Breaking down the Gender Pay Gap through a machine learning model
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Autónoma del Estado de México
2023
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| _version_ | 1876459538357420032 |
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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 |