Decomposition of the gender wage gap using the LASSO estimator

被引：7

作者：

Boeheim, Rene ^{[1
]}

Stoellinger, Philipp ^{[2
]}

机构：

[1] Johannes Kepler Univ Linz, Dept Econ, Linz, Austria

[2] Vienna Univ Econ & Business, Dept Econ, Vienna, Austria

来源：

APPLIED ECONOMICS LETTERS | 2021年 / 28卷 / 10期

关键词：

Gender wage gap; LASSO; decomposition; MODEL SELECTION; REGRESSION; DISCRIMINATION; INFERENCE;

D O I：

10.1080/13504851.2020.1782332

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We use the LASSO estimator to select among a large number of explanatory variables in wage regressions for a decomposition of the gender wage gap. The LASSO selection with a one standard error rule removes about a quarter of the regressors. We use the LASSO-selected regressors for OLS-based gender wage decompositions. This approach results in a smaller error variance than in OLS without LASSO-selection. The explained gender wage gap is 1%-point greater than in the conventional OLS model.

引用

页码：817 / 828

页数：12

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