Robust Multidimensional Poverty Comparisons

Abstract

We investigate how to make poverty comparisons using multidimensional indicators of well-being, showing in particular how to check whether the comparisons are robust to the choice of poverty indices and poverty lines. Our methodology applies equally well to either of what can be defined as "union" and "intersection" approaches to dealing with multidimensional indicators of well-being. When one of two variables is discrete, our methods specialize to those that Atkinson (1991), Jenkins and Lambert (1993) and others have developed to deal with household composition heterogeneity. The results also extend the statistical results recently derived in Davidson and Duclos (2000) to cases where well-being is measured in two or more dimensions. We thus derive the sampling distribution of various multidimensional poverty estimators, including estimators of the "critical" frontiers of poverty lines above which multidimensional poverty comparisons are no longer ethically robust.Multidimensional Poverty, Stochastic Dominance