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Explaining Gender Wage Differentials: Findings from a Random Effects Model


  • Kyyrä, Tomi
  • Korkeamäki, Ossi


In this paper we evaluate the extent to which the gender wage gap in the Finnish manufacturing sector is attributable to within-job wage differentials, sex differences in individual qualifications, and disproportionate concentration of women in lower-paying firms and lower-paying jobs within firms. We use matched employer-employee data to compare wage differentials between similarly qualified female and male workers who are doing the same job for the same employer. Our modelling approach employs a nested random effects specification to account for the hierarchical grouped structure of the underlying data. White-collar women are found to earn 22% less on average than their male counterparts do. Among blue-collar workers, women?s mean wage is 16% lower than men?s mean wage. The major part of the gender wage gap of white-collar workers results from sex segregation among jobs within firms. By contrast, most of the gap of blue-collar workers is attributable to sex segregation among firms. Unexplained within-job wage differentials account for a quarter of the overall gap of white-collar workers and one-fifth of the overall gap of blue-collar workers.

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  • Kyyrä, Tomi & Korkeamäki, Ossi, 2003. "Explaining Gender Wage Differentials: Findings from a Random Effects Model," Discussion Papers 320, VATT Institute for Economic Research.
  • Handle: RePEc:fer:dpaper:320

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    References listed on IDEAS

    1. H. Baltagi, Badi & Heun Song, Seuck & Cheol Jung, Byoung, 2001. "The unbalanced nested error component regression model," Journal of Econometrics, Elsevier, vol. 101(2), pages 357-381, April.
    2. Francine D. Blau & Lawrence M. Kahn, 2000. "Gender Differences in Pay," Journal of Economic Perspectives, American Economic Association, vol. 14(4), pages 75-99, Fall.
    3. Nabanita Datta Gupta & Donna S. Rothstein, 2005. "The Impact of Worker and Establishment-level Characteristics on Male-Female Wage Differentials: Evidence from Danish Matched Employee-Employer Data," LABOUR, CEIS, vol. 19(1), pages 1-34, March.
    4. Kimberly Bayard & Judith Hellerstein & David Neumark & Kenneth Troske, 2003. "New Evidence on Sex Segregation and Sex Differences in Wages from Matched Employee-Employer Data," Journal of Labor Economics, University of Chicago Press, vol. 21(4), pages 887-922, October.
    5. Altonji, Joseph G. & Blank, Rebecca M., 1999. "Race and gender in the labor market," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 48, pages 3143-3259 Elsevier.
    6. Davis, Peter, 2002. "Estimating multi-way error components models with unbalanced data structures," Journal of Econometrics, Elsevier, vol. 106(1), pages 67-95, January.
    7. Erica L. Groshen, 1991. "The Structure of the Female/Male Wage Differential: Is It Who You Are, What You Do, or Where You Work?," Journal of Human Resources, University of Wisconsin Press, vol. 26(3), pages 457-472.
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    Cited by:

    1. Korkeamaki, Ossi & Kyyra, Tomi, 2006. "A gender wage gap decomposition for matched employer-employee data," Labour Economics, Elsevier, vol. 13(5), pages 611-638, October.


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