The contribution of human capital variables to changes in the wage distribution function

• This paper proposes a modified wage decomposition method.
• I show the asymptotic properties of the proposed decomposition method.
• Empirical results of the proposed method are consistent with the previous studies.
• The proposed decomposition method can also be used in different applications.

This paper proposes a semi-parametric procedure to determine the contribution of human capital variables in explaining the changes of U.S. wage distribution function over the period of 1990–2000. The effects of these factors are estimated by using the Chamberlain's two stage Box–Cox quantile regression approach. One of the main contributions of this paper is that it relaxes the linearity assumption of the conditional quantile function to estimate the counterfactual wage distribution function consistently. This paper also shows that the proposed method provides better estimates of capturing the effects of human capital variables in the two tails of the wage distribution while the results of other parts of the distribution are comparable with the estimates of the previous study which used the linear quantile regression approach. To summarize, these results suggest that this proposed approach is more applicable in cases where the behavior of the tails of the distribution bear much importance.