A natural concern with multivariate poverty measures, as well as with other composite indices, is the robustness of their ordinal comparisons to changes in the indices’ parameter values. Applying multivariate stochastic dominance techniques, this paper derives the distributional conditions under which a multidimensional poverty comparison based on the popular counting measures, and ordinal variables, is fully robust to any values of the indices’ parameters. As the paper shows, the conditions are relevant to most of the multidimensional poverty indices in the literature, including the Alkire–Foster family, upon which the UNDP’s “Multidimensional Poverty Index” (MPI) is based. The conditions are illustrated with an example from the EU-SILC data set.

How can indices of multidimensional poverty be adapted to produce measures that quantify both the joint incidence of multiple deprivations and their chronicity? This paper adopts a new approach to the measurement of chronic multidimensional poverty. It relies on the counting approach of Alkire and Foster (2011) for the measurement of multidimensional poverty in each time period and then on the duration approach of Foster (2009) for the measurement of multidimensional poverty persistence across time. The proposed indices are sensitive both to (i) the share of dimensions in which people are deprived and (ii) the duration of their multidimensional poverty experience. A related set of indices is also proposed to measure transient poverty. The behaviour of the proposed two families is analysed using a relevant set of axioms. An empirical illustration is provided with a Chilean panel dataset spanning the period from 1996 to 2006.

A natural concern with multivariate poverty measures, as well as with other composite indices, is the robustness of their ordinal comparisons to changes in the indices' parameter values. Applying multivariate stochastic dominance techniques, this paper derives the distributional conditions under which a multidimensional poverty comparison based on the popular counting measures, and ordinal variables, is fully robust to any values of the indices.parameters. As the paper shows, the conditions are relevant to most of the multidimensional poverty indices in the literature, including the Alkire-Foster family, upon which the UNDP.s "Multidimensional Poverty Index" (MPI) is based. The conditions are illustrated with an example from the EU-SILC dataset.

This paper documents the extent of inequality of educational opportunity in India spanning the period 1983-2004 using National Sample Survey (NSS) data. We build on recent developments in the literature that has operationalized concepts in the inequality of opportunity theory (including Roemer's) and construct three indices of inequality of educational opportunity using data on an adult sample. Irrespective of the index used, the state of Kerala stands out as the least unequal in terms of educational opportunities. However, even after excluding Kerala, significant inter-state divergence remains amongst the remaining states. Transition matrix analysis confirms substantial inter-temporal mobility in inequality of opportunity across Indian states. Rajasthan and Gujarat in the West and Uttar Pradesh and Bihar in the Centre experienced large-scale fall in the ranking of inequality of opportunities. However, despite being poor, Eastern states of West Bengal and Orissa made significant progress in reducing inequality of opportunity. At a region level, Southern, North-eastern and Eastern regions on average experienced upward mobility (i.e. decline in inequality of opportunity) whilst the Central region experienced downward mobility. We conclude by examining the link between progress towards equality of opportunity and poverty reduction, growth and a selection of pro-poor policies.