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

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.

Poor Residents’ Perceptions of the Impacts of Tourism on Poverty Alleviation: From the Perspective of Multidimensional Poverty

“…In addition, the debate about how to identify the weight of each indicator [32]. On the assessment of multidimensional poverty, scholars have mainly adopted: (1) In the UNDP-MPI methodology, the equal weighting method has been broadly employed [33,34].…”

“…In order to obtain appropriate importance of indicators, quite a few innovative approaches are being used. For example, Pinar, Stengos and Topaloglou [32] identified the weights by extending the stochastic dominance (SD) efficiency. With the cognition and connotation deepening, the multidimensional poverty theory has triggered a large wave of poverty research circle.…”

The Topography and Sources of Multidimensional Poverty in Turkey

“…In addition to cutoff determination, there are other aspects in the applied literature (See Catalán and Gordon (2019); Santos and Villatoro (2019) for a recent debate on MPI and its reliability over an example for Latin American countries.). Recent attempts examine the weight determination in MPI calculation suggesting that it might have an influence on the level of MPI (Catalán 2019;Pinar et al 2020). While the level of arbitrariness in weight selection is acknowledged, it is not the level of MPI that matters but the geographical distribution that is central in the setup of our analyses.…”

Choquet-Integral Aggregation Method to Aggregate Social Indicators to Account for Interactions: An Application to the Human Development Index

“…Ravallion (2012) offered an alternative aggregation function based on the generalized aggregation formula by Chakravarty (2003Chakravarty ( , 2011, which led to more sensible tradeoffs across the dimensions compared to the geometric mean (see Pinar, 2019 for the recent application of the generalized aggregation method to the OECD's regional well-being index). Additionally, multiple studies analyzed the robustness of allocating weights to HDI dimensions using linear programming tools to assess the precision of rankings with alternative weights (Athanassoglou, 2015;Cherchye et al, 2008;Foster et al, 2013;Pinar et al, 2017Pinar et al, , 2020Rogge, 2018). 3 Even though the existing literature focused on the impact of alternative weight allocation across the dimensions of the HDI on the ranking precision, the literature mentioned above did not consider the potential interaction among the dimensions of the HDI.…”