On the construction of a feasible range of multidimensional poverty under benchmark weight uncertainty

MEHMET PINAR, THANASIS STENGOS, NIKOLAS TOPALOGLOU

Research output: Contribution to journalArticle

Abstract

There are infinitely many alternative benchmark weights that decision makers could choose to measure multidimensional poverty. To overcome the resulting uncertainty, we derive a feasible range of multidimensional poverty that considers all admissible weights within the chosen lower and upper bounds of weights. We use Kenyan and Canadian data to illustrate the use of our methodology, which is an adaptation of existing methods for portfolio analysis based on stochastic dominance. These two-empirical analyses suggest that different weights allocated to poverty dimensions can produce very different multidimensional poverty outcomes for a given population even in cases with small weight perturbations.
Original languageEnglish
JournalEuropean Journal of Operational Research
Early online date2 Sep 2019
DOIs
Publication statusE-pub ahead of print - 2 Sep 2019

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Poverty
Benchmark
Uncertainty
Range of data
Stochastic Dominance
Upper and Lower Bounds
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Multidimensional poverty
Perturbation
Methodology
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Keywords

  • Multidimensional poverty measurement

Cite this

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On the construction of a feasible range of multidimensional poverty under benchmark weight uncertainty. / PINAR, MEHMET; STENGOS, THANASIS; TOPALOGLOU, NIKOLAS.

In: European Journal of Operational Research, 02.09.2019.

Research output: Contribution to journalArticle

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AB - There are infinitely many alternative benchmark weights that decision makers could choose to measure multidimensional poverty. To overcome the resulting uncertainty, we derive a feasible range of multidimensional poverty that considers all admissible weights within the chosen lower and upper bounds of weights. We use Kenyan and Canadian data to illustrate the use of our methodology, which is an adaptation of existing methods for portfolio analysis based on stochastic dominance. These two-empirical analyses suggest that different weights allocated to poverty dimensions can produce very different multidimensional poverty outcomes for a given population even in cases with small weight perturbations.

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