Multidimensional Poverty: Measurement, Estimation, and Inference

Abstract

Multidimensional poverty measures give rise to a host of statistical hypotheses which are of interest to applied economists and policy-makers alike. In the specific context of the generalized Alkire-Foster (Alkire and Foster 2008) class of measures, we show that many of these hypotheses can be treated in a unified manner and also tested simultaneously using the minimum p-value methodology of Bennett (2010). When applied to study the relative state of poverty among Hindus and Muslims in India, these tests reveal novel insights into the plight of the poor which are not otherwise captured by traditional univariate approaches. This publication is copyright, however it may be reproduced without fee for teaching or non-profit purposes, but not for resale. Formal permission is required for all such uses, and will normally be granted immediately. For copying in any other circumstances, or for re-use in other publications, or for translation or adaptation, prior written permission must be obtained from OPHI and may be subject to a fee. AcknowledgementsWe wish to thank James Foster, Sabina Alkire, and participants at the Oxford Poverty & Human Development Initiative workshop on Robustness Methods for Multidimensional Welfare Analysis for their comments. We also wish to thank Chrysanthi Hatzimasoura for alerting us to an error in our original R code.

A Review of Stochastic Dominance Methods for Poverty Analysis

“…Consequently, there have been several recent attempts to derive dominance conditions for robust multidimensional poverty orderings based on counting measures. For example, Lasso de la Vega (2010) and Alkire and Foster (2011) derive dominance conditions for robust multidimensional poverty orderings across different values of k. Another contribution that is worth pointing out is that of Bennett and Mitra (2013), who propose a general framework to develop dominance tests on counting measures based on the standard p-value approach. This procedure would allow, for example, to infer the specific range of poverty lines over which a poverty ordering holds or the specific dimensions in which a country (or region) underperforms, among other questions that can be relevant from a policy perspective.…”

Multidimensional Measures of Poverty: The Potential Contribution of Non‐Profit Food Pantry Data to Assess Community Economic Condition

“…While there are criticisms on how the idea of material deprivation is used within the European Union, either alone or in combination with other income or consumption measures, it is considered an improvement on exclusively incomebased measures (Bennett & Mitra, 2013;Bossert, Chakravarty, & D'Ambrosio, 2013;Israel, 2016;Mauro, Biggeri, & Maggino, 2016;Mitra et al, 2013;Treanor, 2014). In the end, research in many countries, such as Australia (Callander, Schofield, & Shrestha, 2012), those in East Asia and the Pacific (Minujin, McCaffrey, Patel, & Paienjton, 2014), and across the European Union (Bossert et al, 2013) is now arguing for using multidimensional measures of poverty.…”

Examining socioeconomic health disparities using a rank-dependent Rényi index

“…In the U.S., composite measures of health and health-related quality of life remain core tools for monitoring progress toward the HP2020 goals (DHHS, 2014). The focus on multidimensional analyses is also manifested in the development of indices for multidimensional inequality (Bennett and Mitra, 2013; Decancq and Lugo, 2009; Tsui, 1999; Maasoumi, 1986). …”