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Gender Wage Gap : A Semi-parametric Approach with Sample Selection Correction

  • Matteo PICCHIO


    (Tilburg University, Department of Economics, CentER, ReflecT and IZA, Germany)

  • Chiara MUSSIDA


    (Department of Economics and Social Sciences, Università Cattolica, Milan and Prometeia spa, Bologna)

Sizeable gender differences in employment rates are observed in many countries. Sample selection into the workforce might therefore be a relevant issue when estimating gender wage gaps. This paper proposes a new semi-parametric estimator of densities in the presence of covariates which incorporates sample selection. We describe a simulation algorithm to implement counterfactual comparisons of densities. The proposed methodology is used to investigate the gender wage gap in Italy. It is found that when sample selection is taken into account gender wage gap widens, especially at the bottom of the wage distribution. Explanations are offered for this empirical finding.

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Paper provided by Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES) in its series Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) with number 2010005.

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Length: 31
Date of creation: 12 Mar 2010
Date of revision:
Handle: RePEc:ctl:louvir:2010005
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