Confidence sets for dynamic poverty indexes

TL;DR

This paper extends dynamic poverty indexes by including within-class inequality and establishes confidence sets using a central limit theorem, demonstrated through Italian income data from 1998 to 2012.

Contribution

It introduces an extended computation of the dynamic Gini and Sen indexes incorporating within-class inequality and derives their confidence sets.

Findings

Effective confidence sets for dynamic poverty indexes are constructed.

Application to Italian data shows the evolution of poverty and inequality.

Method proves useful for analyzing real economic data.

Abstract

In this study, we extend the research on the dynamic poverty indexes, namely the dynamic Headcount ratio, the dynamic income-gap ratio, the dynamic Gini and the dynamic Sen, proposed in D'Amico and Regnault (2018). The contribution is twofold. First, we extend the computation of the dynamic Gini index, thus the Sen index accordingly, with the inclusion of the inequality within each class of poverty where people are classified according to their income. Second, for each poverty index, we establish a central limit theorem that gives us the possibility to determine the confidence sets. An application to the Italian income data from 1998 to 2012 confirms the effectiveness of the considered approach and the possibility to determine the evolution of poverty and inequality in real economies.