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Gender Wage Gaps in the Netherlands with Sample Selection Adjustments


  • James Albrecht
  • Aico van Vuuren


In this paper, we use quantile regression methods to analyze the gender gap in the Netherlands. Specifically, we use data from the 1992 wave of the OSA Labour Survey Panel to decompose the difference between the distributions of wages for males and females who are employed full-time. The decomposition technique we use is the Machado and Mata (2000) method, as applied in Albrecht, Bj`rklund and Vroman (2003). There is strong evidence of a glass ceiling effect in the Netherlands; i.e., the gender log wage gap is greater for higher quantiles. Because part-time work is common among women in the Netherlands and because the female participation rate is relatively low, sample selection is a serious issue. We apply Buchinsky’s technique for quantile regression with selectivity bias correction and estimate a series of quantile regressions to find the marginal contributions of individual characteristics to log wages for men and for women at various quantiles in their respective wage distributions. We then use the Machado/Mata technique amended to deal with sample selection to construct a counterfactual distribution, namely, the distribution of wages that would prevail among women were women to work full-time to the same extent as men do. This allows us to decompose the gender gap at different quantiles taking account of sample selection and to determine how much of the gap is due to differences in the labor market characteristics of men and women and how much is due to gender differences in rewards to these characteristics

Suggested Citation

  • James Albrecht & Aico van Vuuren, 2004. "Gender Wage Gaps in the Netherlands with Sample Selection Adjustments," Econometric Society 2004 North American Winter Meetings 504, Econometric Society.
  • Handle: RePEc:ecm:nawm04:504

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    References listed on IDEAS

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    Cited by:

    1. Olivier Bargain & Prudence Kwenda, 2009. "The Informal Sector Wage Gap: New Evidence Using Quantile Regressions on Panel Data," CEDI Discussion Paper Series 09-06, Centre for Economic Development and Institutions(CEDI), Brunel University.
    2. Marjan Petreski & Nikica Blazevski & Blagica Petreski, 2014. "Gender Wage Gap when Women are Highly Inactive: Evidence from Repeated Imputations with Macedonian Data," Journal of Labor Research, Springer, vol. 35(4), pages 393-411, December.

    More about this item


    Gender Gap; Quantile Regressions; Sample Selection;

    JEL classification:

    • J0 - Labor and Demographic Economics - - General
    • J7 - Labor and Demographic Economics - - Labor Discrimination


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