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Determining the gender wage gap through causal inference and machine learning models: evidence from Chile

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Abstract

In the last decades, there has been increasing awareness of the different types of inequalities that women experience. A very important inequality is the wage gap. Understanding the elements that affect this gap is crucial in order for governments to take the right actions to diminish the gap. It is also important to understand the broader context in which this inequality has evolved over time. In this paper, we develop a causal inference model based on the ideas of Potential Outcome (PO) and Metalearners (ML) to address this important issue. We include a time variable in the causal analysis which helps to determine how the effects have evolved over the last decades. We apply data from 1990 to 2017 from the official government social survey of Chile to fit the models. We then make a deep analysis of each variable using the SHAP framework to see the impact of each variable on the gender wage gap. Sadly, our results indicate that there has been a gap between the earnings of men and women over the last three decades, and the gap actually widened over time. We also find that variable decomposition helps to clarify the different effects as some variables clearly help to diminish this gap. Our results may assist the government of Chile and other organizations to endorse policies that may reduce the gap.

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Data availability

All the data were downloaded and are available from: a) URL:http://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen-2017. Note that the url changes with the year.

Notes

  1. http://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen-2017 Note that the url varies with the year. The UF is an inflation-adjusted measure of the purchasing power of the Chilean peso. It was worth about 40 USD in early 2022.

  2. http://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen-2017 Note that the url changes with the year.

  3. https://www.mop.cl/CentrodeDocumentacion/Documents/Investigacionesyestudios/10aosdeinfraestructura1990-1999.pdf.

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Acknowledgements

This study was supported by ANID Fondecyt 1200555 fund.

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Authors and Affiliations

  1. Departamento de Industrias, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Werner Kristjanpoller & Kevin Michell

  2. Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, USA

    Josephine E. Olson

Authors
  1. Werner Kristjanpoller
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  2. Kevin Michell
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  3. Josephine E. Olson
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Correspondence to Werner Kristjanpoller.

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Appendix

Appendix

1.1 Description of categorical features

See Tables 4, 5, 6, 7, 8, 9 and 10.

Table 4 Industry description table
Full size table
Table 5 Occupation description table
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Table 6 Hierarchy description table
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Table 7 Firm size description table
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Table 8 Macro Zone description table
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1.2 Resulting variables after drop** for better performance

Table 9 Final variables (considering one-hot encoder) for the analysis 1990 - 2003
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Table 10 Final variables (considering one-hot encoder) for the analysis 2006 - 2017
Full size table

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Kristjanpoller, W., Michell, K. & Olson, J.E. Determining the gender wage gap through causal inference and machine learning models: evidence from Chile. Neural Comput & Applic 35, 9841–9863 (2023). https://doi.org/10.1007/s00521-023-08221-9

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