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Occupational Sorting and Wage Gaps of Refugees

Author

Listed:
  • Christopher F. Baum

    () (Boston College
    DIW Berlin
    CESIS, KTH Royal Institute of Technology)

  • Hans Lööf

    () (CESIS, KTH Royal Institute of Technology)

  • Andreas Stephan

    () (Jönköping International Business School
    DIW Berlin)

  • Klaus F. Zimmermann

    () (UNU-MERIT
    Maastricht University
    CEPR
    GLO)

Abstract

Refugee workers start low and adjust slowly to the wages of comparable natives. The innovative approach in this study using unique Swedish employer-employee data shows that the observed wage gap between established refugees and comparable natives is mainly caused by occupational sorting into cognitive and manual tasks. Within occupations, it can be largely explained by differences in work experience. The identification strategy relies on a control group of matched natives with the same characteristics as the refugees, using panel data for 2003--2013 to capture unobserved heterogeneity.

Suggested Citation

  • Christopher F. Baum & Hans Lööf & Andreas Stephan & Klaus F. Zimmermann, 2018. "Occupational Sorting and Wage Gaps of Refugees," Boston College Working Papers in Economics 963, Boston College Department of Economics, revised 25 May 2020.
  • Handle: RePEc:boc:bocoec:963
    Note: Previously circulated as "Refugee immigrants, occupational sorting and wage gaps"
    as

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    References listed on IDEAS

    as
    1. 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.
    2. David Card, 1990. "The Impact of the Mariel Boatlift on the Miami Labor Market," ILR Review, Cornell University, ILR School, vol. 43(2), pages 245-257, January.
    3. Bauer, Thomas K. & Lofstrom, Magnus & Zimmermann, Klaus F., 2000. "Immigration Policy, Assimilation of Immigrants and Natives' Sentiments towards Immigrants: Evidence from 12 OECD-Countries," IZA Discussion Papers 187, Institute of Labor Economics (IZA).
    4. Fane Groes & Philipp Kircher & Iourii Manovskii, 2015. "The U-Shapes of Occupational Mobility," Review of Economic Studies, Oxford University Press, vol. 82(2), pages 659-692.
    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. Fasani, Francesco & Frattini, Tommaso & Minale, Luigi, 2018. "(The Struggle for) Refugee Integration into the Labour Market: Evidence from Europe," IZA Discussion Papers 11333, Institute of Labor Economics (IZA).
    7. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    8. Giovanni Peri & Vasil Yasenov, 2019. "The Labor Market Effects of a Refugee Wave: Synthetic Control Method Meets the Mariel Boatlift," Journal of Human Resources, University of Wisconsin Press, vol. 54(2), pages 267-309.
    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, December.
    11. 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.
    12. 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.
    13. 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.
    14. Amelie F.Constant & Klaus F.Zimmermann, 2017. "Towards a New European Refugee Policy that Works," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(04), pages 03-08, February.
    15. 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.
    16. 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.
    17. Sarvimäki, Matti, 2017. "Labor Market Integration of Refugees in Finland," Research Reports 185, VATT Institute for Economic Research.
    18. Semih Tumen, 2016. "The Economic Impact of Syrian Refugees on Host Countries: Quasi-experimental Evidence from Turkey," American Economic Review, American Economic Association, vol. 106(5), pages 456-460, May.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    refugees; wage earnings gap; Blinder–-Oaxaca decomposition; employer-employee data; coarsened exact matching; correlated random effects model;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J6 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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