Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/265394 
Year of Publication: 
2022
Series/Report no.: 
WiSo-HH Working Paper Series No. 68
Publisher: 
Universität Hamburg, Fakultät für Wirtschafts- und Sozialwissenschaften, WiSo-Forschungslabor, Hamburg
Abstract: 
We compare the earnings of monolingual and competent bilingual immigrants in Germany. A joint discussion of language skills as human capital or social capital and theories of ethnic marginalisation leads us to expect heterogeneous returns to bilingualism. To track this potential divergence, we differentiate effects by gender, language group, immigrant density in respond- ents' areas of residence, and communication intensity of their occupations. Doing so reveals sizeable differences in the returns to bilingualism. We find positive effects for the largest immigrant community in Germany, the Turkish population, while other language groups do not seem to benefit from retaining their heritage language. Individuals with a Turkish migration background have higher earnings when proficient in their heritage language and German. We discuss how the size of the immigrant community and the bilingualism premium might be related and pursue two alternative explanations: Specialised labour demand due to the wide dissemination of the Turkish-speaking population in Germany and ethnic social networks that are open only to those with proficiency in the heritage language. Our data indicate that both are important but operate in gender-specific ways. Turkish bilingual men experience an earnings premium only in occupations with high communication intensity, while there is no statistical relationship for women. The social capital channel is also much more potent for men, while bilingual German-Turkish women do not seem to profit from heritage-language networks for labour market success to the same degree. We discuss differences by gender in activating social capital and cultural influences as drivers of this gap.
Document Type: 
Working Paper

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