Learning Poverty: Measures and Simulations

Abstract

COVID-19-related school closures are pushing countries off track from achieving their learninggoals. This paper builds on the concept of learning poverty and draws on axiomatic properties from social choiceliterature to propose and motivate a distribution-sensitive measures of learning poverty. Numerical, empirical, andpractical reasons for the relevance and usefulness of these complementary inequality sensitive aggregations forsimulating the effects of COVID-19 are presented. In apost-COVID-19 scenario of no remediation and low mitigation effectiveness for the effects of school closures, thesimulations show that learning poverty increases from 53 to 63 percent. Most of this increase seems to occur inlower-middle-income and upper-middle-income countries, especially in East Asia and the Pacific, Latin America, andSouth Asia. The countries that had the highest levels of learning poverty before COVID-19 (predominantly in Africaand the low-income country group) might have the smallest absolute and relative increases in learning poverty, reflecting how great the learning crisis was in those countries before the pandemic. Measures of learning povertyand learning deprivation sensitive to changes in distribution, such as gap and severity measures, showdifferences in learning loss regional rankings. Africa stands to lose the most. Countries with higher inequalityamong the learning poor, as captured by the proposed learning poverty severity measure, would need far greateradaptability to respond to broader differences in student needs.

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