Counterfactual decomposition of changes in wage distributions using quantile regression

TLDR

The study proposes a method to decompose wage distribution changes over time into contributing factors. Using quantile regression, the authors estimate marginal wage distributions consistent with the conditional distribution and hypothesized covariate distributions, then compare these to perform counterfactual exercises that separate changes in worker characteristics from changes in returns. The method reveals that rising educational attainment was the primary driver of increased wage inequality in Portugal between 1986 and 1995. © 2005 John Wiley & Sons, Ltd.

Abstract

Abstract We propose a method to decompose the changes in the wage distribution over a period of time in several factors contributing to those changes. The method is based on the estimation of marginal wage distributions consistent with a conditional distribution estimated by quantile regression as well as with any hypothesized distribution for the covariates. Comparing the marginal distributions implied by different distributions for the covariates, one is then able to perform counterfactual exercises. The proposed methodology enables the identification of the sources of the increased wage inequality observed in most countries. Specifically, it decomposes the changes in the wage distribution over a period of time into several factors contributing to those changes, namely by discriminating between changes in the characteristics of the working population and changes in the returns to these characteristics. We apply this methodology to Portuguese data for the period 1986–1995, and find that the observed increase in educational levels contributed decisively towards greater wage inequality. Copyright © 2005 John Wiley & Sons, Ltd.