Using panel data clustering regression analysis to revisit income inequalities in the European Union

Income inequality has long been a focal point of concern within European Union countries. This paper aims to revisit this topic from a newer methodological framework, employing panel data clustering regression. The iterative partitional algorithm relies entirely on the data when providing the optimal number of clusters and the membership, it allows the estimation of fixed-effects panel models ensuring homogeneity within the cluster, and it highlights the different slope coefficients across clusters. Building upon the stimulating and disturbing main factors identified in the analysis, we discuss possible measures for mitigating income inequality at the EU level, tailored to each resultant cluster. First-cluster countries should prioritize initiatives aimed at enhancing access to public education to alleviate poverty and have better educated people, while public interventions leveraging direct taxes and social transfers are more effective in reducing inequality across the remaining clusters.