THE BENEFITS OF MIGRATION

ABSTRACT

Assuming that migration and integration have lasting effects on economic and social processes, this paper analyses if a diverse cultural environment has a positive impact on labour market success. We expect that culturally diverse labour markets provide new opportunities through an open and tolerant climate, thus contributing to overall economic growth. We test this assumption by analysing the successful transition from education to work depending on the regional distribution and ethnic mix of the foreign population in Germany (cultural diversity). To account for variation within Germany, cultural diversity is observed at small administrative units. We analyse a cohort of young adults at the time of the successful completion of their apprenticeship in Germany and follow them through the beginning of their career. The concentration on a homogenous group regarding occupational certificates enables us to focus on the effects of the local and social environment as well as individual characteristics such as their national background on finding a job (or not). We apply an instrumental variable design to disentangle the effects of cultural diversity and share of foreigners. The results show that both young foreigners and Germans face significantly lower barriers for employment entry in culturally more diverse German regions.

Keywords:

• cultural diversity
• labour markets
• integration
• Germany

Notes

¹This Paper is part of the Migrant Diversity and Regional Disparity in Europe (MIDI-REDIE) project, funded by the NORFACE research programme Migration in Europe – Social, Economic, Cultural and Policy Dynamics. Financial support from the Volkswagen Foundation for an earlier draft is also gratefully acknowledged as part of the Study Group on Migration and Integration “Diversity, Integration and the Economy”. For helpful comments on this article, we are grateful to two anonymous ES reviewers and to Gianmarco Ottaviano for comments on an earlier draft.

²Beside education in the host country, the generational status (second and third generation) becomes increasingly important (see for example Hammarstedt 2009; Andersson and Hammarstedt 2010).

³This assumption relies on the socio-cultural mixed embeddedness hypothesis; see e.g., Kloosterman and Rath (2001).

⁴Furthermore, cultural diversity may enter directly into individual utility, because consumers have a love for variety and prefer to choose from different products. Therefore, cultural diversity enriches the supply of goods in a region, because different goods and services are offered (international restaurants, exotic food stores, etc.) (see Alesina and La Ferrara 2005).

⁵For a more detailed description of the institutional background of the education and training system in Germany see for instance Euwals and Winkelmann (2002).

⁶For detailed information see Jacobebbinghaus and Seth (2007).

⁷An alternative specification is a duration model for the time between end of apprenticeship and first employment. But the transition from VTS to work is quite smooth compared to, for example, university graduates due to institutionalised training in firms (Gangl 2003). Hence, previous research suggests that not the transition process itself seems to be relevant, but the outcome since transition into unemployment has long lasting negative effects on labour market chances (Dietrich and Abraham 2008).

⁸For example, graduates attending the university after apprenticeship training or foreigners returning to their home countries. Job seekers are included in the ‘unemployed’. Those apprentices, who do not enter the regular labour market within 2 years after completing training, are excluded from the sample. This time span was chosen because 2 years is the maximum gap that would appear if national service applied. In Germany, national service is compulsory for men and its length varies between 12 and 18 months (see Fitzenberger and Kunze 2005).

⁹For sensitivity analysis we analysed different cohorts (e.g., 1998 and 2002) with basically the same results. For a discussion of longitudinal aspects of vocational training see Burkert and Seibert (2007).

¹⁰According to other studies (e.g., Granato and Kalter 2001; Kogan 2004), migrants from EU 15 countries regardless of their heritage perform relatively well on the German labour market whereas foreigners from other countries suffer from ethnic penalties that originate in institutional arrangements. For the sake of brevity we have aggregated EU 15 nationals to one group.

¹¹The group ‘Other Nationals’ consists of more than 100 nationalities, from whom the largest proportion are people from Morocco (16 percent), Poland (13 percent) and Russia (6 percent). Interpretations should be done carefully due to its heterogeneity.

¹²Ethnic Germans (‘Spaetaussiedler’) cannot be identified as they are granted German citizenship upon entry to Germany. Thus, they are a small share of the German group.

¹³The classification of the regions (districts) refers to NUTS 3 level. The Nomenclature of Territorial Units for Statistics (NUTS) is a geocode standard for referencing the administrative divisions of countries for statistical purposes. The standard was developed by the European Union and is to some extent similar to the FIPS standard in the United States.

¹⁴The classification is developed by the Federal Office for Building and Regional Planning (BBR).

¹⁵It is important to note that standard errors cannot be clustered per counties anymore in this two-step set-up. To overcome this shortcoming, we undertake a maximum likelihood instrumental estimation as robustness check including cluster-robust standard errors (see Appendix 4). Clustering on county level accounts for the nested structure of the data and yields unbiased estimation of standard errors. The influence of the diversity index remains significant. Alternatively, a multilevel approach can be employed. One advantage of multilevel modeling, compared to the cluster-standard-error approach, is separate estimates for each county (326 in Western Germany and Berlin). This advantage is of minor relevance in this approach as we focus on the impact of cultural diversity in general.

¹⁶ Buendnis 90/Die Gruenen. This instrument is also employed by Suedekum et al. (2009).

¹⁷Additionally, we report the results of the F-test on the excluded instrument of Equation (1). An often-used reasonable critical value for this test is 10; if the F-statistic is higher, there is little reason to worry about weak instruments (Verbeek 2008).

¹⁸For example, GNP-development and share of Green Party voters at county level are negatively correlated and, moreover, very small (–0.045).

¹⁹Hence, both diversity indices are calculated without Germans.

²⁰The index of diversity is a commonly used measure in demographic research to account for variation in categorized data. It was created by Gibbs and Martin (1962).

²¹This strategy is in line with other studies assuming an optimal range of diversity. The optimal level is exceeded if (transaction) costs become too high and outreach the benefits, for example due to language barriers, missing trust, different norms and values (the ‘Babe effect').

²²The corresponding intervals are for 0–25 percent quartile: 0.3325–0.8091; 25–50 percent quartile: 0.8092–0.8496; 50–75 percent quartile: 0.8497–0.8852; 75–100 percent quartile: 0.8853–0.9585.

²³The corresponding intervals are for 0–25 percent quartile: 0.5756–0.8437; 25–50 percent quartile: 0.8438–0.8761; 50–75 percent quartile: 0.8762–0.9112; 75–100 percent quartile: 0.9113–0.9650.

²⁴All coefficients are displayed in Appendix 1. The first stage regression is shown in Appendix 2.

²⁵See Appendix 4.

²⁶Additional regression results are available from the authors upon request.

²⁷See Appendix 3.