Box-Cox 幂变换无条件分位数回归及其在工资不平等问题上的应用
Box-Cox power transformation unconditional quantile regressions with an application on wage inequality.
作者信息
Ghosh Pallab Kumar
机构信息
Department of Economics, University of Oklahoma, Norman, OK, USA.
出版信息
J Appl Stat. 2020 Jul 21;48(16):3086-3101. doi: 10.1080/02664763.2020.1795817. eCollection 2021.
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.
本研究提出了一种半参数估计方法，即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。所提出的方法包括对结果变量的重新中心化影响函数（RIF）与解释变量进行非线性回归。我们还展示了所提出估计量的渐近性质，并应用该估计方法解决劳动经济学中一个现有的谜题——自20世纪80年代末以来，美国第50/10百分位工资差距为何一直在下降。我们的结果表明，工会化程度下降可以解释1990 - 2000年50/10工资差距下降的约10%，以及2000 - 2010年的23%。
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