while a migrant from the socially integrated group earns about the same. Different from the political integration results, years of education promotes income only for the socially unintegrated group while having German education does not have an impact on either groups’ income. In terms of the impact of networks, having larger familial networks in Germany promotes income only for the socially unintegrated while having transnational networks reduces the income for both groups. Moreover, being a female head of household leads to lower income only in the socially unintegrated group, and there is an income premium for being Turk and Alevite in the integrated group.

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The key findings of the FIML regression analysis can be summarized as follows. Objective integration (i.e. measured using an objective criterion) has a positive impact on income and thus complements findings on subjective integration by Dustmann (1996). Among the three dimensions of integration used in the paper, political integration has the strongest impact on income. In the case of social and economic integration, we find that socially and economically integrated migrants earn more than unintegrated migrants; however, their incomes are not above those of average Turkish migrants. Among the remaining variables, years of education promote income though more so in the integrated group which confirms findings reported in Zimmermann (2007) that the adaptation to the destination country matters for economic success. Age has a positive non-linear impact on income only in economically and politically integrated groups, and thus reinforces the view that standard human capital factors play a stronger role for integrated immigrants. Women have income disadvantages in socially and economically unintegrated groups; the familial network in Germany increases and the transnational network decreases income in all three types of unintegrated groups, while their local ethnic network promotes income for economically unintegrated groups. Being from a Turkish ethnic background leads to higher income in all three forms of integrated groups, while being from Alevite subreligious group leads to lower income in unintegrated groups. Using a quantile regression approach we show in Table 7 that the pay-off from full integration is significant only for households in the higher quintile of unobserved ability. Returns to local ethnic networks, on the other hand, are significant only in the lower end of the income distribution, while returns from family networks are significant in the lower and upper end of the distribution. These findings combined with the fact that local and family networks promote income only in the unintegrated group (as shown in Table 6) provide further support for our view that integration might be costly for lower income households who tend to increase their economic outcome by staying in local networks. In addition, transnational Turkish networks lower the economic success of the households at the upper levels of the ability distribution (Table 7). Table 7 Impact of the Degree of Integration and Networks on Income at Different Income Quantiles Income Quantile 0.2 0.4 0.6 0.8

Integration Index

Local Ethnic Network

Transnational Ethnic Network

Local Family Network

0.048 0.034 0.044 0.033

0.003 0.014 0.006 2 0.002

2 0.007 2 0.006 2 0.009 2 0.008

0.004 0.001 0.001 0.004

significant at 10%; significant at 5%; significant at 1%. Source: Authors’ calculations.


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Taking this outcome together with the results showing that transnational networks reduce income (Table 6) indicates that the preservation of strong transnational ties is accompanied by lower economic effort in Germany. As noted earlier this can be explained by the costs of maintaining the transnational network.

V. CONCLUSION AND POLICY IMPLICATION Our analysis offers some new insights for the debate on the inter-linkages between integration, network scales and economic success of immigrants. First, education turns out to be the key determinant of both integration and economic success. Education raises the chances of becoming integrated into the host country by opening up a wider array of economic and social options and enabling people to efficiently collect and process information. Education may also increase the openness and adaptability to a new surrounding, thus easing and fostering the access of immigrants to further education opportunities, and to social, economic and political participation. Additionally, higher education not only leads to higher returns in the labor market but also increases the mobility of labor and decreases the volatility of future income streams. The prospects of higher and more stable income relax the income constraints on integration. Therefore, long term educational policies targeting the children of immigrants will be beneficial in improving their integration chances and economic success. Second, we find evidence that deeper integration promotes the economic success of Turkish immigrants. However, with regards to the separate impacts of political, social and economic integration, only the political integration measured by holding German citizenship had a strong impact on income levels. When combining all three integration indicators a consistently significant relationship between income and integration can be established. The policy implication from this assessment is that some combination of different forms of integration might be necessary in order to foster economic success. Policies aiming at single dimensions of integration might fail because migrants have to incur costs in several dimensions. The award of citizenship could be seen as an avenue towards integration rather than the reward for successful integration. In the economic sphere, the state can support young immigrants in taking up public sector jobs while fighting statistical discrimination in private sector hiring. Third, local familial networks foster economic success indicating that ethnic niches may be economically advantageous and may partly substitute for missing integration, given that they consistently promote income in unintegrated groups. This result confirms our view that people prefer integration only

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if economic incentives exist. The state should make use of local migrant initiatives to strengthen migrant self-organization that could operate as a gate into the host society. The national integration summits that have been taking place in Germany since 2006 are a useful prerequisite for fostering social interaction; however, policy dialogue alone does not solve integration deficits. Fourth, the integration and network channel of income generation differs across different levels of unobserved ability. While integration helps the betterendowed, the integration premium for less-able immigrants is zero. Local ethnic networks work like insurance schemes for poor immigrants and policies that improve economic incentives for these migrants will foster greater levels of integration. REFERENCES Adsera, Alicia and Barry R. Chiswick (2007). Are there Gender and Country of Origin Differences in Immigrant Labor Market Outcomes across European Destinations?, Journal of Population Economics. 20: 495–526. Alesina, Alberto and Eliana La Ferrara (2005). Ethnic Diversity and Economic Performance, Journal of Economic Literature. 43: 762–800. Berger, Maria, Christian Galonska and Ruud Koopmans (2004). Political Integration by a Detour? Ethnic Communities and Social Capital of Migrants in Berlin, Journal of Ethnic and Migration Studies. 30: 491–507. Bertrand, Marianne, Erzo F.P. Luttmer and Sendhil Mullainathan (2000). Network Effects and Welfare Cultures, Quarterly Journal of Economics. 115: 1019–1055. Blitz, Rudolph C. (1977). A Benefit-Cost Analysis of Foreign Workers in West-Germany, 1957–1973, Kyklos. 30: 479–502. Borjas, George J. (1994). The Economics of Immigration, Journal of Economic Literature. 32: 1667–1717. Borjas, George J. (1995). Ethnicity, Neighborhoods, and Human-Capital Externalities, American Economic Review. 85: 365–390. Borjas, George J. and Lynette Hilton (1996). Immigration and the Welfare State: Immigrant Participation in Means-Tested Entitlement Programs, Quarterly Journal of Economics. 111: 575–604. Borooah, Vani K. and John Mangan (2007). Love Thy Neighbour: How Much Bigotry Is There In Western Countries?, Kyklos. 60: 295–317. Bu¨chel, Felix and Joachim R. Frick (2005). Immigrants’ Economic Performance across Europe – Does Immigration Policy Matter?, Population Research and Policy Review. 24: 175–212. Chiswick, Barry R. (1978). The Effect of Americanization on the Earnings of Foreign-Born Men, Journal of Political Economy. 86: 897–921. Chiswick, Barry R. and Paul W. Miller (1996). Ethnic Networks and Language Proficiency among Immigrants, Journal of Population Economics. 9: 19–35. Chiswick, Barry R., Yinon Cohen and Tzippi Zach (1997). The Labor Market Status of Immigrants: Effects of the Unemployment Rate at Arrival and Duration of Residence, Industrial & Labor Relations Review. 50: 289–303. Chiswick, Barry R. and Noyna DebBurman (2004). Educational Attainment: Analysis by Immigrant Generation, Economics of Education Review. 23: 361–379.


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INTEGRATION, SOCIAL NETWORKS AND ECONOMIC SUCCESS OF IMMIGRANTS Scho¨nwa¨lder, Karen and Janina So¨hn (2007). Siedlungsstrukturen von Migrantengruppen in Deutschland: Schwerpunkte der Ansiedlung und innersta¨dtische Konzentrationen. WZB Discussion Paper SP IV 2007-601. Berlin. Tillie, Jean (2004). Social Capital of Organisations and their Members: Explaining the Political Integration of Immigrants in Amsterdam, Journal of Ethnic and Migration Studies. 30: 529–541. Topa, Giorgio (2001). Social Interactions, Local Spillovers and Unemployment, Review of Economic Studies. 68: 261–295. von Loeffelholz, Hans D. (2001). Kosten der Nichtintegration ausla¨ndischer Zuwanderer. Beihefte der Konjunkturpolitik Heft 52, Migration in Europa: 191–212. Ulku, Hulya (2010). Remitting Behaviour of Turkish Migrants: Evidence from Household Data in Germany, BWPI Working Paper 115. Manchester. Yang, Philip Q. (1994). Explaining Immigrant Naturalization, International Migration Review. 28: 449–477. Zimmermann, Klaus F. (2007). The Economics of Migrant Ethnicity, Journal of Population Economics. 20: 487–494. SUMMARY The observation that some immigrants choose not to integrate into the host society has caused political controversies across European states. This paper hypothesizes that immigrants can exploit social networks of different scales in order to substitute for costly integration. Using a novel dataset of Turkish households in Berlin, which was specifically collected for this analysis, we investigate the determinants of integration as well as the impact of integration and networks on households’ economic success. We find evidence that integration promotes income even after accounting for potential endogeneity bias. Using endogenous switching regression model, we test whether local ethnic networks can be successfully used to generate household income. In line with the view that there is a trade-off between integration and the establishment of ethnic contacts, we find that local ethnic and familial networks increase the income of unintegrated migrants, while transnational networks decrease it. Moreover, education is more income improving for integrated than non-integrated immigrants and remaining closely integrated within their own ethnic group is more economically advantageous for poorer households. These results provide evidence that integration is the rational strategy for better-off immigrants while it may be too costly for poorer immigrants.


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