Migrant Networks and Labor Market Integration of Immigrants from the Former Soviet Union in Germany

Introduction

It is widely accepted among immigration scholars that social networks play an important part in understanding the labor market integration of recently arrived immigrants and that a human-capital-based approach alone does not suffice to explain their labor market outcomes. In particular, it is commonly argued that migrant networks, that is, the existence of social ties to co-ethnic immigrants already living in the host society, not only drive migration (Kalter 2011; Massey and Espinosa 1997), but are also helpful in the aftermath of immigration. They provide support and a means to overcome common obstacles to gain access to adequate employment in the new environment. Meanwhile, a considerable number of empirical studies, most of which deal with the context of immigration to the United States, provide evidence for positive effects of migrant networks on the labor market success of immigrants (e.g., Aguilera 2002, 2003, 2005; Aguilera and Massey 2003; Greenwell, Burciaga, and DaVanzo 1997).

However, this basically optimistic view of the role of migrant networks has also been challenged. It has long been questioned whether migrant networks can really provide the “right” sort of help and resources needed to succeed in the host society or whether they rather turn out to be traps in the middle or the long run (e.g., Portes and Sensenbrenner 1993; Wiley 1970). Recent studies (Elliott 2001; Green, Tigges, and Diaz 1999; Kazemipur 2006), again pertaining to the United States or Canada, also support this basically skeptical view.

In this paper, we analyze the impact of migrant networks on the labor market integration of new immigrants in the context of recent immigrants from the former Soviet Union (FSU) to Germany. During the 1990s and the early 2000s, two distinct groups, Ethnic Germans (“Aussiedler”) and so-called Jewish Quota Refugees, migrated in substantial numbers from more or less the same regions in the FSU to Germany. Studying these groups and this specific migration context seems a worthwhile endeavor for at least three reasons.

First, while reexamining empirical effects in different contexts is useful per se, it is especially important when exploring the labor market effects of migrant networks. As indicated above, research shows that the impact of co-ethnic ties varies between different receiving contexts, between different immigrant groups, and for different labor market outcomes. Our analyses aim at contributing to identifying the conditions under which migrant networks might or might not be helpful and to integrating seemingly conflicting theoretical arguments. They do so not only by adding a recent European migration context to the research literature and by systematically comparing the two groups involved, but also by differentiating between different types of jobs new migrants might strive for; it will turn out that all this is important in order to understand the role of migrant networks more precisely.

Second, examining migration from the FSU to Germany seems a particularly promising test case, both for theoretical and for methodological reasons. On the theoretical side, recent FSU immigrants arrived with relatively high levels of formal qualifications. Moreover, they are awarded a special legal status, which, at least in theory, should facilitate their smoother labor market allocation. Nevertheless, FSU immigrants still face clear labor market disadvantages (Kogan 2011); studying the role of migrant networks thus could be especially telling. On the methodological side, migration from the FSU to Germany turns out to be a convenient and strategic case because it downsizes a typical pitfall: migrants are often simultaneously selected on the basis of both social and human capital, and this is likely to bias the effect of migrant networks on labor market integration. Migration from the FSU to Germany, however, was not an essentially labor-motivated type of migration, and for the subgroup of Ethnic Germans, it included nearly the whole population at risk. Thus, by studying FSU immigrants, we can considerably reduce the general problem that the effect of migrant networks might be confounded by the selectivity on unobserved labor-market-relevant characteristics.

Third, in addition to the abovementioned selection problem, when looking at migrant networks specifically, research on labor market effects of social networks in general involves the well-known issue of endogeneity (Mouw 2002, 2003, 2006). In our study, we can rely on data that downsize many usual limitations and thus provide a relatively strong test: retrospectively collected data on a Germany-wide representative sample of FSU immigrants allow us to study the impact of pre-migration ties in a longitudinal perspective, thus tackling the problem of reverse causality; we are also able to control for a rich repertoire of human-capital-related and other variables to alleviate the issue of unobserved characteristics potentially influencing both networks and labor market success.

We start with a short review of the main theoretical arguments, the available empirical evidence, and the methodological issues involved. We then describe the German setting, focusing in particular on the two FSU immigrant groups to be studied. We continue by describing in more detail our data, methods, and variables and then report our major empirical findings. As briefly summarized in the discussion, our analyses show that for the immigrants from the FSU in Germany the effect of migrant networks depends heavily on the kind of jobs they target, and it is differently pronounced for different groups.

The Role of Migrant Networks: Theory, Past Research, and Methodological Pitfalls

Social networks are known to play an important part when it comes to matching employees to jobs. Since Granovetter's seminal work (1973, 1974), a large body of research has addressed the role of social contacts in labor market success (Burt 2001; Lin 1999, 2001; Lin, Cook, and Burt 2001; Portes 1998). The use of informal search methods is viewed as efficient for both employers and job seekers (Erickson 2001; Ioannides and Loury 2004; Marsden 2001), the major mechanisms being influence and information (Lin, Ensel, and Vaughn 1981; Yakubovich 2005). For employers, referrals by third parties reduce the uncertainty of the screening process related to the value of potential employees' skills and credentials. For job seekers, the use of social resources provides a means of accessing information on job openings and increases the efficiency of job searches (Flap and Boxman 2001; Montgomery 1992). Thus, for both types of actors in the search and matching process, social networks might reduce transaction costs and increase efficiency (Burt 1992; Granovetter 1974; Waldinger 1996).

When asking about the specific role of migrant networks in labor market integration, in the literature we find largely an optimistic view that follows this very general line of argument. All the advantages associated with social networks are more or less straightforwardly transferred to the case of immigrants. It is even argued that the marginal utility of using social ties might be higher in their case, due to the fact that fewer alternatives are at hand. Particularly among recent newcomers, ethnic community infrastructure and inter-ethnic connections might offer immigrants a shelter in the initial period of their adaptation to the host society and provide security, great solidarity, and labor market opportunities within the ethnic economy (Portes 1995; Sanders and Nee 1996; Sanders, Nee, and Sernau 2002; Waldinger 1994, 2005; Zhou 1992).

In the meantime, much evidence supports this basically optimistic view. Studies using data from the Mexican Migration Project show that ties to co-ethnics improved the employment chances (Aguilera 2002), the job quality (Aguilera and Massey 2003), the wages (Aguilera and Massey 2003; Massey and Espinosa 1997), and the job stability (Aguilera 2003) of Mexican immigrants in the United States. There are similar findings on the wages of Salvadorian and Filipino immigrants in Los Angeles (Greenwell, Burciaga, and DaVanzo 1997), or on the earnings of Puerto Rican immigrants in the United States (Aguilera 2005).

However, there is also a more skeptical view of this matter that challenges the general reasoning above. Most importantly, it is questionable whether migrant networks really do provide the right sort of social ties. It is often counter-argued that sticking to ethnic bonds might impede the building of more helpful ties to the host society, and might thus lead to lower-quality employment opportunities, often provided within ethnic communities (Portes and Sensenbrenner 1993). As a result, one speaks of “entrapment” resulting from ethnic ties (Bonacich 1973; Portes 1998; Portes and Rumbaut 2001; Portes and Sensenbrenner 1993; Wiley 1970).

These kinds of arguments thus would give reason to expect negative effects of the existence of ethnic ties on the labor market success of immigrants, and indeed there is also some evidence of such disadvantages. It has been shown that ethnic ties led to lower-paying jobs for Hispanics and Blacks in the United States (Green, Tigges, and Diaz 1999), to more ethnically homogeneous jobs for Blacks in US cities (Elliott 2001), and to lower wages of immigrants in Canada (Kazemipur 2006).

Given the mixed empirical evidence, the obvious task is to integrate these seemingly conflicting standpoints and to specify conditions under which the optimistic or the pessimistic view might be more likely to hold. The broader literature on network effects in the labor market has identified general social capital theory to be a fruitful framework that allows such a more differentiated perspective. Accordingly, it is necessary to emphasize that it is not contacts per se that matter, but the resources they contain (Lin 1999, 2001; Mouw 2003).

This general perspective has also been explicitly, and very fruitfully, applied to the specific case of immigrants (Aguilera and Massey 2003; Portes 1995, 1998). Two implications seem particularly important to understand the contingency of migrant network effects: it has been argued that the resources available via migrant networks might be very specific and limited, providing information and influence predominantly for jobs that are already common among the members of the ethnic group (Portes and Rumbaut 2001, 48). In many empirical cases, these jobs might be on the lower end of the occupational ladder. In the extreme, this might lead to the abovementioned “entrapment”; that is, there is a danger that even newcomers with greater human capital will end up in low positions when using migrant networks as channels. In any case, the argument shows that the effect of migrant networks might be contingent on the type of target jobs, on the one hand, and on the specific immigrant group and receiving context on the other hand. Second, it has been stressed that the utility of the resources contained in networks must be seen relative to alternative means, especially more formal ways, of getting access to the labor market. For example, the relative utility of network resources is assumed to be higher for undocumented migrants than for those who are documented (Aguilera and Massey 2003, 676). Likewise, the relative utility might also be very different for different groups, different kinds of jobs, and different receiving contexts. So in sum, for every case of application, the crucial theoretical question to answer is whether, within the institutional settings of a given receiving country, ethnic ties of members of a given group provide a relatively high quantity and/or quality of particular resources that allow—via the basic mechanisms of influence and/or information—relatively better access to given types of jobs.

Besides the theoretical tasks, detecting the effects of migrant networks poses considerable methodological challenges, questioning much of the available empirical evidence. One major issue is, again, not specific for migrants but holds for social capital effects in the labor market in general: it is the statistical problem of endogeneity, particularly caused by the substantive phenomenon of homophily (Mouw 2006). Friends or acquaintances tend to be similar with respect to many characteristics, some of which might be important in the labor market; omitted variables might thus bias the effect of networks. The effect could also be biased by reverse causality, reflecting the fact that friends or acquaintances might be selected on the dependent variable (rather than influencing it) or that the usage of existing contacts is selectively triggered by labor market outcomes. When trying to control more properly for these issues by means of adequate longitudinal designs, the evidence for causal effects of social capital is often only very weak (Mouw 2002, 2003).

In the case of migrants, the interpretation of empirical effects is further complicated by the fact that their networks are likely to affect migration itself (Kalter 2011; Massey and Espinosa 1997), and there might thus be a selection bias in the sample. More precisely, if both social capital and expected labor market chances in the host country are causes of migration, this induces a (negative) correlation between these two variables within the subgroups of migrants, even if they are independent from each other in the total home-country population.¹ As actual labor market chances in the host country after migration will be correlated to expected labor chances before migration, this will bias the effect of migrant networks when common causes of expected and actual labor market chances stay unobserved (Pearl 2000, 17; Morgan and Winship 2007, 67–71).² Both migrant networks and labor market chances are certainly major driving forces behind many migration phenomena all over the world. As a consequence, much empirical evidence for the effect of migrant networks on labor market integration provided in the literature is likely to suffer from these methodological issues.

Fortunately, however, recent migration from the former Soviet Union to Germany provides a specific case where these problems are much less pronounced: most importantly, we are not dealing with labor migration, but with migration that was driven mainly by legal privileges as well as by political and more general economic aspects; thus, the artificial correlation between migrant networks and unobserved aspects of labor chances is largely avoided.³ For one of the two subgroups of migrants involved, namely Ethnic Germans, we can hardly expect any substantive selection bias at all, as over the years almost all the eligible subpopulation in the former Soviet Union has migrated to Germany. These peculiarities will be elaborated in more detail in the next section. That section will also derive some basic expectations about how the effect of migrant networks might differ for the two groups and for different types of jobs in the German labor market.

Immigrants from the Former Soviet Union in Germany

During the 1990s and the early years of the new century, Germany experienced massive immigration from the successor states of the former Soviet Union. The migration movement was comprised of two different groups: the vast majority of immigrants, totaling more than two million, arrived as Ethnic Germans (“Aussiedler”). These are people who are defined as being of German ancestry according to criteria specified in the German Federal Law Concerning Displaced Persons (“Bundesvertriebenengesetz”), and they are regarded as Germans in the sense of the German constitutional law. Since Germany is a single country that has been unconditionally accepting FSU Ethnic Germans and their immediate (non-German) relatives, almost all individuals able to claim the respective status and willing to do so have left the territory of the former Soviet Union. Only a small minority, a largely assimilated subpopulation with high intermarriage rates, remained in Russia (Sticker 1997). Upon arrival in Germany, Ethnic Germans were awarded a special settlement status, allowing them easy access to German citizenship and other privileges, such as recognition of their educational qualifications from the FSU, or extensive retraining opportunities. It is also worth mentioning that Ethnic Germans used to benefit from special integration programs providing information and guidance about the functioning of the German labor market, and the German educational and health care systems, as well as counseling and assistance with integration problems.

In addition to Ethnic Germans, more than 200,000 Jews and their family members have migrated from the former Soviet Union to Germany since 1991. Although the numbers of FSU Jewish immigrants who settled in Germany seem to be relatively small, similarly to Ethnic Germans, the vast majority of Jews residing within the territory of the former Soviet Union have emigrated in the past two to three decades, the bulk of whom headed to Israel (Haberfeld et al. 2011). The fact that only a small fraction of the over 1.5 million Jews who left the FSU since the end of the 1980s settled in Germany can be explained—apart from any historical and culturally related interpretations—by a fixed yearly contingent of Jewish immigrants who could be accepted, hence the title “Jewish Quota Refugees” (Cohen and Kogan 2005, 2007). Due to their special status, Jewish Quota Refugees also enjoyed the right of permanent settlement and an extensive integration support, far beyond that to which other groups of immigrants in Germany are entitled (e.g., unrestricted labor market access or social assistance). Until recently, however, the privileges enjoyed by Jewish immigrants were less comprehensive than those to which Ethnic Germans were entitled.

Aside from permanent settlement intentions and formal privileges accorded to them due to their respective status, both Ethnic Germans and Jewish Quota Refugees arrived with relatively high levels of qualifications (Kogan 2011). This is important to note, as this clearly distinguishes these recent immigrants from the FSU from earlier immigrant groups in Germany, such as the labor migrants arriving predominantly in the 1970s and 1980s. Migration research has repeatedly shown that these classical migrant groups in Germany are considerably overrepresented among the unemployed and are channeled into the lower strata of the occupational hierarchy. It has also been shown that their difficulties to integrate satisfactorily into the labor market can largely be explainfed by negative selection of human capital (Granato and Kalter 2001; Kalter and Granato 2002, 2007; Kogan 2004, 2007).

Nevertheless, recent studies suggest that despite their privileges and equipment with human capital, Ethnic Germans and Jewish Quota Refugees also faced considerable problems upon entering the German labor market (Cohen and Kogan 2007; Kogan 2011). This makes it especially interesting to look at the precise role that migrant networks might play in their case. What then would one expect following the general arguments in part 2?

First, one would assume that the role of ethnic social capital differs for different segments of the labor market. While immigrant networks are a useful source of information and should speed up entry into employment, they do not necessarily guarantee favorable jobs. Since Germany's immigrant population is known for its overrepresentation on the lower tier of the occupational hierarchy, any advice and assistance on the part of close friends and relatives might not indubitably lead to newcomers' better labor market allocation. As a result, we might find newer immigrants with more extensive immigrant networks being channeled into the lower segments of the German labor market, whereby these networks might be less helpful for their entry into more prestigious employment.

Second, one could expect a difference between groups. Taking into account that out of the two groups of FSU immigrants, Jewish immigrants are not entitled to integration programs similarly extensive to those provided to Ethnic Germans, nor equipped with the same information on the functioning of the German labor market, we should expect a more pronounced effect of network resources on their labor market outcomes than on those of Ethnic Germans.

Data, Methods, and Variables

We use data from the recently completed project “Labor Market Integration: Aussiedler and Jewish Immigrants from the Former Soviet Union in Germany and Israel,” funded by the German-Israeli Foundation. In the current paper, we rely on the German part of the data, which were collected by means of a telephone survey in May and June 2007. The target population was immigrants from the former Soviet Union who were 25–54 years old, and who had arrived in Germany at age 18 or older between the years 1994 and 2005. We aimed for disproportionate stratified sampling of the two groups involved, that is, Ethnic Germans and Jewish Quota Refugees. A Germany-wide telephone register provided the sampling frame, whereby the names of the potential respondents were preselected based on an onomastic procedure (Humpert und Schneiderheinze 2000). According to this procedure, the probability that a certain combination of first and last names will pertain to a specific ethnic background is calculated based on the computerized dictionaries of names and pertinent regional codes. After the sample of potential immigrants from the former Soviet Union was created, we screened it to establish whether the selected individuals indeed belonged to the target population. Of the original sample of 12,500 individuals who were subjected to the onomastic procedure, 6,413, or about half, participated in the screening.⁴ Of these individuals, 40 percent satisfied all criteria to participate in the study (i.e., age, age at migration, and years of migration; see definition of the target population above). Respondents were offered the option of being interviewed by a native Russian-language speaker, which was preferred by the vast majority of those interviewed. The interview length was between 20 and 30 minutes. Overall, 658 interviews with Jewish Quota Refugees (JQR) and 892 interviews with Ethnic Germans (EG) are available for the current analysis, which represents 60 percent of the identified target population.⁵ Note that the survey in general offers very unique data on these two important recent immigrant groups in Germany, as they cannot usually be perfectly identified in other data sets or, if so, cannot be found in convenient numbers. For more details on the sampling procedure, representativeness of the data, and methodological issues of the telephone survey, see Liebau (2011).

In our core analysis, we apply discrete event history modeling to study the entry into the first job after immigration to Germany, the time unit being years.⁶ Of 1,545 respondents with valid information on both the year of immigration and, if applicable, the year of entry into the first job, 7 percent had already entered employment in the year of immigration, and 28 percent in the year afterward (see also figure 1 below). After 10 years, 89 percent of all FSU immigrants in our sample had been employed (at least once) in Germany. In competing-risks models, we further differentiate the status of this employment: higher-status employment encompassing professional, managerial, or technical occupations, referred to as PTM (ISCO-88 one-digit codes: 1, 2, and 3), or lower-status jobs, referred to as non-PTM (all other ISCO-88 one-digit codes).

Our central independent variable is a measure of how many friends or relatives the FSU immigrants already had in Germany prior to their immigration. Due to the close-tie nature of the contacts and the history of the FSU migration to Germany, it is reasonable to assume that the majority of the respondents' friends and relatives stemmed from the former Soviet Union. As the variable pertains to the existence of ties prior to migration, we avoid the common problem of reverse causality when including it in the analysis of the time-dependent risk of entering the first job in Germany after immigration.

The upper part of table 1 shows that migrant networks are quite prevalent among the immigrants from the FSU who came to Germany between 1994 and 2005. In total, about 40 percent of all FSU immigrants knew at least four people in Germany prior to migration, whereas less than a third did not know anyone at all. Among the group of Ethnic Germans, 52.3 percent answered “a lot (4 and more),” and another 26.3 percent “a few (1–3).” Among the Jewish Quota Refugees, the percentages are a bit lower, albeit also sizeable. Here, 25.5 percent of the respondents responded “a lot,” and 35.3 percent “a few.”⁷ Thus, 78.6 percent of all Ethnic Germans and 60.8 percent of JQRs knew at least somebody in Germany before they migrated.

Also included in our multivariate analyses is a set of control variables. First, we differentiate between two immigrant groups: Ethnic Germans and Jewish immigrants. We also control for age at arrival and for “years since migration” (YSM), that is, the time that elapsed between immigration and the entry into the first job. Persons who at the moment of interview had never found first employment are right censored; that is, for them, YSM extends from the moment of migration to the moment of interview. To account for possible nonlinearities in the YSM effect, we include YSM squared in the model. Immigrants' human capital is captured by their education and occupational status (measured against the ISEI scale) back in the home countries. Host-country-specific cultural capital is proxied by the subjectively assessed level of proficiency in the German language at the time of migration, measured on a scale from 0 (not at all) to 4 (very good). Finally, we control for gender and marital status at migration, as well as for personality traits measured by the so-called “Big Five” (Dehne and Schupp 2007; Lang and Lüdtke 2005). This encompasses the items pertaining to openness, conscientiousness, extroversion, agreeableness, and neuroticism. Unfortunately, these variables are measured not at the moment of migration, but at the time of interview; however, their omission does not change our results significantly.

Table 1 also allows us to learn about the composition of immigrants with more or less extensive networks in Germany as opposed to that of individuals with fewer or without any contacts in Germany prior to migration. There are hardly any differences with regard to age at arrival and marital status upon migration, though women seemed to have a more extensive network of contacts prior to migration than men did. Our results further show little variation across various levels of education with regard to the contact accessibility. If anything, the correlation is negative: better-educated immigrants were less likely to possess an extensive network of friends and relatives in Germany. Immigrants with less prestigious occupations back in their home countries were those with more contacts in Germany; they were also more proficient in the German language. It is important to note that the major differences in the abovementioned characteristics of immigrants with various contact intensity are related to their legal status. Ethnic Germans, who possessed more extensive pre-migration networks in Germany, were somewhat less educated than Jewish immigrants; they used to occupy jobs of lower socio-economic status prior to migration, but on average had a somewhat better command of the German language. Hence, immigrant legal status upon arrival appears to be one of the central mediators for the compositional differences among migrants with regard to their respective access to social resources in Germany.

Findings

The Importance of Migrant Networks: A Standard Perspective

Before we turn to our core discrete event history analysis, it is worth taking a simpler, frequently used perspective to get a first impression of the importance of migrant networks for the process of labor market integration among FSU immigrants in Germany. This can be gained by exploring the standard question about how the respondents found their current job. Table 2 shows a list of channels that were mentioned as answer categories in our survey and the proportions of immigrants that checked each of these categories.

The results in table 2 suggest that migrant networks are indeed very important for the integration of FSU immigrants into the German labor market. As many as 14.3 percent of the employed Ethnic Germans and 9 percent of the employed Jewish immigrants found their jobs via relatives and close friends, so-called strong ties. In the vast majority of the cases (EG: 92.2 percent; JQR: 87.2 percent), these relatives or close friends also stemmed from the FSU. Even more immigrants (EG: 29.1 percent; JQR: 23.2 percent) found their jobs via acquaintances (weak ties), and again, these were predominantly (78.8 percent, resp. 65.0 percent) co-ethnic ties. It is quite telling to compare these figures to the data from the German Socio-Economic Panel (GSOEP), which includes a similar battery but without discriminating between strong and weak ties. Here, in the year 2007, one finds for a reference group of native Germans of the same age that 28.2 percent found their current jobs via referrals by others (own separate analyses, not shown in table 2).Networking apparently plays a more important role in the immigrant job search, as the respective value stands at 43.3 percent among Ethnic Germans and at 32.2 percent among JQR.

These descriptive results support the general findings from many other studies (see especially Drever and Hoffmeister [2008]). With regard to getting jobs, social ties seem more important for FSU immigrants than for native Germans, and in the vast majority of cases these ties are co-ethnic. However, relying solely on the information about how the current job was found does not tell us whether or not individuals who have not found any employment also relied on co-ethnic ties. Moreover, the causality remains unresolved once we approach the issue solely from a cross-sectional perspective. An answer to the question of whether networking leads to a quicker entry into employment, and particularly into “good” jobs, calls for a longitudinal perspective.

The Impact of Social Ties in a Longitudinal Perspective

In this section, we use event history modeling to study the impact of migrant networks on the labor market integration. Figure 1 shows the cumulative rate of entry into the first job over years since immigration for those who had either “no,” “few,” or “many” friends and relatives in Germany before migration.

The figure shows that having friends or relatives in Germany prior to migration helped one integrate faster into the German labor market. While the entry rate before the end of the first year was approximately 7 percent for all respondents, clear differences between the three distinguished groups developed later. Of all FSU immigrants with “many” pre-migration ties, 39 percent were integrated by the end of the second year, and 61 percent by the end of the third year. For those with only “few” ties, the cumulative rates were lower (35 and 55 percent), and for those with no ties at all, the rates were much lower still (29 and 49 percent). Over time, the differences narrowed somewhat, but a lead of those with many pre-migration ties still existed at the end of the ninth year. Overall, various tests (i.e., log-rank, Cox, Wilcoxon, Tware, and Peto) consistently report significant advantages of having a lot of friends and relatives with regard to quick labor market entry, both in comparison to having just a few, respectively to having no contacts in Germany prior to migration. The differences between the latter two categories are not statistically significant at the 5-percent level.

The questions following up on this figure are whether these differences persist and whether they are statistically significant upon controlling for important covariates. To answer them, model 1 in table 3 reports the coefficients and standard errors of a discrete event history model on the risk of entering the first job.

Indeed, model 1 in table 3 indicates that even after controlling for various other variables, migrant networks seem to be very helpful for entering the labor market. As compared to those who had no friends or relatives in Germany prior to their immigration, those who had at least a few friends found their first jobs faster, and those who had many friends much faster still, the latter coefficient (.21) being significant at a 5-percent level.

Above and beyond the effects of migrant networks, Jewish Quota Refugees entered the German labor market less smoothly than Ethnic Germans did, a finding also confirmed by other studies (Kogan et al. 2011; Liebau 2011). We find that women found jobs faster than men did and that the impact of years since migration is curvilinear, with the “risk” of entering the first job increasing in the first years but flattening out later on. Those who immigrated at older ages entered their first jobs faster; women tended to do so more quickly than men. Surprisingly, education does not seem to be very important for quick labor market entry. If at all, highly educated migrants were even slower to enter employment than those with less education were. Job status in the FSU also does not seem to matter for success in Germany. And astonishingly, this also holds for their knowledge of the German language prior to migration. Finally, extroverted, agreeable, or neurotic persons seemed to face particular difficulties.

Model 1 in table 3 thus seems to provide clear evidence for the optimistic view of the impact on migrant networks. Having friends and relatives in Germany pre-migration fostered the labor market entry of FSU immigrants, while all the human-capital-related variables did a poor job of explaining early success. However, this conclusion might be somewhat premature, as in model 1 we do not consider the kinds of jobs the immigrants ended up in. Distinguishing employment according to its quality leads to a more differentiated picture, as the second model in table 3 shows.

Model 2 in table 3 is a competing-risks model discriminating roughly between PTM jobs and non-PTM jobs. We find that the bulk of the prior conclusions hold only for lower-status non-PTM jobs, as the coefficients for this destination are very similar to those in model 1. When it comes to higher-status PTM jobs, however, the picture is completely different. Here, the human-capital-related variables were extremely important; effects were strong and went in a theoretically expected direction. A better knowledge of German was beneficial for access to PTM jobs, and younger migrants had a higher chance of entering them. We also find that education is crucial for access to PTM jobs. The same is true for the occupational status of FSU employment: the higher the status of the job one held back in the home country, the higher the chances were for entering PTM employment in Germany. Immigrants for whom ISEI status from the FSU was missing or who were largely individuals without any work experience back in their sending countries were significantly more likely to target higher-status employment. An interesting side finding is that the pattern of first job entry differs largely between Ethnic Germans and JQRs. Whereas the latter were more likely to enter PTM jobs, the former compromised higher-status employment in favor of a quick entry into lower-status jobs, other things being equal (for explanations on the differences between the two groups, see Liebau [2011]).

Most importantly, we find that pre-migration ties do not enhance labor market integration in the case of higher-status employment. The effects of our measure are statistically not significant, and the signs are even negative. Migrant networks thus did not seem to bear any advantages when FSU immigrants in Germany aimed for higher-status jobs in the labor market.

In a further model (not shown here), we included a measure of the ethnic composition of the friendship network one year after immigration. Having more native Germans among one's friends had a significantly positive impact on the chances of entering PTM jobs and mediated the negative effect of pre-migration friends. Although including the one-year-after measure in the model is methodologically somewhat problematic, this lends tentative support to the theoretical assumption that pre-migration friendships might impede early social assimilation, which in turn is necessary for access to higher-status employment.

Differences in the contexts of reception among Ethnic German and Jewish immigrants led us to believe that for the latter, migrants' ethnic networks should play a more important role when it comes to labor market entry, due to the less extensive integration efforts targeting this group. Results presented in table 4 largely support our hypothesis about more pronounced effects of these networks on the labor market incorporation of Jewish immigrants in comparison to Ethnic Germans: whereas the effects of multiple social contacts are insignificant for Ethnic Germans, they are much more pronounced and statistically significant for the Jewish immigrants. Similar to the analyses presented in table 3, current results show that Jewish immigrants who knew a lot of people in Germany prior to migration displayed higher chances of ending up in low-status jobs. At the same time, their prospects for higher-status employment were significantly reduced.

Interestingly, the effects of FSU education seem to be stronger among Ethnic Germans targeting higher-status jobs. This is not particularly surprising, taking into account that the education of Ethnic German immigrants is more readily recognized in Germany. Ethnic Germans also showed a stronger improvement of their labor market entry chances with each year after migration, and their improvement rates were particularly strong when it came to PTM jobs. Language proficiency, on the other hand, seemed to affect entry chances to PTM jobs more strongly among Jewish immigrants than among Ethnic Germans.

Conclusion

Recent years have witnessed an increased interest in the issues of social capital and network resources in migration and integration research. Multiple studies have confirmed that social networks play a role in immigrants' labor market allocation. However, whether this role is positive or negative is still disputed. Our paper aimed to provide a detailed account of both optimistic and pessimistic scenarios for the effects of social resources in immigrants' labor market attainment. Following general social capital theory, we argued that both theoretical accounts are not necessarily contradictory, as social networks can indeed be helpful for quick employment entry, without guaranteeing any high occupational status of this employment. We stressed that the effect of migrant networks is heavily dependent on how established members of particular groups of interest are already integrated into the labor market and for what kinds of jobs the capital included in ethnic ties is thus specific for. The relative value of this capital is further dependent on how available to the newcomers alternative, more formal channels of getting access to jobs are.

We have empirically examined the contingent effects of migrant networks on labor market allocation, studying the strategic case of recent FSU immigrants to Germany. Using information from a unique survey with Ethnic Germans and Jewish immigrants, we were able to show that there is evidence for the optimistic as well as for the skeptical view. Having social ties in the country of destination prior to migration proves to be helpful to finding employment quickly after immigration, but a more detailed look shows that this holds only for comparably low positions in the labor market. For smooth access to better jobs, however, migrant networks seem unhelpful or even counterproductive. Finally, we assumed that ethnic networks among Jewish immigrants should play a more profound role during their job search than for Ethnic Germans, compensating for less pronounced integration support. Our results are in accordance with this hypothesis and reveal that this holds for the strength of both positive and negative effects.

We think this adds an important piece to understanding the role of migrant networks in general, as the literature so far has dealt mostly with immigrant groups that are relatively low in human capital in less strongly institutionalized labor markets, like the United States or Canada. In contrast, we have analyzed two relatively highly educated immigrant groups in a relatively highly regulated labor market. Furthermore, both of the groups under study benefited—to a varying degree—from legal privileges that are usually not open to immigrants in other contexts. The fact that we were able to find evidence for a positive role of co-ethnic ties under these conditions at all is thus a particularly strong support for the optimistic view on migrant networks and the general line of reasoning behind it. At the same time, however, this holds in parallel for the pessimistic view. The strength or even the sign of the effect changes when strategically comparing job levels and groups, and this fact clearly underlines that each of both views is valid only under certain specific circumstances, which are often not made explicit. Our analyses helped identify the aspired to level of jobs and legal privileges as major elements of these conditions.

The specific context of recent migration from the FSU to Germany proved to be strategic not only in theoretical respect, but also in methodological respect: as this migration was driven mainly by motives other than labor market expectancies, and in the case of Ethnic Germans even comprised almost the whole population at risk; the case allows us to avoid the difficult issue of possible double selection on networks and labor market expectancies, and thus a common, but usually overseen, source of bias when studying the effect of migrant networks on outcomes in the receiving society. Furthermore, our data have allowed us to alleviate the basic problem of endogeneity that has been identified as a major challenge when studying social capital effects in the labor market. We have relied on retrospective longitudinal information and have run event history analyses to tackle the issue of reverse causality. We have also been able to include a very rich set of theoretically relevant independent variables—comprising not only detailed information on education and occupations in the sending country, but also language, personality traits, and others—into the analyses to downsize the issue of omitted-variable bias. So, our analyses also contribute to the more general literature on social capital effects in the labor market.

Finally, next to the elaboration of social capital effects, our analyses also contain some interesting results with respect to the relative value of human capital. Findings at the beginning suggest that human capital resources, like education or occupational skills from abroad, seem to be discounted in the German labor market, but later analyses show that this holds true only if we do not discriminate between the types of jobs that immigrants target. Once a more differentiated approach is taken, we find clear support for the human capital perspective: younger immigrants with higher educational qualifications, better jobs in their sending countries, and a better knowledge of the German language have clearly easier access to professional, technical, and managerial jobs in Germany. Relying on these resources brings FSU immigrants favorable labor market outcomes, whereas sticking to ethnic ties deters from economic success.

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