Counterfactual decomposition of changes in wage distributions using quantile regression
Counterfactual decomposition of changes in wage distributions using quantile regression
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DOI：
10.1002/jae.788
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发表时间：
2005-05-01
影响因子：
2.1
通讯作者：
Mata, J
中科院分区：
文献类型：
--
作者：
Machado, JAF;Mata, J
我们提出了一种方法，将一段时间内工资分布的变化分解为导致这些变化的几个因素。该方法基于边际工资分布的估计，该估计与分位数回归估计的条件分布以及协变量的任何假设分布一致。比较协变量的不同分布所隐含的边际分布，然后就能够进行反事实练习。所提出的方法能够确定大多数国家观察到的工资不平等加剧的根源。具体来说，它将一段时间内工资分布的变化分解为促成这些变化的几个因素，即区分劳动人口特征的变化和这些特征的回报的变化。我们将这一方法应用于葡萄牙 1986-1995 年期间的数据，发现观察到的教育水平的提高对工资不平等的加剧起到了决定性的作用。版权所有 (c) 2005 John Wiley & Sons, Ltd.
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 (c) 2005 John Wiley & Sons, Ltd.