On statistical inference for inequality measures calculated from complex survey data
Judith Clarke () and
Empirical Economics, 2012, vol. 43, issue 2, 499-524
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 typically quite similar. 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. Copyright Springer-Verlag 2012
Keywords: Atkinson; Complex survey; Decomposition; Generalized entropy; Inequality; Linearization; C12; C42; D31 (search for similar items in EconPapers)
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Working Paper: On Statistical Inference for Inequality Measures Calculated from Complex Survey Data (2010)
Working Paper: On Statistical Inference for Inequality Measures Calculated from Complex Survey Data (2009)
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