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Breaking down the Gender Pay Gap through a machine learning model.
- Source :
-
Convergencia: Revista de Ciencias Sociales . 2023, Vol. 30, p1-29. 29p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14051435
- Volume :
- 30
- Database :
- Academic Search Index
- Journal :
- Convergencia: Revista de Ciencias Sociales
- Publication Type :
- Academic Journal
- Accession number :
- 174746235
- Full Text :
- https://doi.org/10.29101/crcs.v30i0.20656