Multi-Dimensional Poverty Among Adults in Central America and Gender Differences in the Three I’s of Poverty: Applying Inequality Sensitive Poverty Measures With Ordinal Variables

Abstract

The Alkire and Foster (2011) methodology, as the mainstream approach to the measurement of multi-dimensional poverty in the developing world, is insensitive to inequality among the multidimensionally poor individuals and does not consider simultaneously the concepts of efficiency and distributive justice. Moreover, the vast majority of empirical indices of multi-dimensional poverty in the literature overlook intra-household inequalities, an issue that is crucial to a better understanding of gender inequalities, because they equate the poverty status of the household with the poverty status of all individuals in the household. Consequently, using the general framework proposed by Silber and Yalonetzky (2014) and Rippin's ideas on multi-dimensional poverty measurement (2013, 2017), we propose in this paper to depart somehow from the mainstream approach and take an individual-based and inequality sensitive view of multi-dimensional poverty when only ordinal (dichotomized) variables are available. We use such an approach to estimate multi-dimensional poverty among individuals aged 18 and 59 years living in Guatemala, El Salvador, Honduras, Nicaragua, and Costa Rica, shedding thus some light on gender differences in poverty and inequality in those countries. Overall, we find that individuals living in Guatemala have the highest probability of being multidimensionally poor, followed by the ones from Nicaragua; people living in Costa Rica, by contrast, have by far the lowest probability of being poor. In the middle appears Honduras and El Salvador, Hondurans having a larger probability of being multi-dimensionally poor than the Salvadorians. Regarding the gender gaps, the overall estimates suggest that the incidence and the intensity of multidimensional poverty in Central America are higher among females; inequality, however, is somewhat higher among males.

Assessing Trends in Multidimensional Poverty During the MDGs

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An Individual-based Index of Multidimensional Poverty for Low- and Middle-Income Countries

“…It should be noted that since the global MPI uses a "dual cut-off identification method" that converts the weighted deprivation counts vector (c) into a binary variable (0-1), 12 it employs a "discrete identification function"; moreover, this method, and so the global MPI, censors below k the distribution of weighted deprivation counts, disregarding thus valuable information on the distribution of deprivations. It also implicitly assumes that up to k dimensions are "perfect substitutes", while it considers the same dimensions as "perfect complements" from k onwards (Rippin 2017), a difficult assumption to justify theoretically (Espinoza-Delgado and Silber 2018Silber , 2021.…”

The Topography and Sources of Multidimensional Poverty in Turkey

“…We are aware of the fact that age and gender are important demographic characteristics that influence poverty at the regional level. Moreover, we acknowledge that the time dimension of such analyses is vital as generational effects can be a crucial aspect of multidimensional poverty (Vijaya et al 2014;Klasen and Lahoti 2016;Espinoza-Delgado and Silber 2018). The follow-up of a given generation throughout the sample period would be an insightful exercise.…”