Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality

Russell Davidson and Jean-Yves Duclos ()

No 181, LIS Working papers from LIS Cross-National Data Center in Luxembourg

Abstract: We derive the asymptotic sampling distribution of various estimators frequently used to order distributions in terms of poverty, welfare and inequality. This includes estimators of most of the poverty indices currently in use, as well as estimators of the curves used to infer stochastic dominance of any order. These curves can be used to determine whether poverty, inequality or social welfare is greater in one distribution than in another for general classes of indices. We also derive the sampling distribution of the maximal poverty lines (or income censoring thresholds) up to which we may confidently assert that poverty or social welfare is greater in one distribution than in another. The sampling distribution of convenient estimators for dual approaches to the measurement of poverty is also established. The statistical results are established for deterministic or stochastic poverty lines as well as for paired or independent samples of incomes. Our results are briefly illustrated using data for 6 countries drawn from the Luxembourg Income Study data bases.

Pages: 40 pages
Date: 1998-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Published in Econometrica 68, no. 6 (Nov. 2000): 1435-1464

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Related works:
Journal Article: Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality (2000)
Working Paper: Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality (1998)
Working Paper: Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality (1998)
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