# Introduction

Even before COVID-19 shut schools, the world was in the middle of a learning crisis that threatened efforts to build human capital—the skills and know-how needed for the jobs of the future. More than half of children (53 percent) in low- and middle-income countries either were out of school or failed to learn to read with comprehension by age 10 and so were in learning poverty.<sup>1</sup> The most recent historical trends suggest that it will take 50 years to halve learning poverty in the developing world. COVID-19 is likely to slow that rate by deepening learning gaps and so increasing the number of children in learning poverty in many countries.

<mark>Temporary school closures in more than 180 countries have kept nearly 1.6 billion students out of school, further complicating global efforts to reduce learning poverty. Although most countries have made heroic efforts to put remote and remedial learning strategies in place, learning losses are accumulating rapidly. A recent survey on national education responses to COVID-19 by the United Nations Children’s Fund (UNICEF), the United Nations Educational, Scientific, and Cultural Organization (UNESCO), and the World Bank shows that while countries and regions have responded in various ways, they have found it difficult to reach even half of students.</mark> COVID-19-related school closures are forcing countries even further off track from achieving their learning goals. Students currently in school stand to lose $10 trillion in labor earnings over their working lives.<sup>2</sup> That is one-tenth of global GDP, or half the United States’ annual economic output, or twice the global annual public expenditure on primary and secondary education.

The objectives of this paper are twofold. First it presents some of the properties and limitations of the Learning Poverty measure and proposes complementary aggregations such as the learning poverty gap and the learning poverty severity (or inequality-sensitive-gap), while discussing their main motivations and shortcomings. Second, this paper complements other COVID-19 related simulations by looking at the learning poverty measure. The main drivers of these simulations are school closures, mitigation and remediation effectiveness and the economic shock, which affect two main transmission channels, namely, learning losses and student drop-out at the primary age-group. The rationale and motivation for this broader class of Learning Poverty measures is then used in the simulations, and are quite relevant to nuance the results, especially the regional differences observed between Latina America, South Asia, the Middle East and Africa.

This paper is organized in five sections beyond this introduction. The first section discusses some of the main properties and axioms of the learning poverty measure and proposes a few complementary aggregations, such as a learning poverty gap and a learning poverty distributional-sensitive gap (or _learning poverty severity_ ) which can address some of the discussed short comes. The second section summarizes the COVID-19 simulation methodology and introduces the main scenarios. The third section discuses data coverage and indicators used in the simulations. The fourth section presents and discusses the main results of the simulation work. The last section concludes with a few final remarks.

> 2 Azevedo et al. 2020.

1 World Bank 2019.

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