Breaking down the Gender Pay Gap through a machine learning model

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Auteur principal: Valeria Carolina Edelsztein
Format: Artículo científico
Langue:en
Publié: Universidad Autónoma del Estado de México 2023
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author Valeria Carolina Edelsztein
author_facet Valeria Carolina Edelsztein
contents Breaking down the Gender Pay Gap through a machine learning model Valeria Carolina Edelsztein Sebastián Ariel Waisbrot Sociología women’s labor Gender wage gap machine learning wage disparities force participation 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. 2023 artículo científico 1405-1435 https://www.redalyc.org/articulo.oa?id=10574559005 https://www.redalyc.org/journal/105/10574559005/ https://www.redalyc.org/journal/105/10574559005/html/ https://www.redalyc.org/journal/105/10574559005/10574559005.epub https://www.redalyc.org/journal/105/10574559005/movil 10.29101/crcs.v30i0.20656 en http://www.redalyc.org/revista.oa?id=105 Convergencia. Revista de Ciencias Sociales application/pdf Universidad Autónoma del Estado de México Convergencia. Revista de Ciencias Sociales (México) Vol.30
format Artículo científico
id redalyc_10574559005
institution Redalyc
language en
publishDate 2023
publisher Universidad Autónoma del Estado de México
spellingShingle Breaking down the Gender Pay Gap through a machine learning model
Valeria Carolina Edelsztein
Sociología
women’s labor
Gender wage gap
machine learning
wage disparities
force participation
Breaking down the Gender Pay Gap through a machine learning model Valeria Carolina Edelsztein Sebastián Ariel Waisbrot Sociología women’s labor Gender wage gap machine learning wage disparities force participation 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. 2023 artículo científico 1405-1435 https://www.redalyc.org/articulo.oa?id=10574559005 https://www.redalyc.org/journal/105/10574559005/ https://www.redalyc.org/journal/105/10574559005/html/ https://www.redalyc.org/journal/105/10574559005/10574559005.epub https://www.redalyc.org/journal/105/10574559005/movil 10.29101/crcs.v30i0.20656 en http://www.redalyc.org/revista.oa?id=105 Convergencia. Revista de Ciencias Sociales application/pdf Universidad Autónoma del Estado de México Convergencia. Revista de Ciencias Sociales (México) Vol.30
title Breaking down the Gender Pay Gap through a machine learning model
topic Sociología
women’s labor
Gender wage gap
machine learning
wage disparities
force participation
url https://www.redalyc.org/articulo.oa?id=10574559005
https://www.redalyc.org/journal/105/10574559005/
https://www.redalyc.org/journal/105/10574559005/html/
https://www.redalyc.org/journal/105/10574559005/10574559005.epub
https://www.redalyc.org/journal/105/10574559005/movil