Graphical Representation of Multidimensional Poverty: Insights for Index Construction and Policy Making

Rodrigo García Arancibia and Ignacio Girela ()
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Ignacio Girela: Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2024, vol. 172, issue 2, No 10, 595-634

Abstract: Abstract By means of probabilistic graphical models, in this paper, we present a new framework for exploring relationships among indicators commonly included in the Multidimensional Poverty Index (MPI). In particular, we propose an Ising model with covariates for modeling the MPI as an undirected graph. First, we prove why Ising models are consistent with the theoretical distribution of MPI indicators. Then, a comparison between our estimates and the association measures typically used in the literature is provided. Finally, we show how undirected graphs can complement the MPI policy-relevant properties, apart from discovering further insightful patterns that can be useful for policy purposes. This novel approach is illustrated with an empirical application for the global MPI indicators of Guinea and Ecuador, taking living areas and monetary poverty as covariates, respectively.

Keywords: MPI; Random Markov fields; Ising models; Conditional dependency; Deprivations (search for similar items in EconPapers)
Date: 2024
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Working Paper: Graphical Representation of Multidimensional Poverty: Insights for Index Construction and Policy Making (2023)
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DOI: 10.1007/s11205-024-03325-8

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