Gender wage gap: A semi-parametric approach with sample selection correction

M. Picchio, C. Mussida

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)

Abstract

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. We propose a 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. We find that, when sample selection is taken into account, the gender wage gap widens, especially at the bottom of the wage distribution.
Original languageEnglish
Pages (from-to)564-578
JournalLabour Economics
Volume18
Issue number5
DOIs
Publication statusPublished - 2011

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