A1164
Title: High-dimensional panel expectiles regression: A decomposition of the gender wage gap
Authors: Pedro Raposo - Catolica Lisbon school of business and economics (Portugal) [presenting]
Paulo Rodrigues - Universidade Nova de Lisboa (Portugal)
Pedro Portugal - Banco de Portugal (Portugal)
Matei Demetrescu - CAU Kiel (Germany)
Abstract: The aim is to develop expectile panel data methods for high-dimensional fixed effects estimation in line with a prior study, which allows for a wide range of applications in fields such as Labor economics, Economics of Education, and Inequality. It is shown how the Gelbach decomposition can be validly implemented in the context of panel expectile regressions. Using a unique Portuguese-linked employer-employee dataset, use the estimator to explore the determinants of the gender wage gap over the period 1995-2022. It is found that: (i) the gender wage gap is larger in the upper tail; (ii) the difference is mainly explained both in the left and right tail by the individual unobserved heterogeneity; and (iii) assortative matching is less pronounced in the tails.
