Measuring inequality: application of semi-parametric methods to real life data

TL;DR

This paper applies semi-parametric methods to real-world inequality data, comparing their performance to non-parametric estimators and providing guidance on tail distribution fitting.

Contribution

It demonstrates the practical application of semi-parametric inequality estimators and compares them with traditional non-parametric methods using real data.

Findings

Semi-parametric estimators perform well for heavy-tailed data

Guidance on selecting parametric tail distributions is provided

Comparison shows advantages over non-parametric estimators in certain cases

Abstract

A number of methods have been introduced in order to measure the inequality in various situations such as income and expenditure. In order to curry out statistical inference, one often needs to estimate the available measures of inequality. Many estimators are available in the literature, the most used ones being the non parametric estimators. kpanzou(2011) has developed semi-parametric estimators for measures of inequality and showed that these are very appropriate especially for heavy tailed distributions. In this paper we apply such semi-parametric methods to a practical data set and show how they compare to the non parametric estimators. A guidance is also given on the choice of parametric distributions to fit in the tails of the data