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

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.