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

By Matteo Picchio, Chiara Mussida. Social Science Research Network, 2011

55 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/b938aae61e3bcf91e9049825f3974d30590440cd

Citations in the corpus

• Quantile Selection Models with an Application to Understanding Changes in Wage Inequality (2017, unknown): “[58] Picchio, M.”
• Labor Market Policy: A Comparative View on the Costs and Benefits of Labor Market Flexibility (2010, unknown): “For example, several authors have reported gender pay gaps for Italy ranging from 4% to 6% over the 1995-2005 period such as: Olivetti and Petrongolo (2008), p. 632; Arulampalam, Booth and Bryan (2007), p. 166; Picchio and Mussida (2010), p. 13; and OECD (2008), p. 146.”