Analysis of economic satisfaction using machine learning models and explainable artificial intelligence

The economic satisfaction of a nation can reflect citizens' perceptions of their government's performance, and machine learning models can help uncover non-trivial information from such data. In this context, this article aimed to analyze the satisfaction of Latin American citizens with their country's economy. To achieve this, six traditional classifier algorithms and four ensemble models were used, with a final application of an explainable method (SHapley Additive exPlanations, SHAP) to analyze the key factors contributing to economic satisfaction. The models were trained and tested on a dataset comprising data from the 2020 and 2023 Latinobarómetro surveys, totaling 27,600 instances in the final set. As a result, it was found that the Random Forest was the best individual model, while the stacking ensemble achieved the best performance in classifying between “satisfied” and “dissatisfied” citizens. The SHAP method revealed that “satisfaction with democracy” and “perception of the country's progress” are the main factors influencing economic satisfaction. This study offers insights for public managers on how to improve their citizens' economic satisfaction.