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Gender differences in productivity rewards in Italy: the role of human capital


  • Tindara Addabbo

    (Department of Economics, University of Modena and Reggio Emilia)

  • Donata Favaro

    () (Department of Economics, University of Padova)

  • Stefano Magrini

    () (Department of Economics, University Of Venice C� Foscari)


In this paper we evaluate the gender wage gap component due to differences in characteristics� rewards in Italy. The main focus is on the relationship between human capital characteristics and gender differences in rewards. We propose a methodology that combines the quantile regression analysis with non-parametric procedures for the estimation of the probability density functions of reward differentials in order to evaluate the evolution of the gap due to human capital characteristics. The analysis is carried out on Italian data taken from the latest available cross-section of the European Community Household Panel (2001). Our study suggests that education can be a good productivity signal and helps reduce the range of the gap; furthermore, highly-educated women experience lesser gender-based pay differences as the length of the employment relationship increases.

Suggested Citation

  • Tindara Addabbo & Donata Favaro & Stefano Magrini, 2010. "Gender differences in productivity rewards in Italy: the role of human capital," Working Papers 2010_11, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2010_11

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    References listed on IDEAS

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    More about this item


    Gender wage gap; human capital; distributional analysis;

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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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