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Explaining Gender Wage Differentials: Findings from a Random Effects Model

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  • Ossi Korkeamäki
  • Tomi Kyyrä

Abstract

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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Bibliographic Info

Paper provided by Government Institute for Economic Research Finland (VATT) in its series Discussion Papers with number 320.

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Date of creation: 18 Dec 2003
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Handle: RePEc:fer:dpaper:320

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Related research

Keywords: Gender wage gap; wage discrimination; sex segregation; random effects model;

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References

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  1. Francine D. Blau & Lawrence M. Kahn, 2000. "Gender Differences in Pay," NBER Working Papers 7732, National Bureau of Economic Research, Inc.
  2. 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.
  3. 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.
  4. Gubta, Nabanita Datta & Rothstein, Donna S., 2001. "The Impact of Worker and Establishment-level Characteristics on Male-Female Wage Differentials: Evidence from Danish Matched Employee-Employer Data," CLS Working Papers 01-9, University of Aarhus, Aarhus School of Business, Centre for Labour Market and Social Research.
  5. Kenneth R Troske & Kimberly N Bayard & Judith Hellerstein & David Neumark, 1998. "New Evidence On Sex Segregation And Sex Differences In Wages From Matched Employee-Employer Data," Working Papers 98-18, Center for Economic Studies, U.S. Census Bureau.
  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. 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.
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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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