This paper shows how the policy challenges arising from COVID-19 can be understood by drawing on core concepts from the capability approach developed by Sen and others.

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.

This paper is part of the Oxford Poverty and Human Development Initiative's Research in Progress (RP) series. These are preliminary documents posted online to stimulate discussion and critical comment. The series number and letter identify each version (i.e. paper RP1a after revision will be posted as RP1b) for citation.For more information, see www.ophi.org.uk.

Composite measures such as multidimensional poverty indices depend crucially on the weights assigned to the different dimensions and their indicators. A recent strand of the literature uses endogenous weights, determined by the data at hand, to compute poverty scores. Notwithstanding their merits, we demonstrate both analytically and empirically how a broad class of endogenous weights violates key properties of multidimensional poverty indices such as monotonicity and subgroup consistency. Without these properties, antipoverty policy targeting and assessments are bound to be seriously compromised. Using reallife data from Ecuador and Uganda, we show that these violations are widespread. Hence, one should be extremely careful when using endogenous weights in measuring poverty. Our results naturally extend to other welfare measures based on binary indicators, such as the widely studied asset indices.