On the estimation of fuzzy poverty indices

TL;DR

This paper reviews fuzzy poverty indices, compares different membership functions, and investigates their estimation accuracy and robustness through simulations, focusing on mean squared error estimation.

Contribution

It provides a comparative analysis of fuzzy poverty indices and explores their estimation accuracy and robustness, highlighting which indices are more reliably estimated from sample data.

Findings

Certain fuzzy indices can be estimated more accurately using sample data.

Membership function parameters significantly affect mean squared error estimation.

Simulation results demonstrate robustness of specific indices under parameter variations.

Abstract

We review the fuzzy approach to poverty measurement by comparing poverty indices using different membership functions proposed in the literature. We put our main focus on the issue of estimation of the mean squared errors of these fuzzy methods showing which indices can be more accurately estimated using sample data. By means of simulations, we also investigate the role of parameters of the membership function when it comes to estimating mean squared errors via a robustness analysis.