Moderate Multidimensional Poverty Index: Paving the Way Out of Poverty.

Eradicating poverty in all its forms, everywhere, requires indicators that measure sustainable pathways out of poverty, and not only the absence of acute poverty. This paper introduces a trial Moderate Multidimensional Poverty Index (MMPI) that reflects moderate rather than acute levels of multidimensional poverty. The MMPI adjusts nine of the 10 indicators of the global Multidimensional Poverty Index (global MPI) to reflect moderate poverty and create a meaningful superset of the acutely poor population. Although data-constrained, the trial MMPI outlines a methodology and potential indicators for a measure that would: (i) be meaningful and comparable across populations at higher levels of development; (ii) align with higher standards defined in Agenda 2030; and (iii) provide insight into aspects of intrahousehold deprivation. The MMPI is illustrated empirically using nationally representative household surveys from Bangladesh, Guatemala, Iraq, Serbia, Tanzania and Thailand. The results confirm the added value of having three nested measures of destitution, acute, and moderate multidimensional poverty. The MMPI also complements monetary measures with informative differences in poverty levels observed. The results demonstrate that a Moderate MPI is a desirable global poverty index, which is likely to illuminate thus far hidden aspects in of multidimensional poverty, such as intrahousehold deprivations in education. Challenges remain regarding data availability, and further study across additional countries is required before the MMPI structure can be finalised.

Exploring the potential for a new measure of socioeconomic deprivation status to monitor health inequality.

BACKGROUND: Monitoring health inequalities is an important task for health research and policy, to uncover who is being left behind - and where - and to inform effective and equitable policies and programmes to tackle existing inequities. The choice of which measure to use to monitor and analyse health inequalities is thereby not trivial. This article explores a new measure of socioeconomic deprivation status (SDS) to monitor health inequalities. METHODS: The SDS measure was constructed using the Alkire-Foster method. It includes eight indicators across two equally weighted dimensions (education and living standards) and specifies a four-level gradient of socioeconomic deprivation at the household-level. We conducted four exercises to examine the value-added of the proposed SDS measure, using Demographic and Health Surveys data. First, we examined the discriminatory power of the new measure when applied to outcomes in four select reproductive, maternal, neonatal, and child health (RMNCH) indicators across six countries: skilled birth attendance, stunting, U5MR, and DTP3 immunisation. Then, we analysed the behaviour and association of the new SDS measure vis-à-vis the DHS Wealth Index, including chi-squared test and Pearson correlation coefficient. Third, we analysed the robustness of the SDS measure results to changes in its structure, using pairwise comparisons and Kendal Tau-b rank correlation. Finally, we illustrated some of the advantageous properties of the new measure, disaggregation and decomposition, on Haitian data. RESULTS: 1) Higher levels of socioeconomic deprivation are generally consistently associated with lower levels of achievements in the RMNCH indicators across countries. 2) 87% of all pairwise rank comparisons across a range of SDS measure structures were robust. 3) SDS and DHS Wealth Index are associated, but with considerable cross-country variation, highlighting their complementarity. 4) Haitian households in rural areas experienced, on average, more severe socioeconomic deprivation as well as lower levels of RMNCH achievement than urban households. CONCLUSIONS: The proposed SDS measure adds analytical possibilities to the health inequality monitoring literature, in line with ethically and conceptually well-founded notions of absolute, multidimensional disadvantage. In addition, it allows for breakdown by its dimensions and components, which may facilitate nuanced analyses of health inequality, its correlates, and determinants.

Global multidimensional poverty and COVID-19: A decade of progress at risk?

According to the global Multidimensional Poverty Index (MPI), an internationally comparable measure, poverty in developing countries has fallen substantially over the last 15 years. The COVID-19 pandemic and associated economic contraction are negatively impacting multiple dimensions of poverty and jeopardising this progress. This paper uses recent assessments of food insecurity and school closures made by UN agencies to inform microsimulations of potential short-term impacts of the pandemic under alternative scenarios. These simulations use the nationally representative datasets underlying the 2020 update of the global MPI. Because these datasets were collected in various years before the pandemic, we develop models to translate the simulated impacts to 2020. Our approach accounts for the country-specific joint distribution of deprivations in the simulations, recent poverty reduction trends, and resulting differences in the responsiveness of the global MPI to the scenarios. Aggregating results across 70 countries that account for 89% of the global poor according to the 2020 global MPI, we find that the potential setback to multidimensional poverty reduction is between 3.6 and 9.9 years under the alternative scenarios. We argue that the extent to which such disruptions result in persistent increases of poverty and deprivations may be attenuated by appropriate policy responses.