On the robustness of multidimensional counting poverty orderings

Bibliographic Data

ID12836632
AuthorsFrancisco Azpitarte (0000-0002-2688-6933, Loughborough University, corresponding author), José V Gallegos (0000-0002-2421-5749, Pontificia Universidad Católica del Perú), Gaston Isaias Yalonetzky (0000-0003-2438-0223, University of Leeds)
Year2020
Volume18
Issue3
Pages339-364
Publication date2020-05-12
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueThe Journal of Economic Inequality (JOURNAL)
Journal identifiersISSN: 1569-1721 • E-ISSN: 1573-8701
PublisherSpringer Science+Business Media (PUBLISHER • DE)
DOI10.1007/s10888-019-09435-5
OpenAlexW2765871721
LanguageEN
Citations received4
References cited20

Counting poverty measures have gained prominence in the analysis of multidimensional poverty in recent decades. Poverty orderings based on these measures typically depend on methodological choices regarding individual poverty functions, poverty cut-offs, and dimensional weights whose impact on poverty rankings is often not well understood. In this paper we propose new dominance conditions that allow the analyst to evaluate the robustness of poverty comparisons to those choices. These conditions provide an approach to evaluating the sensitivity of poverty orderings superior to the common approach of considering a restricted and arbitrary set of indices, cut-offs, and weights. The new criteria apply to a broad class of counting poverty measures widely used in empirical analysis of poverty in developed and developing countries including the multidimensional headcount and the adjusted headcount ratios. We illustrate these methods with an application to time-trends in poverty in Australia and cross-regional poverty in Peru. Our results highlight the potentially large sensitivity of poverty orderings based on counting measures and the importance of evaluating the robustness of results when performing poverty comparisons across time and regions

Dominance (genetics · Econometrics · Economic growth · Economics · Poverty · Robustness (evolution · Statistics · Stochastic dominance · Agricultural risk and resilience · Income, Poverty, and Inequality · Mathematics · Poverty, Education, and Child Welfare

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