Decomposition of the gender wage gap using the LASSO estimator - René Böheim; Philipp Stöllinger (Johannes Kepler University of Linz, Department of Economics, 2020)
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.
Keywords: J31; J71; 330; gender wage gap; LASSO; decomposition