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Gini Index Estimation within Pre-Specified Error Bound: Application to Indian Household Survey Data

1
Mendoza College of Business, University of Notre Dame, Notre Dame, IN 46556, USA
2
Institute of Biometry and Clinical Epidemiology, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
3
Berlin Institute of Health, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany
4
Department of Decision Sciences and Information Systems, Indian Institute of Management Visakhapatnam, Visakhapatnam, Andhra Pradesh 530003, India
*
Author to whom correspondence should be addressed.
This work is part of the final dissertation of Francis Bilson Darku that was submitted to the Department of Mathematical Sciences at The University of Texas at Dallas.
Econometrics 2020, 8(2), 26; https://doi.org/10.3390/econometrics8020026
Received: 23 July 2019 / Revised: 11 June 2020 / Accepted: 12 June 2020 / Published: 18 June 2020
The Gini index, a widely used economic inequality measure, is computed using data whose designs involve clustering and stratification, generally known as complex household surveys. Under complex household survey, we develop two novel procedures for estimating Gini index with a pre-specified error bound and confidence level. The two proposed approaches are based on the concept of sequential analysis which is known to be economical in the sense of obtaining an optimal cluster size which reduces project cost (that is total sampling cost) thereby achieving the pre-specified error bound and the confidence level under reasonable assumptions. Some large sample properties of the proposed procedures are examined without assuming any specific distribution. Empirical illustrations of both procedures are provided using the consumption expenditure data obtained by National Sample Survey (NSS) Organization in India. View Full-Text
Keywords: complex household survey; confidence interval; income distribution; inequality; sequential analysis complex household survey; confidence interval; income distribution; inequality; sequential analysis
MDPI and ACS Style

Bilson Darku, F.; Konietschke, F.; Chattopadhyay, B. Gini Index Estimation within Pre-Specified Error Bound: Application to Indian Household Survey Data. Econometrics 2020, 8, 26.

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