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Education and wage differentials by gender in Italy

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  • Tindara Addabbo
  • Donata Favaro

Abstract

In this paper we evaluate wage differentials in Italy combining gender and education perspectives. The main goal of the article is to verify whether the extent of the gender pay gap varies between highly- and low-educated workers, and whether or not the role played by gender differences in characteristics and in market rewards is similar in the two groups. We apply quantile regression analysis and an adaptation of the procedure suggested by Machado and Mata (2005) to evaluate the predicted wage gap at different levels of education, at different points of the female wage distribution scale. The analysis is carried out on the Italian sample of the last available year of the European Community Household Panel (2001). We show that the extent and the trend of the gap predicted across the female distribution is sharply different between groups with diverse educational levels. In the case of low-educated workers, although the predicted gap is largely explained by differences in rewards, lower levels of education or experience are however responsible for the gap, especially on the right-hand side of the distribution. On the contrary, highly-educated females have better characteristics than highly-educated men that partially compensate the rather high difference in returns, in particular at the extremes of the distribution. It thus follows that the unexplained part of the predicted gap reveals a glass ceiling effect only for more highly-educated females.

Suggested Citation

  • Tindara Addabbo & Donata Favaro, 2009. "Education and wage differentials by gender in Italy," Center for Economic Research (RECent) 036, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
  • Handle: RePEc:mod:recent:036
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    Cited by:

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    4. Noe', Chiara, 2009. "Subject of degree and the gender wage gap: Evidence from Italy," MPRA Paper 47289, University Library of Munich, Germany.

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    More about this item

    Keywords

    human capital; gender wage differentials; quantile regressions;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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