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

Transcription

1 Migrant Networks and Labour Market Integration of Immigrants from the Former Soviet Union in Germany Frank Kalter and Irena Kogan Paper submitted to be presented at the conference Migration: Economic Change, Social Challenge, NORFACE/CReAM, London, 6 9 April 2011 Frank Kalter and Irena Kogan School of Social Sciences/Mannheim Centre for European Social Research (MZES) University of Mannheim D Mannheim kalter@uni mannheim.de ikogan@mail.uni mannheim.de

2 Abstract Migrant networks are usually seen as being helpful for the labour market integration of recently arrived immigrants. The basic reasoning builds on Granovetter s seminal work and argues that the impact of networks is even stronger in the case of new immigrants, as they have restricted access to alternative ways of getting good jobs. From a general assimilation perspective, however, it has been questioned whether migrant networks really provide the capital necessary to success in the host society or whether they instead represent mobility traps that have negative effects in the middle and the long run. Meanwhile, a series of studies reported positive effects of migrant networks, most of them in the context of Mexican or Latin American migration to the U.S. In other contexts, however, negative effects have been reported. In this paper we test the impact of migrant networks on labour market integration in the context of recent migration from the Former Soviet Union (FSU) to Germany. Relying on longitudinal information provided by a recent survey of Ethnic Germans and Jewish Quota Refugees and using discrete event history models, we are able to show that there is evidence for both kinds of arguments: whether the effects of migrant networks are positive or negative depends on the specific labour market outcome. 2

3 1. Introduction It is widely accepted among immigration scholars that social networks play an important part in understanding the labour market integration of recently arrived immigrants and that a human capital based approach alone is not sufficient. In particular, it is commonly argued that migrant networks, i.e. the existence of social ties to co ethnic immigrants living already in the host society, not only drive migration (Massey/Espinosa 1997; Kalter 2010), but are also helpful in the aftermath of immigration by providing support and means to overcome common obstacles to get access to adequate employment in the new environment. Meanwhile, a considerable number of empirical studies provide evidence for positive effects of migrant networks on the labour market success of immigrants, most of them dealing with the context of immigration to the U.S. (e.g., Aguilera 2002; 2003; 2005; Aquilera/Massey 2003; Greenwell 1997). In this paper we study the impact of migrant networks on the labour market integration of new immigrants in the context of recent immigrant from the Former Soviet Union (FSU) to Germany. This seems a worthwhile attempt for at least three reasons: First, while re examining empirical effects in different contexts is useful per se, it is especially important when asking about the labour market effects of migrant networks. Research shows that a positive impact of co ethnic ties does not seem to be unconditional, as this has been challenged theoretically (e.g., Wiley 1970; Portes/Sensenbrenner 1993; Portes 1998) and empirically (Green et al. 1999; Elliott 2001; Kazemipur 2006). So, by our analyses we contribute to identifying conditions under which migrants networks might be helpful or not and to integrating seemingly conflicting theoretical arguments. Second, looking at recent immigrants from the FSU in Germany seems a particularly promising test case: whereas negative selection with respect to human capital is held responsible for economic difficulties faced by Germany s classic labour immigrants (Granato and Kalter 2001; Kalter and Granato 2002, 2007; Kogan 2004, 2007), the standard economic framework seems unable to fully account for disadvantages more recent immigrants from the FSU face, as they arrive with relatively high levels of formal qualifications. Moreover, some of these immigrant groups are awarded a special legal status: since the early 1990s over 2 million people have arrived as so called Ethnic Germans 3

4 (EG) from the successor states of the former Soviet Union; they have immediate access to German citizenship and enjoy other important privileges. In addition, since 1991 Germany has introduced a settlement programme for Jewish immigrants from the former Soviet Union, granting them a quota refugee status (Cohen and Kogan 2005; 2007). More than 200,000 Jews and their family members have re settled in Germany. Due to their special status, Jewish quota refugees (JQR) enjoy very extensive integration support that far exceeds what other immigrants groups are entitled to (e.g. unrestricted labour market access, social assistance). So, if even these two relatively privileged groups of Ethnic Germans and Jewish Quota Refugees still benefitted from migrant networks, this would be especially telling. Third, we can rely on data that provide a relatively strong test, avoiding many limitations and pitfalls of empirical research on labour market effects of social networks (Mouw 2003; Mouw 2006). In a recent research project we gathered rich retrospective information on 892 Ethnic Germans and 658 Jewish Quota Refugees that allows us to study the impact of pre migration ties in a longitudinal perspective, controlling for a series of human capitalrelated variables. We start with a short review of the main theoretical arguments underlying the more optimistic, as well as the more sceptical view on the role of migrant networks for labour market integration (section 2). We continue by describing our data, methods, and variables in more detail (section 3) and then report our major empirical findings (section 4). As briefly summarized in the discussion (section 5), our analyses show that for the immigrants from the FSU in Germany the effect of migrant networks depends heavily on the kind of job they are looking for. 2. Theoretical background: Optimistic vs. sceptical view on the role of migrant networks Social networks are known to be effective sources of information on job vacancies, of reduced transaction costs and of increased efficiency (Granovetter 1974; Waldinger 1996, Burt 1992). Since Granovetter s seminal work (1973, 1974) a large body of research has addressed the role of social capital in labour market success (Burt 2001; Lin 2001; Lin et al. 4

5 2001; Portes 1998). The use of informal search methods is viewed as efficient for both employers and job seekers (Ioannides and Loury 2004; Marsden 2001; Erickson 2001). 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 (Montgomery 1992; Flap and Boxman 2001). When asking about the specific role of migrant networks in labour market integration, in the literature we largely find a very optimistic view that follows this very general line of argument. All the advantages associated with social networks are more or less straightforwardly transferred to immigrants. It is even argued that the marginal utility of using social ties might be higher in this case, due the fact that fewer alternatives are at hand; an extreme example would be that undocumented immigrants cannot simply go the federal employment bureaus. 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, high solidarity, and labour market opportunities within the ethnic economy (Portes 1995; Zhou 1992; Waldinger 1994, 2005; Sanders and Nee 1996; Sanders et al. 2002). In the meantime there is much evidence supporting this basically optimistic view. Studies using data from the Mexican Migration Project (MMP) show that ties to co ethnics improve the employment chances (Aguilera 2002), the job quality (Aguilera/Massey 2003), the wages (Massey/Espinosa 1997; Aguilera/Massey 2003), or the job stability (Aguilera 2003) of Mexican immigrants in the U.S. There are similar findings for the wages of Salvadorian and Filipino immigrants in Los Angeles (Greenwell et al. 1997), or the earnings of Puerto Rican immigrants in the U.S. (Aguilera 2005). However, there is also a more sceptical view on this matter. On the one hand, the scepticism refers to methodological issues. A closer look at many studies reveals that the empirical evidence for positive effects is not always convincing: effect sizes are often rather weak, and biases due to the well known problems of homophily or endogeneity are pronounced (Mouw 2002; 2003; 2006). While these are problems of research on labour market effects of networks in general, they apply to many migrant studies too. 5

6 In addition, also the theoretical side of the general reasoning might be challenged. Most importantly, one may question whether migrant networks really provide the right sort of social ties. It is not social ties per se that matter, but the valuable resources they might contain (Lin 2001). And the crucial question is whether ethnic ties provide a satisfying amount of these resources. It is often counter argued that sticking to ethnic bonds might impede building more helpful ties to the host society, and might thus lead to lower quality employment, often located in the ethnic communities (Portes/Sensenbrenner 1993). A picture often used is that of a kind of entrapment resulting from ethnic ties (Bonacich 1973; Wiley 1970; Portes and Rumbaut 2001; Portes 1998; Portes and Sensenbrenner 1993). So these kinds of arguments would give reason to expect negative effects of the existence of ethnic ties on the labour 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 U.S. (Green et al. 1999), to more ethnically homogeneous jobs of Blacks in U.S. cities (Elliott 2001), or to lower wages of immigrants in Canada (Kazemipur 2006). 3. Data, methods, and variables We use data from a recent project called Labour Market Integration: Aussiedler and Jewish Immigrants from the Former Soviet Union in Germany and Israel. The project was funded by the German Israeli Foundation between 2005 and As the title suggests, this was a comparative project and data was collected both in Germany and in Israel. In the current paper, however, we will rely on the German data only, as crucial time related variables relevant for our analyses were not included in the Israeli questionnaire. The data was collected by means of a telephone survey in Germany in May and June The target population was immigrants from the Former Soviet Union (FSU), who were years old, arrived in Germany at age 18 or older, and between the years A Germany wide telephone register provided a sample frame, whereas the names of the potential respondents were pre selected based on an onomastic procedure. According to this procedure the probability that a certain combination of first and last names pertains to 6

7 a specific ethnic background is calculated based on the computerized dictionaries of names and pertinent regional codes. After the lists of potential immigrants from the former Soviet Union were created, we screened this sample to establish whether the selected individuals indeed belonged to the target population (for more details on the sampling procedure and methodological issues of the telephone survey see Liebau 2010). We provided the respondents with the option of being interviewed by a native Russian language speaker, which was preferred by the vast majority of those interviewed. Overall, 658 interviews with Jewish Quota Refugees (JQR) and 892 interviews with Ethnic Germans (EG) were realized. This is a rather unique data set for Germany, as to date there is still not much empirical information on these two important recent immigrant groups. In our core analysis we use discrete event history models to study the entry into the first job after immigration to Germany, the time unit being years. Of 1545 respondents with valid information on both the year of immigration and, if applicable, the year of entry into the first job, 7% already entered employment in the year of immigration, and 28% in the year afterwards (see also Figure 1 below). After 10 years, 89% of all FSU immigrants in our sample had been employed (at least once) in Germany. We further differentiate in competing risk models whether this employment was of higher status, encompassing professional, managerial or technical occupations (ISCO 1 digit codes: 1, 2, and 3), or of lower status (all other ISCO 1 digit codes). Our central independent variable is a measure of how many friends or relatives the FSU immigrants already had in Germany before their immigration. Table 1 shows that migrant networks were quite prevalent among the immigrants from the FSU who came to Germany between 1994 and Among the group of Ethnic Germans, 52.3% of all answered with category a lot (4 and more), and another 26.3% with a few (1 3). Among the Jewish Quota Refugees, the percentages are a bit lower, albeit also sizeable. Here, 25.5% of the respondents say a lot, and 35.3% a few. So, 78.6% of all Ethnic Germans and 60.8% of JQRs knew at least somebody in Germany before they migrated. 7

8 Table 1: Network characteristics of FSU immigrants Ethnic Germans Jewish Quota Refugees Friends and relatives in Germany before immigration (n=899) (n=662) a lot (4 and more) 52.3% 25.5% a few (1 3) 26.3% 35.3% none 21.5% 39.1% As the variable pertains to the existence of ties prior to migration, we avoid the common problem of endogeneity when including it in the analysis of the time dependent risk of entering the first job in Germany after immigration. Also included in our multivariate analyses is a set of control variables. First, we differentiate between two immigrant groups, and between men and women. We also control for age at arrival and for 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, i.e., 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 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). 4. Findings 4.1 The importance of migrant networks from a standard perspective Before we turn to our core discrete event 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 labour market integration of 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 ticked each of these channels. 8

9 Table 2: Job channels of FSU immigrants How did you hear about your current job? (percentages) Ethnic Germans Jewish Quota Refugees Federal employment office Private recruitment agency Advertisement in the media Advertisement in the internet Relatives and close friends thereof: Russian Native Acquaintance thereof: Russian Native Returned to a former employer Direct application Other/none of the above Source: Data from the project Labour Market Integration: Aussiedler and Jewish Immigrants from the Former Soviet Union in Germany and Israel The results in Table 2 suggest that migrant networks are indeed very important for the integration of FSU immigrants into the German labour market. 14.3% of the employed Ethnic Germans and 9% of the employed Jewish Quota Refugees found their job via relatives and close friends, so called strong ties. In 92.2%, resp. 87.2% of the cases, these relatives or close friends also stem from the FSU. Even more immigrants (EG: 29.1%; JQR: 23.2) found their job via acquaintances (weak ties), and again, these were predominantly (78.8%, resp. 65.0%) co ethnic ties. It is quite telling to compare these figures to data from the German Socio Economic Panel (GSOEP), which includes a similar battery, 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% found their current job via referrals by others. Apparently, networking plays a more important role in the immigrant job search, as the respective value lay at 43.3% among Ethnic Germans and at 32.2% among JQR. These descriptive results support the general findings from many other studies (see especially Drever/Hoffmeister 2008). In getting jobs, social ties seem more important for FSU immigrants than for native Germans, and in the vast majority of cases these ties are coethnic. However, using this straight forward information on how the current job was found we cannot tell whether the usage of co ethnic ties leads to a relative advantage or to a disadvantage in the labour market. Gaining employment with the help of social networks 9

10 must not necessarily result in high quality employment. To give an appropriate answer to the question of whether networking leads to a quicker entry into good jobs, a longitudinal perspective is required. 4.2 The impact of social ties in a longitudinal perspective In this section we study the impact of migrant networks on the labour market integration, using event history modelling. Figure 1 shows the cumulative entries into the first job over years since immigration for those who had either no, a few, or many friends and relatives in Germany before migration. Figure 1: Cumulative rate of job entries among FSU immigrants in Germany Year since migration Friends and relatives in Germany before migration none a few many The figure shows that having friends or relatives in Germany prior to migration helps one to integrate faster into the German labour market. While the entry rate before the end of the first year is approximately 7% for all respondents, clear differences between the three distinguished groups develop later on. Of all FSU immigrants with many pre migration ties, 39% are integrated before the end of the second year, and 61% before the end of the third 10

11 year. For those with only a few ties, the cumulative rates are lower (35% and 55%), and for those with no ties at all, the rates are much lower (29% and 49%). Over time, the differences narrow somewhat, but a lead of those with many pre migration ties still exists at the end of the ninth year. The questions following up on this figure are now: Are these differences statistically significant, and do they persist upon controlling for important third variables? It might well be that people possessing networks in Germany systematically differ from those without such contacts in a number of characteristics relevant for the labour market success. To answer this, 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. Table 3: Effects on the risk function (hazard rate) of entry into the first job Friends in GER before migration: Model 1 Model 2 (competing risks) non PTM PTM none few.12 (.08).16 (.10).03 (.09) many.19 (.08) **.22 (.09) **.18 (.09) **.02 (.04).03 (.05).36 (.05) *** German language prior to migration Years since migration (YSM).75 (.05) ***.73 (.06) ***.36 (.05) *** YSM squared.06 (.01) ***.07 (.01) ***.04 (.01) *** Age at arrival.02 (.00) ***.02 (.00) ***.04 (.01) *** Education: elementary general secondary.17 (.15).09 (.15) 1.34 (.38) *** vocational secondary.16 (.14).09 (.14).98 (.40) ** profess. sec./lower tert..13 (.13).03 (.13) 2.30 (.35) *** tertiary.26 (.15) *.18 (.16) 2.53 (.36) *** ISEI in FSU.00 (.00).00 (.00).04 (.00) *** FSU ISEI missing.30 (.17).23 (.19) 2.55 (.21) *** JQR (vs. EG).62 (.08) ***.73 (.09) ***.69 (.09) *** Female.72 (.07) ***.72 (.08) ***.58 (.08) *** Intercept 2.58 (.24) *** 2.27 (.27) *** 6.03 (.44) *** N (persons) Person years Chi Pseudo R Source: Data from the project Labour Market Integration: Aussiedler and Jewish Immigrants from the Former Soviet Union in Germany and Israel Note: standard errors in brackets; * = p <.10, ** = p <.05, *** = p <.01 11

12 Model 1 of Table 3 indicates that controlling for various variables migrant networks seems indeed to be very helpful for entering the labour market. As compared to those who had no friends or relatives in Germany before their immigration, those who had at least a few friends find their first job faster, and those who had many much faster still, the latter coefficient (.19) being significant at a 5% level. Above and beyond the effects of migrant networks, Jewish Quota Refugees enter the German labour market less smoothly than Ethnic Germans do (coefficient:.62), a finding also confirmed by other studies (Cohen and Kogan 2007; Kogan et al. 2011). We find that females (.72) enter faster than males do and that the impact of years since migration is curvilinear, with the risk of entering the first job increasing in the first years but falling in later years. Those who immigrated at older ages enter the first job faster. Surprisingly, education does not seem to be very important for quick labour market entry. If at all, highly educated migrants are even slower to enter employment than those with less education are. Job status in the FSU also does not seem to matter for success in Germany. And, astonishingly, this also holds for knowledge of the German language prior to migration. So, Model 1 of Table 3 seems to provide clear evidence for the optimistic view on the impact on migrant networks. Pre migration friends and relatives in Germany foster the labour market entry of FSU immigrants, while all the human capital related variables do a poor job of explaining early success. However, this conclusion is drawn a bit too fast, as in Model 1 we do not consider the kind of jobs the immigrants ended up in. Distinguishing according to the quality of a job 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 all prior conclusions hold only for lower status non PTM jobs, as the coefficients in this sub model are very similar to those in Model 1. When it comes to higher status PTM jobs, however, the picture is completely different. Here, the humancapital related variables are extremely important; effects are strong and show the expected sign: a better knowledge of German is beneficial for access to PTM jobs and younger migrants have a higher risk to enter them. Most importantly, we 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 a job one held back in the home country, the higher 12

13 the chances are for entering PTM employment in Germany. An interesting side finding is that the pattern of first job entry differs largely between Ethnic Germans and JQRs. Whereas the latter are more likely to enter PTM job, the latter compromise higher status employment in favour of a quick entry into lower status jobs, other things being equal (for explanations on the differences between the two groups see Liebau 2010). Most importantly, we find that our measure of pre migration ties works in a negative direction in the case of higher status employment. Those who had more friends and relatives in Germany before immigration have, even controlling for all the detailed humancapital characteristics, a significantly lower chance to enter PTM jobs. So, migrant networks seem to be at a disadvantage when FSU immigrants in Germany aim for higher status jobs in the labour 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 relatively more native Germans among one s friends has a significantly positive impact on the risk of entering PTM jobs and mediates the negative effect of pre migration friends. Although including the one year after measure into 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. 5. Conclusion Our analyses clearly show that social networks play an important role in understanding labour market integration of recent immigrants from the FSU in Germany. We have seen that a more precise understanding of these processes requires a longitudinal perspective, on the one hand, and a more differentiated view on the type of jobs, on the other hand. Using event history information from a recent study of Ethnic Germans and Jewish Quota Refugees, we were able to show that there is also evidence for the optimistic as well as for the sceptical view on the role of migrant networks. Having social ties in the country of destination before migration proves to be very helpful to finding employment quickly after immigration, but this holds only for comparably low positions in the labour market. For smooth access to better jobs, however, migrant networks seem counter productive. 13

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