Multidimensional Poverty: Measurement, Estimation, and Inference

Bennett, Christopher J.; Mitra, Shabana

doi:10.2139/ssrn.2093576

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…See also Mohanty (2011) for a related study on deprivation scores in India. Bennett and Mitra (2013) develop multiple statistical tests for Alkire and Foster's family of poverty measures. 32 Bosmans et al (2013b) introduce an approach that deals with joint aggregation of cardinal and ordinal variables.…”

Section: Poverty Measurement Based On Continuous Variablesmentioning

confidence: 99%

Multidimensional Poverty and Inequality

Aaberge

Brandolini

2015

Handbook of Income Distribution

250

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This paper examines different approaches to the measurement of multidimensional inequality and poverty. First, it outlines three aspects preliminary to any multidimensional study: the selection of the relevant dimensions; the indicators used to measure them; and the procedures for their weighting. It then considers the counting approach and the axiomatic treatment in poverty measurement. Finally, it reviews the axiomatic approach to inequality analysis. The paper provides a selective review of a rapidly growing theoretical literature with the twofold aim of highlighting areas for future research and offering some guidance on how to use multidimensional methods in empirical and policy-oriented applications.

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Section: Poverty Measurement Based On Continuous Variablesmentioning

confidence: 99%

Multidimensional Poverty and Inequality

Aaberge

Brandolini

2015

Handbook of Income Distribution

250

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“…For example, Lasso de la Vega () and Alkire and Foster () 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 (), 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.…”

Section: Multivariate Stochastic Dominancementioning

confidence: 99%

A Review of Stochastic Dominance Methods for Poverty Analysis

García‐Gómez

Pérez

Prieto‐Alaiz

2019

Journal of Economic Surveys

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Stochastic dominance techniques have been mainly employed in poverty analyses to overcome what it is called the multiplicity of poverty indices problem. Moreover, in the multidimensional context, stochastic dominance techniques capture the possible relationships between the dimensions of poverty as they rely upon their joint distribution, unlike most multidimensional poverty indices, which are only based on marginal distributions. In this paper, we first review the general definition of unidimensional stochastic dominance and its relationship with poverty orderings. Then we focus on the conditions of multivariate stochastic dominance and their relationship with multidimensional poverty orderings, highlighting the additional difficulties that the multivariate setting involves. In both cases, we focus our discussion on first‐ and second‐order dominance, though some guidelines on higher order dominance are also mentioned. We also present an overview of some relevant empirical applications of these methods that can be found in the literature in both univariate and multivariate contexts.

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“…The measurement of multidimensional poverty dates back to the early work of Atkinson and Bourguignon (1982), Maasoumi (1986) and Sen (1999). More recently, measures focus on the level of deprivation (Alkire and Foster, 2011;Bennett and Mitra, 2013;Nowak and Scheicher, 2017). Counting approach (Aaberge and Peluso, 2012), Alkire et al approach to fuzzy sets (Betti, 2015).…”

Section: Introductionmentioning

confidence: 99%

Toward a spatial approach for convergence

Amaghouss

Ibourk

2020

IJDI

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Purpose In recent years, there is growing recognition of the importance of geography and space in the analysis of economic convergence by focusing on the dynamics of monetary indicators. The analysis of spatial convergence based on socio-economic indicators are rare. These variables present a complement to understand the spatial dynamics of territorial units. The purpose of this paper is, first, to analyzes and describes trends in multidimensional poverty in Morocco and second it explores the convergence hypothesis. Design/methodology/approach Data are driven from HCP (2017). It concerns 75 provinces over the period 2004 and 2014. In addition to the availability of data, this period corresponds to significant changes in public policy. The nature of the observations necessitates the use of the spatial analysis techniques. Findings The results show that poverty is a geographical phenomenon with low speed of convergence. The paper propose some solutions to help policymakers implement an effective targeting policy aimed at reducing spatial inequalities in terms of multidimensional poverty in Morocco. Originality/value The analysis of spatial convergence based on socio-economic indicators are rare. This paper will focus on the convergence of the poverty index for a developing country.

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Multidimensional Poverty: Measurement, Estimation, and Inference

Cited by 12 publications

References 22 publications

Multidimensional Poverty and Inequality

Multidimensional Poverty and Inequality

A Review of Stochastic Dominance Methods for Poverty Analysis

Toward a spatial approach for convergence

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