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On Statistical Inference for Inequality Measures Calculated from Complex Survey Data

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Abstract

We examine inference for Generalized Entropy and Atkinson inequality measures with complex survey data, using Wald statistics with variance-covariance matrices estimated from a linearization approximation method. Testing the equality of two or more inequality measures, including sub-group decomposition indices and group shares, are covered. We illustrate with Indian data from three surveys, examining pre-school children’s height, an anthropometric measure that can indicate long-term malnutrition. Sampling involved an urban/rural stratification with clustering before selection of households. We compare the linearization complex survey outcomes with those from an incorrect independently and identically distributed (iid) assumption and a bootstrap that accounts for the survey design. For our samples, the results from the easy to implement linearization method and the more computationally burdensome bootstrap are in close agreement. This finding is of interest to applied researchers, as bootstrapping is currently the method that is most commonly used for undertaking statistical inference in this literature.

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Bibliographic Info

Paper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 1002.

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Length: 35 pages
Date of creation: 23 Aug 2010
Date of revision:
Handle: RePEc:vic:vicewp:1002

Note: ISSN 1485-6441
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Keywords: Complex survey; inequality; generalized entropy; Atkinson; decomposition; linearization;

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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Complex Survey Data in Econometrics
    by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2012-05-18 19:14:00
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Cited by:
  1. Judith A. Clarke & Ahmed A. Hoque, 2014. "On Variance Estimation for a Gini Coefficient Estimator Obtained from Complex Survey Data," Econometrics Working Papers 1401, Department of Economics, University of Victoria.

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