Scandinavian Working Papers in Economics

Working Papers in Economics and Statistics,
Linnaeus University, School of Business and Economics, Department of Economics and Statistics

No 3/2024: Estimating the Wage Premia of Refugee Immigrants: Lessons from Sweden

Christopher F. Baum (), Hans Lööf (), Andreas Stephan () and Klaus F. Zimmermann ()
Additional contact information
Christopher F. Baum: Boston College, DIW Berlin and CESIS
Hans Lööf: Royal Institute of Technology, Stockholm
Andreas Stephan: Linnaeus University and DIW Berlin, Postal: School of Business and Economics, Linnaeus University, 351 95 Växjö, Sweden
Klaus F. Zimmermann: UNU-MERIT, Maastricht University, CEPR and GLO, Postal: Department of Economics and Statistics, School of Business and Economics, Linnaeus University, 351 95 Växjö, Sweden

Abstract: This paper examines the wage earnings of fully-employed refugee immigrants in Sweden. Using administrative employer-employee data from 1990 and onwards, about 100,000 refugee immigrants who arrived between 1980 and 1996 and were granted asylum are compared to a matched sample of native-born workers. Employing recentered influence function (RIF) quantile regressions for the period 2011–2015 to wage earnings, the occupational task-based Oaxaca–Blinder decomposition approach shows that refugees perform better than natives at the median wage, controlling for individual and firm characteristics. This overperformance is due to female refugee immigrants, who have higher wages than comparable native-born female peers up to the 8th decile of the wage distribution. Refugee immigrant females perform better than native females across all occupational tasks studied, including non-routine cognitive tasks. A remarkable similarity exists in the relative wage distributions among various refugee groups, suggesting that cultural differences and the length of time spent in the host country do not significantly affect their labor market performance.

Keywords: refugees; wage earnings gap; occupational sorting; employer-employee data; recentered influence function; Oaxaca–Blinder decomposition

JEL-codes: C23; F22; J24; O15

Language: English

60 pages, February 9, 2024

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