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Estimating the Wage Premia of Refugee Immigrants: Lessons from Sweden
KTH, School of Industrial Engineering and Management (ITM), Centres, Centre of Excellence for Science and Innovation Studies, CESIS. Boston College.ORCID iD: 0000-0003-4766-3699
KTH, School of Industrial Engineering and Management (ITM), Centres, Centre of Excellence for Science and Innovation Studies, CESIS. KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.).ORCID iD: 0000-0002-5871-8571
KTH, School of Industrial Engineering and Management (ITM), Centres, Centre of Excellence for Science and Innovation Studies, CESIS. Linnaeus University.ORCID iD: 0000-0001-5776-9396
Center for Economic Policy Research (CEPR); Bonn University; Global Labor Organization (GLO).
2024 (English)In: Industrial & labor relations review, ISSN 0019-7939, E-ISSN 2162-271X, Vol. 77, no 4, p. 562-597Article in journal (Refereed) Published
Abstract [en]

This article examines the wage earnings of refugee immigrants in Sweden. Using administrative employer–employee data from 1990 onward, approximately 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 to wage earnings for the period 2011–2015, 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 attributable to female refugee immigrants. Given their characteristics, refugee immigrant females perform better than native females across all occupational tasks studied, including non-routine cognitive tasks. A notable similarity of the wage premium exists among various refugee groups, suggesting that cultural differences and the length of time spent in the host country do not have a major impact.

Place, publisher, year, edition, pages
SAGE Publications , 2024. Vol. 77, no 4, p. 562-597
Keywords [en]
employer–employee data, gender, job-tasks, occupations, recentered influence function (RIF) quantile regressions, refugees, wage earnings gap
National Category
Economics
Identifiers
URN: urn:nbn:se:kth:diva-366532DOI: 10.1177/00197939241261640ISI: 001250710500001Scopus ID: 2-s2.0-85197642461OAI: oai:DiVA.org:kth-366532DiVA, id: diva2:1982652
Note

QC 20250708

Available from: 2025-07-08 Created: 2025-07-08 Last updated: 2025-07-08Bibliographically approved

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Baum, ChristopherLööf, HansStephan, Andreas

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