The use of ICTs and income distribution in Brazil: A machine learning explanation using SHAP values

• Published in: Telecommunications policy 2023-09, Vol.47 (8), p.102598, Article 102598
• Main Authors: Herrera, Gabriel Paes, Constantino, Michel, Su, Jen-Je, Naranpanawa, Athula
• Format: Article
• Language: English
• Subjects: Digital divide; Gender gap; Inequality; Neural network; XGBoost
• ISSN: 0308-5961; 1879-3258
• DOI: 10.1016/j.telpol.2023.102598

This study explores the complex relationship between information and communication technologies (ICTs) and socioeconomic characteristics. We employ a cutting-edge explainable machine learning approach, known as SHAP values, to interpret an XGBoost and neural network model, as well as benchmark traditional econometric methods. The application of machine learning algorithms combined with the SHAP methodology reveals complex nonlinear relationships in the data and important insights to guide tailored policy-making. Our results suggest that there is an interaction between education and ICTs that contributes to income prediction. Furthermore, level of education and age are found to be positively associated with income, while gender presents a negative relationship; that is, women earn less than men on average. This study highlights the need for more efficient public policies to fight gender inequality in Brazil. It is also important to introduce policies that promote quality education and the teaching of skills related to technology and digitalization to prepare individuals for changes in the job market and avoid the digital divide and increasing social inequality. •A machine learning perspective on the link between ICTs and income distribution.•SHAP values is used to interpret machine learning and reveal nonlinear patterns.•The results confirm the gender inequality issue existent in the country.•There is a nonlinear relationship between education and ICTs that impact income.•Policies are needed to fight gender inequality and to prevent the digital divide.