Saeid Tahmasebi

The issue of income and wealth inequality and the analysis of its indices have always been a central focus for economic researchers. In this study, we investigate the application of cumulative entropy as a concept from information theory in the analysis of income inequality. Additionally, we demonstrate how cumulative entropy can be integrated with well-known income inequality indices to provide novel perspectives on income inequality. Income inequality analysis in this research is conducted based on indices such as the income gap ratio, the Lorenz curve, the Gini coefficient, the Bonferroni curve, the Zenga curve, and the Canberra index. We clarify the relationships between these income inequality indices and cumulative entropy, showing that cumulative entropy can function both as an independent inequality measure and as a tool for quantifying the uncertainty associated with these measurements.