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Box–Cox power transformation unconditional quantile regressions with an application on wage inequality

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  • Pallab Kumar Ghosh

Abstract

This study proposes a semi-parametric estimation method, Box–Cox power transformation unconditional quantile regression, to estimate the impact of changes in the distribution of the explanatory variables on the unconditional quantile of the outcome variable. The proposed method consists of running a nonlinear regression of the recentered influence function (RIF) of the outcome variable on the explanatory variables. We also show the asymptotic properties of the proposed estimator and apply the estimation method to address an existing puzzle in labor economics–why the 50th/10th percentile wage gap has been falling in the USA since the late 1980s. Our results show that declining unionization can explain approximately 10% of the decline in the 50/10 wage gap in 1990–2000 and 23% in 2000–2010.

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  • Pallab Kumar Ghosh, 2021. "Box–Cox power transformation unconditional quantile regressions with an application on wage inequality," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(16), pages 3086-3101, December.
    • Handle: RePEc:taf:japsta:v:48:y:2021:i:16:p:3086-3101
    DOI: 10.1080/02664763.2020.1795817
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