Asymptotically Optimal Sequential Confidence Interval for the Gini Index Under Complex Household Survey Design with Sub-Stratification

TL;DR

This paper develops and analyzes sequential confidence interval procedures for estimating the Gini index in complex survey designs, ensuring asymptotic optimality and practical applicability.

Contribution

It introduces two new sequential estimation procedures for the Gini index under complex survey designs with theoretical guarantees of efficiency and consistency.

Findings

Procedures achieve asymptotic efficiency and consistency.

Simulation confirms optimality across various settings.

Empirical application demonstrates practical utility.

Abstract

We examine the optimality properties of the Gini index estimator under complex survey design involving stratification, clustering, and sub-stratification. While Darku et al. (Econometrics, 26, 2020) considered only stratification and clustering and did not provide theoretical guarantees, this study addresses these limitations by proposing two procedures - a purely sequential method and a two-stage method. Under suitable regularity conditions, we establish uniform continuity in probability for the proposed estimator, thereby contributing to the development of random central limit theorems under sequential sampling frameworks. Furthermore, we show that the resulting procedures satisfy both asymptotic first-order efficiency and asymptotic consistency. Simulation results demonstrate that the proposed procedures achieve the desired optimality properties across diverse settings. The practical utility of the methodology is further illustrated through an empirical application using data collected by the National Sample Survey agency of India