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

Picchio, Matteo;Mussida, Chiara
(2010) , 31 pages

Files

2010005.pdf
  • Open Access
  • Adobe PDF
  • 1.19 MB
Download

Details

Authors
  • Picchio, Matteo
    Author
  • Mussida, Chiara
    Author
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. 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.
Affiliations
  • Tilburg UniversityDepartment of Economics, CentER, ReflecT
  • Università Cattolica, MilanDepartment of Economics and Social Sciences

Citations

  • APA
  • Chicago
  • FWB

Picchio, M., & Mussida, C. (2010). Gender Wage Gap : A Semi-parametric Approach with Sample Selection Correction (IRES Discussion papers 2010005). https://hdl.handle.net/2078.5/250623