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

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  • Francis Bilson Darku

    (Mendoza College of Business, University of Notre Dame, Notre Dame, IN 46556, USA
    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.)

  • Frank Konietschke

    (Institute of Biometry and Clinical Epidemiology, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
    Berlin Institute of Health, Anna-Louisa-Karsch-Straße 2, 10178 Berlin, Germany)

  • Bhargab Chattopadhyay

    (Department of Decision Sciences and Information Systems, Indian Institute of Management Visakhapatnam, Visakhapatnam, Andhra Pradesh 530003, India)

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.

Suggested Citation

  • Francis Bilson Darku & Frank Konietschke & Bhargab Chattopadhyay, 2020. "Gini Index Estimation within Pre-Specified Error Bound: Application to Indian Household Survey Data," Econometrics, MDPI, vol. 8(2), pages 1-20, June.
  • Handle: RePEc:gam:jecnmx:v:8:y:2020:i:2:p:26-:d:373323
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    References listed on IDEAS

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