More than a century ago, Corrado Gini proposed his well-known concentration index for measuring the degree of inequality in the distribution of income and wealth. His index is still extremely relevant and widely used in several fields of research and application. In this paper, we focus on the inferential properties of the Gini index, and discuss the main directions of analysis proposed in the literature. The aim of the paper is to provide a comprehensive review of the main developments on the inferential aspects of the Gini concentration ratio. We feel that this work can provide a valuable contribution to those scholars who are approaching the large amount of literature on the inferential properties of the Gini index.
The Bonferroni index (BI) and Bonferroni curve (BC) have assumed relief not only in economics to study income and poverty, but also in other fields like reliability, demography, insurance and medicine. Besides, the increasingly frequent comparison with the Lorenz curve (LC) and Gini index (GI) both in theoretical and applied studies has driven us to derive explicit expressions for BI, BC, GI and LC for some thirty five continuous distributions. It is expected that these expressions could provide a useful reference and encourage further research within the aforementioned fields
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