We study the causal effect of local labor market conditions and attitudes towards immigrants at the time of arrival on refugees’ multi-dimensional integration outcomes (economic, linguistic, navigational, political, psychological, and social). Using a unique dataset on refugees, we leverage a centralized allocation policy in Germany where refugees were exogenously assigned to live in specific counties. We find that high initial local unemployment negatively affects refugees’ economic and social integration: they are less likely to be in education or employment and they earn less. We also show that favorable attitudes towards immigrants promote refugees’ economic and social integration. The results suggest that attitudes toward immigrants are as important as local unemployment rates in shaping refugees’ integration outcomes. Using a machine learning classifier algorithm, we find that our results are driven by older people and those with secondary or tertiary education. Our findings highlight the importance of both initial economic and social conditions for facilitating refugee integration, and have implications for the design of centralized allocation policies.

AbstractWe provide a concise introduction to a household-panel data infrastructure that provides the international research community with longitudinal data of private households in Germany since 1984: the German Socio-Economic Panel (SOEP). We demonstrate the comparative strength of the SOEP data in answering economically-relevant questions by highlighting its diverse and impactful applications throughout the field.

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This article highlights the potentials for migration research using the German Socio-Economic Panel Study (SOEP), a longitudinal panel dataset of private households in Germany running since 1984. We provide a concise overview of its basic features, describe the survey contents and research potentials, and demonstrate opportunities to link external data sources to the SOEP thereby presenting its diverse and impactful applications in migration research.

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We study the causal effect of local labor market conditions and attitudes towards immigrants at the time of arrival on refugees' multi-dimensional integration outcomes (economic, linguistic, navigational, political, psychological, and social). Using a unique dataset on refugees, we leverage a centralized allocation policy in Germany where refugees were exogenously assigned to live in specific counties. We find that high initial local unemployment negatively affects refugees' economic and social integration: they are less likely to be in education or employment and they earn less. We also show that favorable attitudes towards immigrants promote refugees' economic and social integration. The results suggest that attitudes toward immigrants are as important as local unemployment rates in shaping refugees' integration outcomes. Using a machine learning classifier algorithm, we find that our results are driven by older people and those with secondary or tertiary education. Our findings highlight the importance of both initial economic and social conditions for facilitating refugee integration, and have implications for the design of centralized allocation policies.

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