Measuring and Decomposing Inequality among the Multidimensionally Poor Using Ordinal Data: A Counting Approach

Poverty has many dimensions, which, in practice, are often binary or ordinal in nature. A number of multidimensional measures of poverty have recently been proposed that respect this ordinal nature. These measures agree that the consideration of inequality across the poor is important, which is typically captured by adjusting the poverty measure to be sensitive to inequality. This, however, comes at the cost of sacrificing certain policy-relevant properties, such as not being able to break down the measure across dimensions to understand their contributions to overall poverty. In addition, compounding inequality into a poverty measure does not necessarily create an appropriate framework for capturing disparity in poverty across population subgroups, which is crucial for effective policy. In this paper, we propose using a separate decomposable inequality measure – a positive multiple of variance – to capture inequality in deprivation counts among the poor and decompose across population subgroups. We provide two illustrations using Demographic Health Survey datasets to demonstrate how this inequality measure adds important information to the adjusted headcount ratio poverty measure in the AlkireFoster class of measures

Inequality Among the MPI Poor, and Regional Disparity in Multidimensional Poverty: Levels and Trends

Poverty reduction is not necessarily uniform across all poor people in a country, or across population subgroups; an improvement overall may yet leave the poorest of the poor behind. In 2014 we use a new measure to analyse inequality among poor people in 90 countries, and find the highest levels are to be found in 15 Sub-Saharan African countries; in Pakistan, India and Afghanistan; and in Yemen and Somalia

MI HÁNY MÉTER? A PÁRKAPCSOLATI ERŐSZAK MÉRÉSÉRŐL

The world now carries over seven billion human beings. Where do the poorest billion of us – the ‘bottom billion’ in terms of multidimensional poverty – live? The question is important to constructing effective policies and informing institutions and movements seeking to reduce poverty. This note does two things: first, it zooms in on the poorest billion based on a multidimensional approach and, second, it goes beyond national aggregates. In particular, it looks at the bottom billion at the subnational level and uses, for the first time, individual poverty profiles. The analysis is based on the global Multidimensional Poverty Index (MPI) – a measure of acute poverty in over 100 developing countries, which includes information on health, education, and living standards. As we show, the MPI allows us to undertake subnational and individual level analyses and so go beyond national averages that hide inequality.Copyright © Oxford Poverty & Human Development Initiative 2013

Global Multidimensional Poverty Index 2014

The Global Multidimensional Poverty Index (MPI) is an index of acute multidimensional poverty that in 2014 covers 108 developing countries. It assesses the nature and intensity of poverty at the individual level, by directly measuring the overlapping deprivations poor people experience simultaneously. It provides a vivid picture of how and where people are poor, within and across countries, regions and the world, enabling policymakers to better target their resources at those most in need. This document provides an overview of key findings