First time around: local conditions and multi-dimensional integration of refugees

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.