The gender pay gap in Spain: A machine learning approach
Sánchez Maudo, Ander
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This thesis investigates the gender pay gap in Spain using Machine Learning (ML) techniques to provide insights and predictive models to understand and address this persistent problem. This study uses a large dataset that includes various labor market factors, such as education, jobs, and industry, to train ML models to predict and explain the gender wage gap. Using advanced methodology, the research aims to identify the key drivers of the wage gap and highlight potential areas where gender-based wage discrimination may exist. The results of this analysis indicate that the gender pay gap is between 14% and 16%. This indicates that according to the estimated models, men are paid between 14 and 16% more in Spain. On the other hand, it is not possible to establish the variables that can explain this gender gap, since most of it is unexplained.