Resumo

EDELSZTEIN, Valeria Carolina  e  WAISBROT, Sebastián Ariel. Breaking down the Gender Pay Gap through a machine learning model. Convergencia [online]. 2023, vol.30, e20656.  Epub 28-Jun-2023. ISSN 2448-5799.  https://doi.org/10.29101/crcs.v30i0.20656.

Being able to decompose the gender pay gap (GPG) and determine the contribution of each component is important to design appropriate policies to reduce it. With the aim of providing a new tool to achieve this, in this paper, we propose a decomposition approach based on a machine learning model. The tool was implemented on a population of 5 742 Argentinean IT-related workers to obtain the value of the adjusted and unadjusted GPG in a four-phase process: sample characterization, development of a wage predictor, calculation of adjusted GPG, and analysis of the explained component of GPG. According to our analysis, there is a GPG of 20%, 7,7% of which can be explained exclusively by direct discrimination while 12,3% can be ascribed to other factors, such as total years of experience, educational level, and number of people in charge.

Palavras-chave : gender wage gap; labor market discrimination; machine learning; women’s labor-force participation; wage disparities.