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Box–Cox power transformation unconditional quantile regressions with an application on wage inequality
Journal of Applied Statistics ( IF 1.5 ) Pub Date : 2020-07-21 , DOI: 10.1080/02664763.2020.1795817
Pallab Kumar Ghosh 1
Affiliation  

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.



中文翻译:

Box-Cox 幂变换无条件分位数回归与工资不平等的应用

本研究提出了一种半参数估计方法Box-Cox幂变换无条件分位数回归,以估计解释变量分布变化对结果变量无条件分位数的影响。所提出的方法包括在解释变量上运行结果变量的中心影响函数 (RIF) 的非线性回归。我们还展示了所提出的估计量的渐近特性,并应用估计方法来解决劳动经济学中的一个现有难题——为什么自 1980 年代后期以来美国 50%/10% 的工资差距一直在下降。我们的研究结果表明,工会组织的减少可以解释 1990-2000 年 50/10 工资差距缩小的大约 10% 和 2000-2010 年 23% 的缩小。

更新日期:2020-07-21
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