Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246293 
Year of Publication: 
2020
Series/Report no.: 
Working Paper No. 2003
Publisher: 
Johannes Kepler University of Linz, Department of Economics, Linz
Abstract: 
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 OLSbased 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.
Subjects: 
gender wage gap
LASSO
decomposition
JEL: 
J31
J71
Document Type: 
Working Paper

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