A nonparametric decomposition of the Mexican American average wage gap
用非参数方法分解墨西哥裔美国人与非西班牙裔白人之间的平均工资差距，并提出了一个纠正样本选择偏差的算法，发现分解结果对函数假设很敏感。
Abstract This paper shows that average wage gap decompositions between any two groups of workers can be carried out using nonparametric wage structures. It also proposes an algorithm to correct for sample selection in nonparametric models known as tree structures. This paper studies the wage gap between third‐generation Mexican American and non‐Hispanic white workers in the southwest. It is shown that the decomposition heavily depends on functional assumptions, and that different aproaches to flexibility may render sufficiently good and similar results Copyright © 2008 John Wiley & Sons, Ltd.