Approximate Bayesian Computation for Lorenz Curves from Grouped Data

TL;DR

This paper introduces a Bayesian method using Approximate Bayesian Computation to estimate the Gini coefficient from grouped income data, providing improved accuracy and interpretability over existing methods.

Contribution

It develops a novel Bayesian approach leveraging likelihood approximation and sequential Monte Carlo to estimate income inequality measures from grouped data.

Findings

The method accurately estimates Gini coefficients in simulations.

Application to Japanese income data demonstrates practical effectiveness.

The approach outperforms traditional estimation techniques.

Abstract

This paper proposes a new Bayesian approach to estimate the Gini coefficient from the Lorenz curve based on grouped data. The proposed approach assumes a hypothetical income distribution and estimates the parameter by directly working on the likelihood function implied by the Lorenz curve of the income distribution from the grouped data. It inherits the advantages of two existing approaches through which the Gini coefficient can be estimated more accurately and a straightforward interpretation about the underlying income distribution is provided. Since the likelihood function is implicitly defined, the approximate Bayesian computational approach based on the sequential Monte Carlo method is adopted. The usefulness of the proposed approach is illustrated through the simulation study and the Japanese income data.