Decomposition of the gender wage gap using the LASSO estimator

René Böheim and Philipp Stöllinger

Applied Economics Letters, 2021, vol. 28, issue 10, 817-828

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 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.

Date: 2021
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Working Paper: Decomposition of the Gender Wage Gap using the LASSO Estimator (2020)
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DOI: 10.1080/13504851.2020.1782332

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