Integration, Social Networks and Economic Success of Immigrants: A Case Study of the Turkish...

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

migranthouseholds, ofwhommanyare locatedon the lowerpart of the income

distribution inGermany. By reconsidering integration in terms of opportunity

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

342r 2011 Blackwell Publishing Ltd., 9600 Garsington Road,

Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

� Alexander M. Danzer, Department of Economics, University of Munich (LMU) and IZA Bonn.

Mailing address: LMU Munchen, Geschwister-Scholl-Platz 1, 80539 Munchen, Germany. E-mail:

[email protected]. Hulya Ulku, Institute for Development Policy andManage-

ment (IDPM), University of Manchester. Mailing address: IDPM, Arthur Lewis Building, Oxford

Road,ManchesterM13 9PL,UK.E-mail: [email protected]. This paper is part of awider

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, UmaKothari, Sam

Hickey, Tanja Muller, Thankom Arun, Ingrid Tucci, Natalia Weisshaar and the participants of the

DIWResearch Seminar, IZA Summer School andWorld EconomicCongress aswell 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, Rut-

MariaGollan andAlperYenilmez 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 non-

integration 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.

mailto:[email protected]











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 fromTurkeywas initiated in1961but stopped in1973asa consequenceof the economic crisis.

Subsequent immigration continued in the framework of family reunification.

r 2011 Blackwell Publishing Ltd. 343

INTEGRATION, SOCIALNETWORKSANDECONOMICSUCCESSOF IMMIGRANTS

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 integra-

tion (political, social and economic integration) only political integration has a

strong positive impact on economic success. However, the degree of integra-

tion, which is measured as the combination of all three dimensions, has a

consistently positive impact on economic success, suggesting that full integra-

tion is important for promoting income levels. We also find that local ethnic

and familial networks are positively associatedwith economic achievements of

the unintegratedmigrants, whilemaintaining a transnational ethnic network is

negatively correlated. When investigating the effect of integration and net-

worksover thedistributionofunobservedability, it turnsout that integration is

a positive determinant of economic success in upper quantiles only. Economic-

ally 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 (Schonwalder and Sohn 2007) and that data collec-

tion 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 newattention 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,

344 r 2011 Blackwell Publishing Ltd.

ALEXANDERM. DANZER/HULYAULKU

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 paperwe 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 appro-

priate outcome variable. Due to data limitations, most previous publications

have focused on subjective integrationmeasures such as self-assessed assimila-

tion (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 dimen-

sions 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 labormarket 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 sincemigration 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.



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 relationshipbetweenethnic networksand integrationhas receivedmuch

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

networkmembers are better integrated. In a comparable study forAmsterdam,

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

Muchof the literature on the economic successof immigrants is concernedwith

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.



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 labormarket theory argues that less well endowedmigrants 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

ofmigrants suggests thatdiscrimination inaccess to specificoccupations 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 inter-

action. 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 insignif-

icant in determining economic success.More objectivemeasures 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 placedwithin an individual’s utilitymaximization (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 networksmight 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 income-

generating paths, especially if they work predominantly in a segmented labor



market (Piore 1979).6 Following from this, an immigrantwill 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 network-

ing results in the question whether ethnic networks can substitute for integra-

tion, 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.Thusour paperprovides a joint analysis of thedeterminants 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 ANDMETHODOLOGY

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.



head of household and all householdmembers. 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 underwhich

a migrant receives access to political and social rights. A good measure of this

integration is citizenship which grants voting rights unavailable to non-

Germans. In our sample, almost 40 percent of respondents hold German

citizenship (Table 1). Social integration comprises social connections with the

host countryand isproxiedwithavariable counting thenumberof closeGerman

households who were ready to lend money to the respondent if he/she found

him-/herself in serious financial troubles.HavingGerman friends reflects access

and contact to the people; it confirms knowledge of and trust in the natives.

Economic integration is proxied by ‘having aGerman bossorGerman 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 inter-

dependently. 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 incomeafter tax, social security andpension

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 characteristicsof theheadofhousehold,householdcharacteristics,



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 transna-

tional 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 toGermanywhile 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

Table 1

Means and Frequencies of Main Variables for Full Sample

Variable % inTotalSample

Variable Mean Stand.Dev.

German Citizenship 39.66 Income 1856 1033Close German Friends 18.31 Age 41.95 12.22German Boss 33.22 Years of Education 10.87 3.81German Employees 3.73 Time Spent in Germany 25.20 10.52German Education 47.29 Number of Close Turkish Friends in Germany 4.47 7.11Female Head of HH 15.25 Number of Close Turkish Friends in Turkey 1.98 5.46Own House in Germany 9.83 Number of Household Members 3.25 1.62Fixed Assets in Turkey 58.47 Number of Working Household Members 1.16 0.87Born in Germany 16.10 Number of Family Members in Germany 11.52 11.85Married 72.37 Number of Close Family Members in Turkey 2.83 2.75Return Plan 42.71 Children/Spouse in Turkey 0.20 0.88Full Time Employed 35.76 Number of Foreigners in the Family 0.33 0.76Own Business 11.36 Frequency of Visits to Turkeya 10.09 2.31Unemployed 18.64 Integration Indexb 0.98 0.88

aFrequency of visits to Turkey is an index variable taking on values between 0 for no visit and 13 forthe most frequent visit, bIntegration index takes on values between 0 for no integration and 3 for thehighestdegreeof integration, i.e. being integrated inall three typesof integration: social,political, andeconomic. Source: Authors’ calculations.



error terms of the following two equations

lnYi 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 integra-

tion 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 thismethod 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 regres-

sions 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 � 0ð3Þ

The error term ui and the error terms from the two income equations (e11iand 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 ofworking age adults. Once the groupmember-

ship 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- ormore-able immigrants

is to apply a semi-parametric quantile regression model over the error

distribution. We estimate the relationship conditional on the explanatory

variablesQyðYijXiÞ at different quantiles y rather than at the samplemeanas in

OLS,which results in lower sensitivity to outliers (Koenker andHallock 2001).



IV. EMPIRICAL ANALYSIS

This section provides an econometric analysis of the determinants of integra-

tion 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 uncondi-

tional means of Table 2, better-integrated persons are younger, more likely to

be female, unmarried and have lived longer inGermany. 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.

Table3 shows results for the level of integrationandethnicnetworksby income

quintiles. Integration indicators are positively associated with income while local

Table 2

Means and Frequencies of Main Variables by the Degree of Integration

FullyIntegrated

Non-Integrated

FullyIntegrated

Non-Integrated

Mean Mean % %

Household Income 2213 1597 Male 52.9 77.5Per Capita Income 982 634 German Education 85.3 30.7Income Per AdultEquivalent

1194 787 Born in Germany 38.2 7.0

Age 39.4 42.9 Married 61.8 76.0Years of Education 13.9 10.0 Return Plans 14.7 44.0Time Spent in Germany 29.2 22.8 Turk 76.5 83.0Number of Close TurkishFriends in Germany

4.5 4.1 Alevite 32.4 20.5

Number of Close TurkishFriends in Turkey

1.4 1.7

Fully Integrated: If the respondent has all of: German citizenship, close German friends, Germanboss/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

GermanCitizenship

CloseGermanFriends

GermanBoss/

Employee

Close TurkishFriends inGermany

Close TurkishFriends inTurkey

LocalFamilyNetwork

Quantile 1 33.9% 14.8% 30.4% 4.7 2.0 12.2Quantile 2 30.1% 17.1% 35.8% 4.3 1.7 11.0Quantile 3 40.7% 17.9% 39.0% 4.3 1.7 11.7Quantile 4 46.0% 22.1% 40.7% 4.0 2.4 9.4Quantile 5 51.0% 21.6% 39.2% 5.0 2.2 13.2Total 39.7% 18.3% 37.0% 4.5 2.0 11.5

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

integrationmay 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 SURanalysis 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 inGermany 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 associa-

tion with any of the integration variables, while being from Alevite sub-

religious group is positively associatedwith political and social integration and

negatively associated with economic integration. None of the network vari-

ables 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.

Table 4

Integration and Ethnic Networks by Immigrant Generation

GermanCitizenship

CloseGermanFriends

GermanBoss/

Employee

Close TurkishFriends inGermany

Close TurkishFriends inTurkey

LocalFamilyNetwork

First Generation 34.8% 16.4% 36.2% 4.5 2.0 10.6Second Generation 66.7% 30.1% 40.9% 4.1 1.8 16.1

Source: Authors’ calculations.



Table5

SUR

RegressionofIncome(log)UsingDifferentIntegrationVariables

Income

Political

integration

Income

Economic

integration

Income

Social

integration

Income

Integration

Index

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

PoliticalIntegration

0.169

(4.32)��

EconomicIntegration

0.035

(0.90)

SocialIntegration

0.034

(0.69)

Integrationindex

0.095

(4.26)��

Income(log),AE

0.209

0.102

0.028

0.444

(4.26)��

(2.01)��

(0.69)

(5.13)��

Tim

ein

Germany

0.002

0.009

0.003

0.010

0.003

20.004

0.003

0.005

(0.57)

(2.54)��

(1.03)

(2.49)��

(1.17)

(1.28)

(0.99)

(0.73)

Born

inGermany

20.019

0.514

0.060

0.240

0.066

20.008

0.019

0.538

(0.22)

(4.62)��

(0.69)

(2.09)��

(0.77)

(0.09)

(0.22)

(2.73)��

Educationin

Germ.

0.072

0.072

0.082

0.061

0.082

0.069

0.056

0.281

(1.40)

(1.16)

(1.59)

(0.96)

(1.59)

(1.39)

(1.08)

(2.58)��

Yrsofeducation

0.020

0.010

0.022

0.013

0.022

0.010

0.019

0.024

(3.53)��

(1.54)

(3.92)��

(1.82)�

(3.94)��

(1.85)�

(3.41)��

(1.97)��

Age

0.010

0.037

0.016

0.019

0.016

0.034

0.007

0.093

(0.85)

(2.75)��

(1.38)

(1.36)

(1.34)

(3.15)��

(0.62)

(3.93)��

Age

squared

20.000

20.000

20.000

20.000

20.000

20.000

20.000

20.001

(0.91)

(2.89)��

(1.46)

(1.68)�

(1.44)

(2.96)��

(0.76)

(3.65)��

Fem

ale

20.111

0.127

20.096

0.109

20.095

0.090

20.123

0.365

(2.23)��

(2.19)��

(1.92)�

(1.83)�

(1.90)�

(1.93)�

(2.46)��

(3.57)��

Married

0.008

20.046

20.003

0.068

0.003

20.064

0.005

20.052

(0.15)

(0.77)

(0.05)

(1.09)

(0.06)

(1.31)

(0.10)

(0.48)

Alevite

20.085

0.131

20.065

20.002

20.063

20.064

20.069

0.079

(2.03)��

(2.70)��

(1.54)

(0.04)

(1.50)

(1.62)

(1.67)�

(0.92)



Turk

0.119

20.074

0.110

0.003

0.111

20.004

0.114

20.104

(2.62)��

(1.35)

(2.42)��

(0.05)

(2.44)��

(0.09)

(2.52)��

(1.08)

Household

size

20.170

0.042

20.168

0.011

20.168

20.002

20.170

0.087

(12.06)��

(2.34)��

(11.87)��

(0.58)

(11.90)��

(0.13)

(12.02)��

(2.75)��

Number

ofworkingHH

mem

bers

0.230

0.236

0.237

0.224

(10.03)��

(10.12)��

(10.23)��

(9.68)��

Localfamilynetwork

0.003

20.001

0.003

0.001

0.003

20.002

0.003

20.000

(1.75)�

(0.31)

(1.73)�

(0.41)

(1.79)�

(1.38)

(1.69)�

(0.02)

Localethnicnetwork

0.008

20.005

0.008

20.011

0.007

0.015

0.007

0.001

(1.49)

(0.80)

(1.43)

(1.68)�

(1.26)

(2.81)��

(1.30)

(0.10)

Trans-nationalethnicnetwork

20.009

20.003

20.010

0.006

20.010

0.004

20.009

20.001

(1.68)�

(0.55)

(1.87)�

(0.87)

(1.86)�

(0.73)

(1.77)�

(0.09)

Siblings

inTurkey

0.014

20.014

20.007

20.001

(1.21)

(1.12)

(0.74)

(0.04)

Childrenin

Turkey

20.028

20.004

20.009

20.044

(1.09)

(0.15)

(0.42)

(0.95)

Spouse

inTurkey

0.046

0.306

20.113

0.200

(0.26)

(1.69)�

(0.79)

(0.64)

Parentsin

Turkey

20.007

0.029

20.010

0.048

(0.21)

(0.88)

(0.39)

(0.85)

Observations

464

464

464

464

464

464

464

464

Absolute

valueofzstatistics

inparentheses� significantat10%;��

significantat5%;�� significantat1%.Note:incomerefers

toper

adultequivalent(A

E)

income.Source:Authors’calculations.

Table5.(Contd)

Income

Political

integration

Income

Economic

integration

Income

Social

integration

Income

Integration

Index

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)



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, timespent inGermany,beingborn inGermany, andbeinga femalehead

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 net-

works 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 house-

hold, 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 non-

linear 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 formsof integration are independent ofmarital status and

being fromaparticularTurkish ethnic group,while only political integration is

positively related to being fromAlevite 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



Table6

FIM

LEstim

ationofIncome(log)UsingDifferentIntegrationVariables

Regression1

Regression2

Regression3

DV:income

DV:income

DV:income

Politically

Unintegrated

Politically

Integrated

DV:Political

Integration

Economically

Unintegrated

Economically

Integrated

DV:

Economic

Integration

Socially

Unintegrated

Socially

Integrated

DV:Social

Integration

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Tim

ein

Germany

0.022

0.000

20.002

0.029

0.004

0.007

0.000

(2.26)��

(0.00)

(0.34)

(2.63)��

(1.01)

(0.99)

(0.03)

Born

inGermany

20.119

0.177

1.353

20.095

0.043

0.775

0.066

20.192

0.330

(1.22)

(1.47)

(4.12)��

(0.80)

(0.26)

(2.46)��

(0.64)

(0.78)

(0.95)

Yrs

ofeducation

0.014

0.043

0.043

0.007

0.032

0.050

0.012

0.025

0.059

(2.02)��

(4.11)��

(2.15)��

(0.84)

(3.55)��

(2.51)��

(1.74)�

(1.48)

(2.54)��

Germaneducation

0.029

0.196

0.416

0.058

0.033

0.248

0.059

20.023

0.348

(0.46)

(2.05)��

(2.29)��

(0.81)

(0.36)

(1.37)

(0.90)

(0.13)

(1.43)

Age

0.001

0.073

0.146

20.006

0.042

0.060

20.005

20.001

0.134

(0.05)

(1.78)�

(3.08)��

(0.38)

(1.95)�

(1.36)

(0.34)

(0.01)

(1.56)

Agesquared

20.000

20.001

20.002

0.000

20.000

20.001

0.000

20.000

20.001

(0.11)

(1.64)

(3.03)��

(0.36)

(1.93)�

(1.61)

(0.26)

(0.02)

(1.54)

Fem

ale

20.098

20.026

0.309

20.135

20.142

0.292

20.141

20.121

0.293

(1.37)

(0.28)

(1.85)�

(1.79)�

(1.57)

(1.72)�

(2.14)��

(0.98)

(1.57)

Married

0.070

20.076

20.146

20.057

20.021

0.209

0.078

20.017

20.195

(1.14)

(0.79)

(0.83)

(0.78)

(0.23)

(1.19)

(1.25)

(0.14)

(0.91)

Alevite

20.140

0.127

0.329

20.110

0.059

0.012

20.065

0.305

20.278

(2.69)��

(1.52)

(2.27)��

(2.08)��

(0.87)

(0.09)

(1.44)

(2.61)��

(1.62)

Turk

0.044

0.268

20.161

0.014

0.243

0.057

0.082

0.243

0.017

(0.66)

(3.02)��

(0.95)

(0.22)

(3.22)��

(0.35)

(1.43)

(2.47)��

(0.10)

Household

size

20.180

20.157

0.036

20.169

20.147

0.012

20.163

20.201

20.037

(9.82)��

(5.92)��

(0.68)

(9.28)��

(5.39)��

(0.24)

(10.13)��

(5.48)��

(0.56)

Localfamily

network

0.003

0.003

0.001

0.004

20.001

0.003

0.005

0.002

20.007

(1.67)�

(0.75)

(0.23)

(2.06)��

(0.30)

(0.56)

(2.68)��

(0.52)

(1.15)



Table6.(Contd)

Regression1

Regression2

Regression3

DV:income

DV:income

DV:income

Politically

Unintegrated

Politically

Integrated

DV:Political

Integration

Economically

Unintegrated

Economically

Integrated

DV:

Economic

Integration

Socially

Unintegrated

Socially

Integrated

DV:Social

Integration

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Localethnic

network

0.008

0.013

20.014

0.012

0.009

20.024

20.003

0.000

0.062

(1.21)

(1.35)

(0.79)

(1.65)�

(0.80)

(1.35)

(0.37)

(0.01)

(3.50)��

Trans-national

ethnicnetwork

20.005

20.017

20.015

20.014

20.009

0.008

20.013

20.019

0.010

(1.04)

(1.56)

(0.86)

(2.01)��

(1.11)

(0.44)

(2.01)��

(1.67)�

(0.57)

Number

ofworking

HH

mem

bers

0.198

0.263

0.245

0.197

0.251

0.163

(5.25)��

(6.10)��

(7.68)��

(2.98)��

(7.42)��

(2.51)��

Siblingsin

Turk.

0.074

20.040

20.017

(2.44)��

(1.24)

(0.37)

Childrenin

Turkey

20.233

20.006

20.105

(2.60)��

(0.08)

(1.01)

Spouse

inTurkey

0.132

1.151

23.915

(0.29)

(2.89)��

(1.81)�

Parentsin

Turkey

20.023

0.145

0.101

(0.31)

(1.63)

(0.95)

DiagnosticTests

Rho0:

20.63��

(se:0.21)

Rho0:

20.75��(se:0.19)

Rho0:

20.83��

(se:0.18)

Rho1:0.85��(se:0.10)

Rho1:

20.61��(se:0.21)

Rho1:

20.75(se:0.38)

Waldtestofindependence,Chisquare:

14.37(p

50.000)

Waldtestofindependence

ofequations:

9.26(p

50.01)

Wald

testofindependence

ofequations:

4.73(p

50.09)

Number

ofobservations:464.Robustzstatisticsin

parentheses� significantat10%

;��

significantat5%;�� significantat1%

Note:T

imeinGermanywasremoved

fromtheincomeequationasthemodeldidnotconvergewhen

itisincluded

intheregression.N

ote:Incomerefersto

peradult

equivalent(A

E)income.Source:Authors’calculation.



using SUR and FIML regressions. We measure economic success by the log

transformationofper adult equivalent income.Columns1, 3, 5 and7ofTable5

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

backgroundhas apositive impacton income,while beingAlevitehas anegative

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;

Buchel 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 (Buchel and Frick 2005).

Although our results from the SUR estimation are consistent with previous

studies, these resultsmight bebiased if incomeand integrationare 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 restrictionshold.Table6 reports thefindings that assess the impactof

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.

Regression1ofTable6 shows the resultsof theFIMLanalysisof the impactof

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.



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 earnsmore than a randomly chosen immigrant from

the sample. Regarding the impact of other variables on income within

politically integrated andunintegrated groups, the table shows that education

promotes income in both groups, though themagnitude of this impact is three

times higher in the integrated group. Interestingly, havingGerman 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 integra-

tion inpromoting 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 inRegression2ofTable 6.Asobserved fromthe rhos inRegression

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

explanationmight be found in specific job affiliations withGerman 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 chosenmigrant from the samplewhile amigrant 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 unin-

tegrated 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.



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 Zimmer-

mann (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 sub-

religious 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

successof thehouseholdsat theupper levelsof theabilitydistribution (Table 7).

Table 7

Impact of the Degree of Integration and Networks on Income at Different Income Quantiles

IncomeQuantile

IntegrationIndex

Local EthnicNetwork

TransnationalEthnic Network

Local FamilyNetwork

0.2 0.048 0.003 2 0.007 0.004�

0.4 0.034 0.014�� 2 0.006 0.0010.6 0.044�� 0.006 2 0.009� 0.0010.8 0.033 2 0.002 2 0.008 0.004�

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



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 ofmaintaining 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 chancesofbecoming 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

measuredbyholdingGerman citizenshiphada strong impacton income levels.

When combining all three integration indicators a consistently significant

relationship between income and integration can be established. The policy

implication fromthis assessment is that somecombinationofdifferent formsof

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 uninte-

grated groups. This result confirmsour view that people prefer integrationonly



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 better-

endowed, 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 thesemigrants will foster greater levels of

integration.

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SUMMARY

The observation that some immigrants choose not to integrate into the host society has caused political

controversies acrossEuropeanstates.Thispaperhypothesizes that immigrants canexploit socialnetworks

of different scales in order to substitute for costly integration.Using a novel dataset ofTurkish households

inBerlin,whichwas specifically collected for this analysis,we investigate thedeterminants of integrationas

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 linewith the view that there is a trade-offbetween 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 thannon-integrated immigrantsand remainingclosely integratedwithin theirownethnic group

ismore economicallyadvantageous for poorerhouseholds.These resultsprovide evidence that integration

is the rational strategy for better-off immigrants while it may be too costly for poorer immigrants.



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