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

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. Firs...

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Bibliographic Details
Published in:Social indicators research 2024-03, Vol.172 (2), p.595-634
Main Authors: García Arancibia, Rodrigo, Girela, Ignacio
Format: Article
Language:English
Subjects:
Graphical models
Graphs
Human Geography
Indexes
Microeconomics
Original Research
Policy making
Poverty
Property
Public Health
Quality of Life Research
Social Sciences
Sociology
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Description
Summary: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.
ISSN:0303-8300
1573-0921
DOI:10.1007/s11205-024-03325-8