Breaking down the Gender Pay Gap through a machine learning model
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jun 28, 2023
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https://doi.org/10.29101/crcs.v30i0.20656
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Valeria Carolina Edelsztein
http://orcid.org/0000-0001-6739-1825
Sebastián Ariel Waisbrot
http://orcid.org/0000-0002-8056-4150
http://orcid.org/0000-0001-6739-1825
Sebastián Ariel Waisbrot
http://orcid.org/0000-0002-8056-4150
Resumen
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.
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Como citar
EDELSZTEIN, Valeria Carolina; WAISBROT, Sebastián Ariel.
Breaking down the Gender Pay Gap through a machine learning model.
Convergencia Revista de Ciencias Sociales, [S.l.], p. 1-29, jun. 2023.
ISSN 2448-5799.
Disponible en: <https://convergencia.uaemex.mx/article/view/20656>. Fecha de acceso: 18 ago. 2026
doi: https://doi.org/10.29101/crcs.v30i0.20656.
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Citas
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Alatrista-Salas, Hugo et al. (2017), “Measuring the gender discrimination: A machine learning approach”, en 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI). 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI). Doi: https://doi.org/10.1109/LA-CCI.2017.8285682 Disponible en: https://ieeexplore.ieee.org/document/8285682 [20 de enero de 2023].
Amado, Carla et al. (2018), “Measuring and decomposing the gender pay gap: A new frontier approach”, en European Journal of Operational Research, núm. 1, Holanda: Elsevier.
Becker, Gary (1971), The Economics of Discrimination, Estados Unidos: University of Chicago Press.
Becker, Gary (1985), “Human Capital, Effort, and the Sexual Division of Labor”, en Journal of Labor Economics, núm. 1, Estados Unidos: University of Chicago Press.
Bergmann, Barbara (1974), “Occupational Segregation, Wages and Profits When Employers Discriminate by Race or Sex”, en Eastern Economic Journal, núm. 2, Alemania: Springer.
Bishu, Sebawit y Alkadry, Mohamad (2016), “A Systematic Review of the Gender Pay Gap and Factors That Predict It”, en Administration and Society, núm. 49, vol. 1, Estados Unidos: Sage.
Blau, Francine y Kahn, Lawrence (2017), “The Gender Wage Gap: Extent, Trends, and Explanations”, en Journal of Economic Literature, núm. 55, vol. 3, Estados Unidos: American Economic Association.
Bonaccolto-Töpfer, Marina y Briel, Stephanie (2022), “The gender pay gap revisited: Does machine learning offer new insights?”, en Labour Economics, 78. Doi: https://doi.org/10.1016/j.labeco.2022.102223 Disponible en: https://www.sciencedirect.com/science/article/abs/pii/S0927537122001130?via%3Dihub [20 de enero de 2023].
Chernozhukov, Víctor et al. (2013), “Inference on Counterfactual Distributions”, en Econometrica, núm. 81, vol. 6, Estados Unidos: Wiley.
Ciminelli, Gabriele et al. (2021), “Sticky floors or glass ceilings? The role of human capital, working time flexibility and discrimination in the gender wage gap”, en OECD Economics Department Working Papers, núm. 1668, Francia: Organisation for Economic Co-operation and Development (OECD).
D’Alessandro, Mercedes et al. (2020), “Los cuidados, un sector económico estratégico. Medición del aporte del Trabajo doméstico y de cuidados no remunerado al Producto Interno Bruto”. Disponible en: https://observatorio.senadoer.gob.ar/materiales/material/los-cuidados-un-sector-economico-estrategico-medicion-del-aporte-del-trabajo-domestico-y-de-cuidados-no-remunerado-al-producto-interno-bruto/ [20 de enero de 2023].
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European Commission. Statistical Office of the European Union (2018), A decomposition of the unadjusted gender pay gap using structure of earnings survey data: 2018 edition. Doi: 10.2785/796328. Disponible en: https://op.europa.eu/en/publication-detail/-/publication/fb389f61-6f7c-11e8-9483-01aa75ed71a1/language-en [20 de enero de 2023].
Fontenot, Kayla et al. (2018), “Income and Poverty in the United States: 2017”, en United States, Census Bureau. Disponible en: https://www.census.gov/library/publications/2018/demo/p60-263.html [20 de enero de 2023].
Fortin, Nichole et al. (2011), “Chapter 1 - Decomposition Methods in Economics”, en Ashenfelter, Orley y Card, David eds., Handbook of Labor Economics, Holanda: Elsevier.
Goldin, Claudia (2006), “The Quiet Revolution That Transformed Women’s Employment, Education, and Family”, en American Economic Review, vol. 96, núm. 2, Estados Unidos: American Economic Association.
Goldin, Claudia (2014), “A Grand Gender Convergence: Its Last Chapter”, en The American Economic Review, vol. 104, núm. 4, Estados Unidos: American Economic Association.
Grybaitė, Virginija (2006), “Analysis of theoretical approaches to gender pay gap”, en Journal of Business Economics and Management, vol. 7, núm. 2, Reino Unido: Taylor & Francis.
International Labour Organization (2018), “Global Wage Report 2018/19: What lies behind gender pay gaps”. Disponible en: http://www.ilo.org/global/publications/books/WCMS_650553/lang--en/index.htm [20 de enero de 2023].
Karamessini, Maria y Ioakimoglou, Elias (2007), “Wage determination and the gender pay gap: A feminist political economy analysis and decomposition”, en Feminist Economics, vol. 13, núm. 1, Reino Unido: Taylor & Francis.
Karimian, Hamid et al. (2019), “A Machine Learning Framework to Identify Employees at Risk of Wage Inequality: U.S. Department of Transportation Case Study”, en The American Economic Review, vol. 73, núm. 3, Estados Unidos: American Economic Association.
Mandel, Hadas y Semyonov, Moshe (2014), “Gender pay gap and employment sector: sources of earnings disparities in the United States, 1970-2010”, en Demography, vol. 51, núm. 5, Alemania: Springer.
Mincer, Jacob y Polachek, Solomon (1974), “Family Investments in Human Capital: Earnings of Women”, en Journal of Political Economy, vol. 82, núm. 2, Estados Unidos: The University of Chicago Press.
Organisation for Economic Co-operation and Development (OECD) (2021), “Earnings and wages - Gender wage gap - OECD Data, OECD data - Gender wage gap”. Disponible en: http://data.oecd.org/earnwage/gender-wage-gap.htm [20 de enero de 2023].
Ospino, Carlos et al. (2010), “Oaxaca-Blinder wage decomposition: Methods, critiques and applications. A literature review”, en Revista de Economía del Caribe, núm. 5, Colombia: Editorial Universidad del Norte.
Phelps, Edmund (1972), “The Statistical Theory of Racism and Sexism”, en The American Economic Review, vol. 62, núm. 4, Estados Unidos: American Economic Association.
Polachek, Solomon (1981), “Occupational Self-Selection: A Human Capital Approach to Sex Differences in Occupational Structure”, en The Review of Economics and Statistics, vol. 63, núm. 1, Estados Unidos: MIT Press.
Qin, Joe y Chiang, Leo (2019), “Advances and opportunities in machine learning for process data analytics”, en Computers & Chemical Engineering, núm. 126. Doi: https://doi.org/10.1016/j.compchemeng.2019.04.003 Disponible en: https://www.sciencedirect.com/science/article/abs/pii/S0098135419302248?via%3Dihub [20 de enero de 2023].
Sysarmy (2020), “Resultados de la Encuesta de sueldos 2020.1, sysarmy”. Disponible en: https://sysarmy.com/blog/posts/resultados-de-la-encuesta-de-sueldos-2020-1/ [20 de enero de 2023].
Töpfer, Marina (2017), “Detailed RIF decomposition with selection: The gender pay gap in Italy”, en Hohenheim Discussion Papers in Business, Economics and Social Sciences, vol. 26, Alemania: University of Hohenheim.
Töpfer, Marina y Briel, Stephanie (2022), “The gender pay gap revisited: Does machine learning offer new insights?”, en Labour Economics, núm. 78, Holanda: Elsevier.
United Nations (2020), “The World’s Women 2020. Trends and Statistics”. Disponible en: https://worlds-women-2020-data-undesa.hub.arcgis.com [20 de enero de 2023].
U.S. Bureau of Labor Statistics (2020), “Current Population Survey (CPS) Tables”. Disponible en: https://www.bls.gov/cps/tables.htm [20 de enero de 2023].
Waisbrot, Sebastián (2020), “Estimación Sueldo 2020.1”. Disponible en: https://seppo0010.github.io/sysarmy-sueldos-2020.1 / [20 de enero de 2023].
Waisbrot, Sebastián (2022), “Seppo0010/sysarmy-sueldos-2020.1”. Disponible en: https://github.com/seppo0010/sysarmy-sueldos-2020.1/blob/d9a7c959a033429f669c3a98ef6c278bec192f23/notebook/Brecha%20de%20g%C3%A9nero.ipynb [20 de enero de 2023].
World Economic Forum (2020), “Global Gender Gap Report 2020, World Economic Forum”. Disponible en: https://www.weforum.org/reports/gender-gap-2020-report-100-years-pay-equality/ [20 de enero de 2023].