Asymptotic and Bootstrap Inference for Inequality and Poverty Measures

By Russell Davidson, Emmanuel Flachaire. 2007

139 citations recorded by the source corpus.

A random sample drawn from a population would appear to offer an ideal opportunity to use the bootstrap in order to perform accurate inference, since the observations of the sample are IID. In this paper, Monte Carlo results suggest that bootstrapping a commonly used index of inequality leads to inference that is not accurate even in very large samples, although inference with poverty indices is satisfactory. We find that the major cause is the extreme sensitivity of many inequality indices to the exact nature of the upper tail of the income distribution. This leads us to study two non-standard bootstraps, the m out of n bootstrap, which is valid in some situations where the standard bootstrap fails, and a bootstrap in which the upper tail is modelled parametrically. Monte Carlo results suggest that accurate inference can be achieved with this last method in moderately large samples.

Subfields: Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/83acaf6990e8f41c3c59d0fd10d14f9b28b5f4ec

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In Economics and Econometrics

1. Mame Astou Diouf
2. Jean-Marie Dufour
3. L. Godfrey

In Sociology and Political Science

1. Mame Astou Diouf
2. Jean-Marie Dufour

Citations in the corpus

• Improved Nonparametric Inference for the Mean of a Bounded Random Variable with Application to Poverty Measures (2005, methodology): “Davidson and Flachaire (2004) set the poverty line z to half the median of the distribution SM(a,b,c).”
• WEALTH INEQUALITY: A SURVEY (2015, unknown): “…the lower tail and a parametric Pareto distribution model for the upper tail is developed in Cowell and Victoria-Feser (2007) to deal with data contamination in point estimation and in Davidson and Flachaire (2007) for confidence interval estimation and hypothesis testing using bootstrap methods.”