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Estimating the wage premia of refugee immigrants: Lessons from Sweden

Author

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  • Baum, Christopher F.

    (Boston College, & CESIS)

  • Lööf, Hans

    (Royal Institute of Technology & CESIS)

  • Stephan, Andreas

    (Linnaeus University, DIW Berlin & CESIS)

  • Zimmermann, Klaus F.

    (UNU-MERIT & Maastricht Universit)

Abstract

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.

Suggested Citation

  • Baum, Christopher F. & Lööf, Hans & Stephan, Andreas & Zimmermann, Klaus F., 2023. "Estimating the wage premia of refugee immigrants: Lessons from Sweden," Working Paper Series in Economics and Institutions of Innovation 496, Royal Institute of Technology, CESIS - Centre of Excellence for Science and Innovation Studies, revised 30 May 2024.
    • Handle: RePEc:hhs:cesisp:0496
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    1. Oskar Nordström Skans & Per-Anders Edin & Bertil Holmlund, 2009. "Wage Dispersion Between and Within Plants: Sweden 1985-2000," NBER Chapters, in: The Structure of Wages: An International Comparison, pages 217-260, National Bureau of Economic Research, Inc.
    2. Sari Pekkala Kerr & William R. Kerr, 2011. "Economic Impacts of Immigration: A Survey," Finnish Economic Papers, Finnish Economic Association, vol. 24(1), pages 1-32, Spring.
    3. Häkkinen Skans, Iida & Carlsson, Mikael & Nordström Skans, Oskar, 2017. "Wage Flexibility in a Unionized Economy with Stable Wage Dispersion," Working Papers 149, National Institute of Economic Research.
    4. Hibbs, Douglas A, Jr & Locking, Hakan, 2000. "Wage Dispersion and Productive Efficiency: Evidence for Sweden," Journal of Labor Economics, University of Chicago Press, vol. 18(4), pages 755-782, October.
    5. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
    6. Erik Hurst & Yona Rubinstein & Kazuatsu Shimizu, 2021. "Task-Based Discrimination," NBER Working Papers 29022, National Bureau of Economic Research, Inc.
    7. John M. Abowd & Francis Kramarz & David N. Margolis, 1999. "High Wage Workers and High Wage Firms," Econometrica, Econometric Society, vol. 67(2), pages 251-334, March.
    8. Iacus, Stefano & King, Gary & Porro, Giuseppe, 2009. "cem: Software for Coarsened Exact Matching," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i09).
    9. Gary King & Christopher Lucas & Richard A. Nielsen, 2017. "The Balance‐Sample Size Frontier in Matching Methods for Causal Inference," American Journal of Political Science, John Wiley & Sons, vol. 61(2), pages 473-489, April.
    10. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    11. Akgündüz, Yusuf Emre & Torun, Huzeyfe, 2020. "Two and a half million Syrian refugees, tasks and capital intensity," Journal of Development Economics, Elsevier, vol. 145(C).
    12. David H. Autor & Michael J. Handel, 2013. "Putting Tasks to the Test: Human Capital, Job Tasks, and Wages," Journal of Labor Economics, University of Chicago Press, vol. 31(S1), pages 59-96.
    13. Reinhard Schunck, 2013. "Within and between estimates in random-effects models: Advantages and drawbacks of correlated random effects and hybrid models," Stata Journal, StataCorp LP, vol. 13(1), pages 65-76, March.
    14. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    15. Courtney Brell & Christian Dustmann & Ian Preston, 2020. "The Labor Market Integration of Refugee Migrants in High-Income Countries," Journal of Economic Perspectives, American Economic Association, vol. 34(1), pages 94-121, Winter.
    16. Joan Llull, 2018. "The Effect of Immigration on Wages: Exploiting Exogenous Variation at the National Level," Journal of Human Resources, University of Wisconsin Press, vol. 53(3), pages 608-662.
    17. Kai Ingwersen & Stephan L. Thomsen, 2021. "The immigrant-native wage gap in Germany revisited," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 825-854, December.
    18. Reinhard Schunck & Francisco Perales, 2017. "Within- and between-cluster effects in generalized linear mixed models: A discussion of approaches and the xthybrid command," Stata Journal, StataCorp LP, vol. 17(1), pages 89-115, March.
    19. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    20. Ezgi Kaya, 2023. "Gender wage gap trends in Europe: The role of occupational skill prices," International Labour Review, International Labour Organization, vol. 162(3), pages 385-405, September.
    21. Fane Groes & Philipp Kircher & Iourii Manovskii, 2015. "The U-Shapes of Occupational Mobility," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(2), pages 659-692.
    22. Matthew Blackwell & Stefano Iacus & Gary King & Giuseppe Porro, 2009. "cem: Coarsened exact matching in Stata," Stata Journal, StataCorp LP, vol. 9(4), pages 524-546, December.
    23. Alan S. Blinder, 1973. "Wage Discrimination: Reduced Form and Structural Estimates," Journal of Human Resources, University of Wisconsin Press, vol. 8(4), pages 436-455.
    24. Kalena E. Cortes, 2004. "Are Refugees Different from Economic Immigrants? Some Empirical Evidence on the Heterogeneity of Immigrant Groups in the United States," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 465-480, May.
    25. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    26. Pieter Bevelander, 2020. "Integrating refugees into labor markets," IZA World of Labor, Institute of Labor Economics (IZA), pages 269-269, September.
    27. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    28. Becker, Sascha O. & Ferrara, Andreas, 2019. "Consequences of forced migration: A survey of recent findings," Labour Economics, Elsevier, vol. 59(C), pages 1-16.
    29. Marbach, Moritz & Hainmueller, Jens & Hangartner, Dominik, 2017. "The Long-Term Impact of Employment Bans on the Economic Integration of Refugees," Research Papers repec:ecl:stabus:3618, Stanford University, Graduate School of Business.
    30. Sergio P. Firpo & Nicole M. Fortin & Thomas Lemieux, 2018. "Decomposing Wage Distributions Using Recentered Influence Function Regressions," Econometrics, MDPI, vol. 6(2), pages 1-40, May.
    31. Fernando Rios-Avila, 2020. "Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition," Stata Journal, StataCorp LP, vol. 20(1), pages 51-94, March.
    32. Mattias Muckenhuber & Miriam Rehm & Matthias Schnetzer, 2022. "A Tale of Integration? The Migrant Wealth Gap in Austria," European Journal of Population, Springer;European Association for Population Studies, vol. 38(2), pages 163-190, May.
    33. Iacus, Stefano M. & King, Gary & Porro, Giuseppe, 2012. "Causal Inference without Balance Checking: Coarsened Exact Matching," Political Analysis, Cambridge University Press, vol. 20(1), pages 1-24, January.
    34. Nahikari Irastorza & Pieter Bevelander, 2021. "Skilled Migrants in the Swedish Labour Market: An Analysis of Employment, Income and Occupational Status," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    35. Eduard Storm, 2022. "Task specialization and the Native‐Foreign Wage Gap," LABOUR, CEIS, vol. 36(2), pages 167-195, June.
    36. Cortes, Kalena E., 2004. "Are Refugees Different from Economic Immigrants? Some Empirical Evidence on the Heterogeneity of Immigrant Groups in the United States," IZA Discussion Papers 1063, Institute of Labor Economics (IZA).
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    More about this item

    Keywords

    refugees; wage earnings gap; occupations; gender; employer–employee data; job-tasks; recentered influence function (RIF) quantile regressions;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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