Research on Economic Inequality

Purpose – A counting approach based on the number of deprivations suffered by the poor is quite an appropriate framework to measure multidimensional poverty with ordinal or categorical data. A method to identify the poor and a number of poverty indices have been proposed to take this framework into account. The implementation of this methodology involves the choice of a minimum number of deprivations required in order for an individual to be identified as poor. This cutoff and the choice of a poverty measure to aggregate the data are two sources of arbitrariness in poverty comparisons. The aim of this chapter is twofold. We first explore properties that characterize an identification method which allows different weights for different dimensions. Then the chapter examines dominance conditions in order to guarantee unanimous poverty rankings in a counting framework.

Design/methodology/approach – In the unidimensional poverty field, one branch of the literature is devoted to establishing dominance criteria that guarantee unanimous orderings at a variety of poverty thresholds and indices. This chapter takes this literature as a starting point, and investigates circumstances in which these ordering conditions may be applied in a weighted counting framework.

Findings – Necessary and sufficient conditions are obtained that guarantee that two vectors, which represent the weighted sum of the deprivations felt by each person, may be unanimously ranked regardless of the identification cutoff and of the poverty measure.

Originality/value – Since most of the data available for measuring capabilities or dimensions of poverty is either ordinal or categorical, the counting approach provides an alternative framework that suits these types of variables. The implementation of the ordering conditions derived in this chapter is based on simple graphical devices that we call dimension deprivation curves. These curves become a useful way to check the robustness of poverty rankings to changes in the identification cutoff. They also provide a tool for determining nonambiguous poverty rankings in a wide set of multidimensional poverty indices that suit ordinal and categorical data.