Publication: Learning Poverty: Measures and Simulations
Date
2020-10
ISSN
Published
2020-10
Author(s)
Abstract
COVID-19-related school closures are
pushing countries off track from achieving their learning
goals. This paper builds on the concept of learning poverty
and draws on axiomatic properties from social choice
literature to propose and motivate a distribution-sensitive
measures of learning poverty. Numerical, empirical, and
practical reasons for the relevance and usefulness of these
complementary inequality sensitive aggregations for
simulating the effects of COVID-19 are presented. In a
post-COVID-19 scenario of no remediation and low mitigation
effectiveness for the effects of school closures, the
simulations show that learning poverty increases from 53 to
63 percent. Most of this increase seems to occur in
lower-middle-income and upper-middle-income countries,
especially in East Asia and the Pacific, Latin America, and
South Asia. The countries that had the highest levels of
learning poverty before COVID-19 (predominantly in Africa
and 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 poverty
and learning deprivation sensitive to changes in
distribution, such as gap and severity measures, show
differences in learning loss regional rankings. Africa
stands to lose the most. Countries with higher inequality
among the learning poor, as captured by the proposed
learning poverty severity measure, would need far greater
adaptability to respond to broader differences in student needs.
Citation
“Azevedo, Joao Pedro. 2020. Learning Poverty : Measures and Simulations. Policy Research Working Paper;No. 9446. © World Bank, Washington, DC. http://openknowledge.worldbank.org/handle/10986/34654 License: CC BY 3.0 IGO.”
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