Decomposing quantile wage gaps : A conditional likelihood approach.

Philippe Van Kerm, Chung Choe, Seunghee Yu

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Résumé

The paper develops a parametric variant of the Machado?Mata simulation methodology to examine quantile wage differences between groups of workers, with an application to the wage gap between native and foreign workers in Luxembourg. Relying on conditional likelihood-based 'parametric quantile regression' in place of the standard linear quantile regression is parsimonious and cuts computing time drastically with no loss in the accuracy of marginal quantile simulations in our application.We find that the native worker advantage is a concave function of quantile: the advantage is small (possibly negative) for both low and high quantiles, but it is large for the middle half of the quantile range (between the 20th and 70th native wage percentiles).
langue originaleFrançais
Pages (de - à)507-527
Nombre de pages0
journalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume65
Les DOIs
étatPublié - 1 janv. 2016

mots-clés

  • Conditional likelihood
  • Cross-border workers
  • Dagum distribution
  • Distribution regression
  • Immigrant wages
  • Quantile regression
  • Singh-Maddala distribution

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