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

Francis Bilson Darku (), Frank Konietschke () and Bhargab Chattopadhyay ()
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Francis Bilson Darku: Mendoza College of Business, University of Notre Dame, Notre Dame, IN 46556, USA
Frank Konietschke: Institute of Biometry and Clinical Epidemiology, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
Bhargab Chattopadhyay: Department of Decision Sciences and Information Systems, Indian Institute of Management Visakhapatnam, Visakhapatnam, Andhra Pradesh 530003, India

Econometrics, 2020, vol. 8, issue 2, 1-20

Abstract: 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.

Keywords: complex household survey; confidence interval; income distribution; inequality; sequential analysis (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2020
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