metadata: authors: 0: author: Azevedo,Joao Pedro Wagner De
1: author: Cojocaru,Alexandru
2: author: Montalva Talledo,Veronica Sonia
3: author: Narayan,Ambar
count: World
docna: 0: docna: COVID-19 School Closures, Learning Losses
            and Intergenerational Mobility
docty: Policy Research Working Paper
abstracts: cdata!: The paper presents a first global
            investigation of the longer-term inequality implications of
            COVID-19 by examining the effect of school closures on the
            ability of children from different countries and backgrounds
            to engage in continued learning throughout the pandemic, and
            their implications for intergenerational mobility in
            education. The analysis builds on the data from the Global
            Database of Intergenerational Mobility, country-specific
            results of the learning loss simulation model using weekly
            school closure information from February 2020 to February
            2022, and high-frequency phone survey data collected by the
            World Bank during the pandemic to assess the incidence and
            quality of continued learning during periods of school
            closures across children from different backgrounds. Based
            on this information, the paper simulates counterfactual
            levels of educational attainment and corresponding absolute
            and relative intergenerational educational mobility measures
            with and without COVID-19 impacts, to arrive at estimates of
            COVID-19 impacts. The simulations suggest that the extensive
            school closures and associated learning losses are likely to
            have a significant impact on both absolute and relative
            intergenerational educational mobility in the absence of
            remedial measures. In upper-middle-income countries, the
            share of children with more years of education than their
            parents (absolute mobility) could decline by 8 percentage
            points, with the largest impacts observed in the Latin
            America region. Furthermore, unequal access to continued
            learning during school closures across children from
            households of different socioeconomic backgrounds (proxied
            by parental education levels) leads to a significant decline
            in relative educational mobility.
display_title: COVID-19 School Closures, Learning
            Losses and Intergenerational Mobility
listing_relative_url: /research/2023/03/34029541/covid-19-school-closures-learning-losses-intergenerational-mobility
topicv3: Economic and Social Mobility m1326259 1152,Education Access and Equity m1327050 2127,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2023-03-27T00:00:00Z
dois: 10.1596/1813-9450-10381
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 675142
1: author_key: 648083
2: author_key: 1878610
3: author_key: 167324
model_extractions: Policy Research Working Paper 10381 COVID-19 School Closures, Learning Losses and Intergenerational Mobility _João Pedro Azevedo Alexandru Cojocaru Veronica Montalva Talledo Ambar Narayan_ Poverty and Equity Global Practice & Education Global Practice March 2023
Policy Research Working Paper 10381

# **Abstract**

The paper presents a first global investigation of the longer-term inequality implications of COVID-19 by examining the effect of school closures on the ability of children from different countries and backgrounds to engage in continued learning throughout the pandemic, and their implications for intergenerational mobility in education. The analysis builds on the data from the Global Database of Intergenerational Mobility, country-specific results of the learning loss simulation model using weekly school closure information from February 2020 to February 2022, and high-frequency phone survey data collected by the World Bank during the pandemic to assess the incidence and quality of continued learning during periods of school closures across children from different backgrounds. Based on this information, the paper simulates counterfactual levels of educational attainment and corresponding absolute and relative intergenerational educational mobility measures with and without COVID-19 impacts, to arrive at estimates of COVID-19 impacts. The simulations suggest that the extensive school closures and associated learning losses are likely to have a significant impact on both absolute and relative intergenerational educational mobility in the absence of remedial measures. In upper-middle-income countries, the share of children with more years of education than their parents (absolute mobility) could decline by 8 percentage points, with the largest impacts observed in the Latin America region. Furthermore, unequal access to continued learning during school closures across children from households of different socioeconomic backgrounds (proxied by parental education levels) leads to a significant decline in relative educational mobility.

This paper is a product of the Poverty and Equity Global Practice and the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at acojocaru@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# COVID-19 School Closures, Learning Losses and Intergenerational Mobility<sup>∗</sup>

João Pedro Azevedo, Alexandru Cojocaru, Veronica Montalva Talledo and Ambar Narayan<sup>1</sup> _Keywords: COVID-19, learning losses, inequality, intergenerational mobility_ _JEL: I20, I24, I25, O15_

>  We are grateful to Maria Ana Lugo, Shwetlena Sabarwal and Benu Bidani for useful comments on an earlier draft of the paper and to Daniel Gerszon Mahler for discussion and suggestions on the use of GDIM data. All remaining errors are those of the authors. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> 1 Affiliations: Azevedo: Education Global Practice, The World Bank and UNICEF. Cojocaru, Montalva Talledo and Narayan: Poverty and Equity Global Practice, The World Bank. Alexandru Cojocaru is the corresponding author: <u>acojocaru@worldbank.org</u>
# **1. Introduction**

There is mounting evidence that the COVID-19 pandemic has had not only a devastating impact on poverty and well-being worldwide, but also that this impact has not been equally distributed both across and within countries. Globally, those with per-capita incomes between $1.9/day and $5.5/day are estimated to have experienced the largest income drops (Narayan et al., 2022). Data from High Frequency Phone Surveys (HFPS) collected by the World Bank throughout the pandemic shows that respondents in Low- and Middle-Income Countries are reporting higher rates of work stoppages and income losses compared to High Income Countries (Bundervoet et al., 2021; Narayan et al., 2022). Ferreira et al. (2021) estimate tradeoffs between health and poverty impacts across countries and show that the number of years in poverty resulting from the pandemic is inversely correlated with the country’s GDP per capita.

Evidence on the impacts of COVID-19 on inequality, both within countries and globally, is more difficult to obtain because of the more stringent data requirements. But there is still consistent evidence that within countries, women, youth, and those with lower levels of education or more precarious connections to the labor market were more severely affected, in terms of job or income losses, by the restrictions to economic activity. This occurred due to several reasons, including the types of jobs they have, their ability to perform work remotely, or the nature of demand for their services (Hill and Narayan, 2020). Access to continued learning during the periods of school closures has also been strongly and positively correlated with GDP per capita across countries, and with the level of parental education within countries. This means that children in poor countries, and in poor households within those countries, were less able to engage in meaningful learning over a significant amount of time (Narayan et al., 2022). These disruptions in learning are estimated to have had dramatic effects in terms of learning losses (Azevedo et al 2021).

The patterns of COVID-19 impacts described above are short-term impacts observed throughout 2020 and 2021. Estimates that try to quantify the short-run changes in income inequality due to the economic impacts of COVID-19 suggest that these impacts are likely to be, on average, small in magnitude, and short-term increases in inequality during the 2020-2021 are not universal (World Bank, 2022). In countries for which high-frequency phone survey data from the pandemic period are available, the Gini index of inequality is estimated to increase by about 1 point on average because of the pandemic’s short-run impacts on labor incomes (Narayan et al., 2022, World Bank, 2022). However, there is a concern that the inequality impacts of the pandemic will be amplified over the medium to long-term by learning losses due to school closures, which is likely to lead to a highly unequal pattern of losses in human capital formation across socio-economic groups, whose impacts are likely to last through the life-cycle.

The main goal of this paper is to explore these medium-to-long term inequality impacts of the COVID-19 pandemic through the education channel. Specifically, we study inequality traps by estimating the impact of the learning losses associated with school closures on inter-generational mobility in education, which we consider to be useful proxies for measuring the longer-term impacts of pandemic-induced school disruptions in a country. Following Narayan et al (2018), we consider two concepts of intergenerational mobility of the many that have been used by the economic literature over the years: _absolute upward mobility_ (absolute mobility for short) that measures the share of individuals who surpass the education of their parents, and _relative mobility_ that captures the degree to which individual socioeconomic outcomes are independent of the outcomes of one’s parents (Van der Weide et al., 2021). These indicators reflect two broad concepts of economic mobility in a society – absolute progress, which also reflects the aspiration every parent has for their children; and fairness, whereby one’s chance of success in a society is not dependent on the circumstances of one’s birth.

These indicators are useful measures of societal progress in a broad sense. Higher absolute upward mobility across generations is closely associated with income growth and a rise in well-being of those at the bottom of the economic ladder that enables escape from poverty traps. Higher relative mobility across generations is 2
associated with greater equality of opportunity, which is the extent to which people’s life achievements are affected by circumstances they are born into, such as parental education and income, race, gender, and birthplace. Both types of mobility are important for economic progress and for sustaining a social contract that addresses the aspirations of society, which is critical for social cohesion. Without absolute mobility, living standards cannot improve; while a lack of relative mobility is deeply unfair and perpetuates inequality across generations. Empirical evidence suggests that lower relative mobility is associated with higher income inequality, likely due to a two-way relationship whereby inequality of opportunity is both a cause and consequence of income inequality. Low relative mobility can also be harmful to long-term economic growth because of wasted human potential that leads to lower levels of human capital accumulation, and misallocation of resources, relative to what would have occurred with greater equality of opportunity.<sup>2</sup> While a few recent studies have examined the implications of school closures on learning outcomes and dropout rates (see Moscoviz and Evans (2022) for a summary of available evidence), we are only aware of a single study so far by Neidhofer et al. (2021) that examines the impact of school closures on inter-generational mobility for a set of 17 Latin American countries. They conclude that in the LAC region the inter-generational persistence in education could increase by 7 percent on account of the pandemic. The contribution of this paper to the literature on COVID-19 and inequality is thus two-fold. First, we estimate the impacts of school closures on educational persistence for a larger set of developing countries across several regions of the world, which enables us to study the heterogeneity of implications of school closures for mobility across different parts of the world. Second, unlike Neidhofer et al. (2021), which is based on pre-pandemic survey data and relies on assumptions of pandemic learning modalities based on policies deployed by different countries and certain household characteristics, we are able to draw from some observed data from the World Bank’s high-frequency phone surveys on what types of learning (if any) children from different types of households are reported to have engaged in during periods of school closures, and the latest learning-loss simulation scenarios from Azevedo et al (2022) which now include observed actual school closure data from the <u>UNESCO school closures tracker.</u> The main findings of the paper are that the learning losses associated with the extensive school closures around the world due to COVID-19 are likely to translate into economically significant reductions in both absolute and relative educational mobility across generations. We estimate that in high-income and in upper middleincome countries the average share of children with more years of education than their parents would decline by 8-9 percentage points, with smaller average declines estimated for low-income countries. The results imply that the impact of the learning losses associated with the pandemic would worsen the pre-existing trend of declining absolute mobility in Upper-Middle Income (henceforth, UMIC) and High-Income (HIC) groups of countries, and reverse the improvements in absolute mobility for Low-Income (LIC) and Lower-Middle Income (LMIC) country groups.<sup>3</sup> Among developing regions, Latin America and Caribbean (LAC) is estimated to have the largest decline in absolute mobility, followed by the Eastern Europe and Central Asia (ECA) region.

Furthermore, the ability of children to engage in continued learning during periods of school closures was not uniform, with children from households with lower socio-economic status having a higher incidence of not engaging in learning during periods of school closures. Estimates of learning losses based on HFPS data that account for differential learning engagement across population groups show that _relative_ intergenerational mobility, measured by the correlation coefficient between parents’ and children’s education (which are not impacted when educational losses are assumed to be uniform) may have declined by almost 4 percent on average, depending on the assumptions about the effectiveness of pandemic learning. The magnitudes of the

> 2 See Narayan et al (2018), Chapter 1 and Van Der Weide et al (2021) for a review of the theory and evidence on the relationship between inequality and intergenerational mobility, and between economic growth and intergenerational mobility.

> 3 The country groups by both income and region are as defined by World Bank Group.

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declines in relative mobility are consistent with those previously estimated for Latin American countries (Neidhofer et al., 2021) and significant, given the evolution of relative mobility over the 1950-1980 birth cohorts.<sup>4</sup> Notably, the simulations presented in this paper, like the ones in Neidhofer et al. (2021) present the impacts on mobility in the absence of remedial interventions in post-pandemic years, and should thus be viewed as upper bound estimates of the long-term damage that the school closures could inflict and not as predictions for the future. That said, some of the emerging evidence that we discuss suggests that broad-based and rapid recovery of learning losses, particularly in developing countries, and particularly among children from disadvantaged households, is unlikely.

The rest of the paper is structured as follows. Section 2 provides the context and rationale for the choice of intergenerational mobility in education as the outcome of interest and discusses how the paper fits into and adds value to the literature on longer-term socioeconomic impacts of the pandemic. Section 3 provides a description of the data employed in the analysis and the empirical methodology. Section 4 presents an overview of our main results of uniform loss simulations, as well as distributionally-sensitive simulations that employ additional information on pandemic learning from high frequency phone surveys. Section 5 extrapolates from the distributionally-sensitive simulations to inform global simulations of distributionally-sensitive pandemic impacts on intergenerational mobility. Section 6 concludes.

# **2. Context and motivation**

Estimates from the World Bank for a sample of 157 countries suggest that COVID-19 could lead to a loss of between 0.3 and 0.9 years of schooling adjusted for quality; close to 7 million students from primary up to secondary education could drop out due to pandemic-related income shocks; and students from the current cohort could face a loss in lifetime earnings that is equivalent to a 5 percent annual reduction of income (Azevedo et al 2021). In several countries in Sub-Saharan Africa, fewer than 1 in 5 primary school students have maintained contact with their teacher since school closures (Josephson et al., 2020).

Our study fits well with the growing literature on assessing the possible longer-term impacts of the pandemic through the channel of human capital losses. The most comprehensive attempt to do so on a global scale so far has been by Samaniego et al (2022), who quantifies the potential long-term economic impact of disruptions in schooling and work experience caused by the pandemic across 145 countries at different levels of development.<sup>5</sup> Both lost schooling and experience are estimated to contribute to significant losses in global learning and output, with developed countries incurring greater losses than developing countries because they have more schooling to start with and higher returns to experience.<sup>6</sup> Fuchs-Schundeln (2022) estimates that school closures in the US on average lead to a reduction of life-time earnings of 1.8 percent for affected children. Another recent study by Buffie et al (2022) analyzes the medium-term macroeconomic impact of the pandemic and associated lock-down measures on low-income countries caused by the degradation of human capital, using a dynamic general equilibrium model. They show that the persistence of loss-of-learning effects on labor productivity is likely to make the post-COVID recovery significantly slower, with large losses in potential output

> 4 See Narayan et al (2018), since updated by Van Der Weide et al (2021) for past trends in intergenerational mobility.

> 5 This is estimated using returns to education and experience by college status, which are globally estimated using more than 1,000 household surveys across 145 countries (Samaniego et al, 2022).

> 6 Note that such simulation exercises are reliant on a set of underlying assumptions about the future. In this case, the simulations do not account for the possible future compensation of current education losses through accelerated and remedial learning, that learning losses impacts do not vary by age, and that past estimates of returns to education will similarly apply in the future.

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stretching into the future even with sizeable increases in public investment in education and health in the early years to help repair these losses.<sup>7</sup> While these studies focus on the extent and persistence of the economic effects of human capital damage wrought by the pandemic, they are limited to simulating aggregate impacts that do not account for the unequal effects of learning disruptions among socioeconomic groups within a country. This is both due to the design of these studies and the lack of data to observe how different groups are actually impacted. Accounting for these inequities and estimating how they influence social mobility are the ways in which our study adds value to the existing literature on the longer-term impacts of COVID.

The underlying assumption of persistence of human capital impacts of large shocks is supported by empirical evidence from past disasters. Disrupted schooling and the trauma of shocks can adversely impact academic performance and produce differences that are observable years later (Gibbs et al., 2019). For example, the 1982–84 Zimbabwe drought resulted in a delay in starting school of 3.7 months and 0.4 grade less of completed schooling, which led to a 14 percent reduction in lifetime earnings for those children (Alderman et al., 2006). Meyers and Thomasson (2017) found that young people aged 14–17 during the 1916 polio pandemic in the United States later had lower educational attainment compared to slightly older peers. Four years after an earthquake in Pakistan that led to the massive destruction of homes and schools near the fault line in 2005, test scores for children living within 10 kilometers of the fault line were significantly below those of children residing 40 kilometers away. This effect was observed even though households near the fault line were similar in terms of monetary well-being and enrollment rates of children to those further away from the fault line, in part due to the significant aid they received in the year after the disaster (Andrabi et al., 2020).

We focus on mobility in education in this paper for two main reasons, the first of which is that human capital development is a strong component of well-being as well as a key predictor of lifetime earnings. In addition to being important in its own right, relative mobility in education is a good proxy for relative income mobility. It is also an imperfect proxy, since any measure of educational mobility does not reflect inequalities produced by the labor market that are unrelated to differences in human capital, such as the extent to which access to good jobs may be influenced with the privilege (or lack thereof) associated with the socioeconomic class one is born into. Empirically, (relative) intergenerational mobility in education and income are correlated strongly but imperfectly, with a correlation coefficient of around 0.5, across countries for which both measures are available. The correlation tends to be stronger for developing economies than high-income economies – consistent with the intuition that mobility in education and income will be more closely associated, the more similar economies are in terms of returns to education and the better education predicts income in both generations.<sup>8</sup> The second reason for focusing on educational mobility is that its estimation involves fewer methodological and data challenges compared to income mobility. Unlike income, the level of education, once acquired, does not vary across an individual’s lifecycle. Intergenerational data on education is also more widely available than on income, partly because individuals can report their parents’ education level much more accurately than they can recall parental income after years or decades.

The developing world before the pandemic was characterized by low and stagnant levels of intergenerational mobility in education and income alike. The global analysis in Narayan et al (2018), covering nearly 150 economies, found both absolute and relative mobility in education to be significantly higher, on average, in high-income economies than in developing economies, for all generations (10-year cohorts) born between the

> 7 Even a large front-loaded investment program in education and health that injects 12 percent of initial GDP over the short/medium run does not return the economy to its pre-pandemic trend line until twelve years have passed and the cumulative loss in potential output reaches over 20 percent of GDP (Buffie et al, 2022).

> 8 See Narayan et al (2018), Chapter 4 (Figure 4.4a, b).

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1940s and 1980s.<sup>9</sup> Over time, although absolute mobility has been converging between the two groups, progress in the developing world has stalled since the 1960s, even though educational attainment remains much lower than in high-income economies that leaves greater room for offspring to exceed the education level of their parents. On relative mobility, high-income economies have improved more than developing economies have, to the extent that for the 1980s generation, all economies in the bottom 10 percent by relative mobility are developing economies. Among developing economies, progress has been highly uneven – average absolute and relative mobility for the 1980s generation are much lower in Sub-Saharan Africa, and average relative mobility is lower in South Asia, than in the other developing regions.

In the pre-pandemic world, both types of intergenerational mobility were strongly correlated with average income levels of a country. Cross-country correlations also hint at the critical role of policies in reducing inequality of opportunity for children in a society. For example, after controlling for a country’s per capita income, relative mobility is positively and significantly correlated with the level of public expenditure relative to GDP, particularly on education, and with tax revenues as a share of GDP.<sup>10</sup> The pandemic’s effects on education of children could have further reduced intergenerational mobility in many developing economies that were already characterized by low mobility, increasing the risk to long-term inequality. The most important factor is likely to be the large and inequitable impacts of school closures, which left a large share of children in the poorer families, particularly in low-income countries, with little or no access to learning opportunities during school closures (Bundervoet et al. 2021). This would have resulted in wide disparities in learning losses both across and within countries, whose effects on education outcomes are likely to reduce both absolute and relative mobility of the current generation of children.

The effect of school closures on learning could be compounded by an “income effect” of the economic shock to households. For example, in Indonesia, during an economic crisis that reduced GDP by 12 percent in the late 1990s, households responded by cutting school expenditure, particularly among poor households with younger children, which reduced enrollments (Thomas et al., 2004). There is also evidence to suggest that unexpected job losses caused by firm closures and mass layoffs can leave long-lasting impacts on family incomes that in turn affect education of children in low-income families (Oreopoulos et al 2008). The simulations of education losses (measured by Learning Adjusted Years of Schooling) used by our paper consider the income effect of labor market disruptions on education of children to some extent (see section 3.2). Our paper, however, does not capture any additional human capital impacts of closures due to schools being the conduits for other programs (such as nutritional or early childhood interventions), which may fall disproportionately on children from low-income households. To the extent these effects occurred, they would have affected children in poorer households more and likely worsened the overall impacts on intergenerational mobility that we estimate through the school closure channel.

# **3. Data and methodology**

Estimating the impact of the COVID-19 pandemic on intergenerational mobility ideally requires three main ingredients, described in more detail below:

- (i) A baseline measure of educational attainment (in years of education) and inter-generational mobility for the current cohort of students in the absence of the COVID-19 shock.

- (ii) An assessment of learning losses associated with school closures that accounts for differential impacts of school closures both across and within countries.

> 9 Van Der Weide et al (2021) updated the analysis with additional data for some countries and found a similar pattern of results across the developing and high-income economies.

> 10 See Van Der Weide et al (2021) for more details.

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- (iii) Since pre-COVID mobility measures are based on years of education and not learning, one must devise a way of translating COVID-induced learning losses to loss in educational attainment, to be able to measure changes in intergenerational mobility relative to the baseline.

# **3.1 Baseline mobility estimates**

For baseline estimates of inter-generational mobility, we rely on data from the Global Database on Intergenerational Mobility (GDIM) constructed by the World Bank, which contains estimates of absolute and relative intergenerational mobility (IGM) by 10-year cohorts, covering individuals born between 1940 and 1989 (GDIM, 2020). Ideally, we would want to have estimates of inter-generational mobility with and without COVID-19 for a cohort of students who are currently in school. Neither of these is observable, however. Following Neidhofer et al. (2021) we rely on mobility estimates from a cohort of children who are closest in terms of age to the children who were in school in 2020-21, when school closures took place. In the case of GDIM data, this is the 1980s birth cohort, comprised of children who were born between 1980 and 1989, most of whom would have completed their secondary education during the 2000s.<sup>11</sup> How consequential are the data constraints that prevent us from working with a more recent birth cohort? The consequences are limited and defensible in our view, even though the current school-going cohort (who were born during 2000-2015, approximately) would have had better education outcomes than the 1980s cohort in the baseline case (absence of the pandemic) in most developing economies. This is because our analysis is not focused on the absolute changes in educational attainment, but rather on the difference between two scenarios represented by two vectors of educational attainment generated for the same cohort of individuals – one without COVID-19 (baseline) and the other accounting for the impacts of the pandemic (counterfactual). Any secular trend in educational attainment from the 1980s-cohort to the current school-going cohort would affect both scenarios equally and have no effect on the impact of the pandemic obtained from the difference between these scenarios. For similar reasons, Neidhofer et al. (2021) estimates counter-factual mobility measures for the 1987-1994 birth cohort, who are also much older than the children currently in school. Moreover, even though there is a secular time trend in educational attainment for most developing economies, the rate of increase appears to have slowed down over time. As seen in Figure 1, there is a steady increase in enrollments in the early 2000s, but enrollments have flattened out across all income groups of countries over the period 20112019 and remained unchanged since 2012 for the Low-Income Countries.

> 11 The 1980s cohort is the most recent cohort in GDIM for whom educational mobility could be accurately measured, given the dates of the underlying surveys and the fact that educational mobility can only be accurately measured for those who are old enough to have completed their education at the time of the survey.

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impacts of shocks on school dropouts, among other relevant data. The use of scenario-based simulations is not new, but it gained prominence during the pandemic as a tool to help governments assess the potential consequences of a shock of unprecedent magnitude. The main parameters of the simulation model are the following:

- **Learning gains** normally achieved during a regular school year before COVID.  The higher the expected learning gains when schools are open, the higher the learning losses when schools close. These expected learning gains have a positive correlation with countries’ income levels. The higher the income level of the country, the higher the expected learning gain. This parameter remains constant across scenarios (see Table A1 in the annex for a summary of the key input parameters on school closures, mitigation effectiveness, and school productivity (expected learning gain) used in the model to simulate the learning and earning outcomes under different scenarios).

- **Income shocks’ impact on enrollments** .  Simulations also partially capture the (much smaller) potential cumulative effects of household income shocks over the past two years on student school enrollment in primary education. This effect is negligible because evidence from both before and during COVID shows that at the primary-school level, income shocks typically have small effects on enrollment.<sup>13</sup> This component varies across countries based on country-specific enrollment-income elasticities and growth projections and remain constant across scenarios.

- **Observed duration of school closures** , which ranged from a few weeks in some countries to nearly two years in others.  We incorporate the latest country-specific school closure data, which covers two full years of schooling during COVID, from February 2020 to February 2022. As Figure 3 below shows, there are significant differences in the school-opening policies of governments around the world. This component varies across countries and remain constant across scenarios.

- **Partial closure estimates** , the share of students in a school system who are assumed to be affected by partial closures. Partial closures can be by geographic location or by certain grades or can cover all students if a hybrid model is adopted. Very few countries have been able to monitor the share of their system partially closed. This parameter varies across scenarios.

On the issue of mitigation effectiveness, the simulations assume—consistent with the observable evidence— that mitigation strategies, particularly remote learning during school closures, were typically not effective. While some governments were able to respond swiftly to school closures by providing a variety of effective remote learning modalities, many were not. Most notably, 40 percent of countries in Sub-Saharan Africa did not provide any remote learning strategy despite full or partial school closures for about one year (Muñoz-Najar et al. 2021). Even in countries that did provide remote learning solutions, the provision of remote learning did not always result in take-up by students. Surveys of schools and households reveal that many children, especially in low-income countries, were not able to engage in remote learning at all (Meinck et al., 2022; UNESCO, UNICEF, World Bank, and OECD, 2021). Some countries experienced a “remote learning paradox” where the chosen remote learning approach was not suitable to the needs of the majority of the students, contributing to this uneven take-up (Muñoz-Najar et al. 2021). Building on the above-mentioned evidence and data from access to personal computers at home, Azevedo et al 2020 (also used by Azevedo et al 2022), build a set of scenarios of mitigation effectiveness conditional on the different income levels of countries. These results yield a range of mitigation effectiveness in low- and middle-income countries from 5 to 20 percent of what an average student would learn while schools were open.

According to a survey of education ministries by UNESCO, UNICEF, World Bank and OECD (2021), over a third of low- and lower-middle income countries that provided lessons through radio or TV reported that less than half of primary school students were reached by these efforts. Even students who were able to receive

> 13 See Azevedo et al 2021 and Evans and Moskovic 2022 for a summary of that evidence.

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some distance education often spent much less time learning than if they would have during in-person instruction, and were exposed to pedagogies and curricula that had been hurriedly adapted to remote learning. Moreover, teachers often did not receive adequate training in remote instruction and digital skills (UNESCO, UNICEF, World Bank and OECD, 2021). Finally, as discussed below, data from actual measurement of learning losses support the assumption that mitigation was not effective: newly collected data on learning levels emerging from some low- and middle-income countries shows major learning losses across a range of contexts (UNESCO, UNICEF and World Bank, 2021).

For purposes of illustration, we focus our narrative below on estimates of losses underlying the intermediate scenario in Azevedo et al. (2022). The results in terms of global and regional effects on the LAYS do not vary dramatically across the scenarios. Our preference for the intermediate scenario in the narrative builds on our understanding of the evidence to date, which suggests that: the mitigation strategies put in place have largely been ineffective (as discussed above); and many countries with educational systems that reported partial closures (on average for those last two years) had a large fraction of the system fully closed. The parameter choices under the intermediate scenario reflect that evidence. As more and better data becomes available, we will be able to periodically improve these estimates.

Globally, the average baseline LAYS pre-COVID were 7.8 (for low- and middle-income countries, it was 6.8). This means that children around the world only achieve 7.8 years of quality education on average (compared to 11.9 non-quality adjusted years of schooling). Our simulation results suggest that the average Learning - Adjusted Years of School (LAYS) may fall due to COVID-19 school closures. In the intermediate scenario, school closures due to COVID-19 could bring the average learning that students achieve during their lifetime from 7.8 to 6.7 Learning Adjusted Years – a reduction of 1.1 years, as shown in Figure 2.<sup>14</sup> _Figure 2: Results of simulation: Effect on Learning Adjusted Years of Schooling (LAYS)_

> 14 For reference, the 2021 simulations suggested that LAYS would go down from 7.8 to 6.9 under the pessimistic scenario – a reduction of 0.9 years (Azevedo et al. 2021). For this exercise, we interpret 7.8 as the expected Learning Adjusted Years of Schooling (LAYS) for the current generation of students by the time they enter the labor market at age 25. The simulated LAYS, 6.9 in the intermediate scenario, can be seen as revised expected values considering the model’s parameters in the absence of effective remediation.

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learning recovery strategies at a national scale, and only half of the countries have national or regional plans to measure student learning and a quarter of low-income countries do not know how many students have returned to school (UNICEF, 2022).

In order to simulate the pandemic impacts on intergenerational educational mobility, our simulations map learning losses measured in LAYS into educational attainment, measured in years of education. Educational attainment refers to the level of education that an individual has completed. It typically refers to the highest degree or level of education that a person has achieved, such as a high school diploma, bachelor's degree, master's degree, or doctoral degree and is often seen as meaningful proxy for the human capital acquired by an individual. There are several ways to measure educational attainment. The most common method used is selfreport through household surveys. This method involves asking individuals to report their highest level of education completed. This method is quick and easy to administer and has been historically recognized that may be subject to errors if people misremember or misreport their educational achievements.

The pandemic has added another potential source of bias to this measure. Automatic re-enrollment and promotion were one of the earliest recommendations made by several international organizations and adopted by most countries (UNICEF, 2020; ECLAC-UNESCO, 2020). A number of development agencies continue to recommend that countries legally mandate a policy of re-enrolling all students regardless of the duration of their absence (UNESCO, UNICEF, World Bank, and OECD (2022). Such policy recommendations are likely to bias the expected years of schooling upward, at the same time as students experienced a reduction of their exposure to schooling and learning due to the COVID-related school closures. For instance, data from the latest Brazilian school census show a sharp increase in promotions, and a reduction in drop-outs and repetitions across all grades during the pandemic, which have not yet returned to pre-pandemic levels. Rigotti et al (2013) show that the policies of automatic promotion between multi-year cycles of Brazilian states, such as São Paulo, had a significant impact on the value of expected years of schooling when adjusted by age.

As consequence, the pandemic is likely to affect the comparability and subsequent interpretability of selfreported educational attainment through household surveys and expected years of schooling derived from administrative records, such as school census. As country-level data is starting to suggest, it is unlikely that these numbers should be interpreted in the same manner since the actual number of learning hours of these two cohorts is drastically different.

In the current simulations, we are not trying to predict educational attainment or explore the long-term relationship between these variables. We have used the LAYS framework to shock the expected years of schooling by the length of the school closure and further adjusted this value by the lower expected learning in Harmonized Test Scores (HTS) points. This exercise also accounts for the potential income effect on student enrollment, but this last mechanism seems to be of negligible magnitude compared to the other two.

# **Distributional-sensitive measures of national mitigation strategies**

In addition to the national estimates of learning losses due to COVID-19, in the distributionally-sensitive simulations undertaken in this paper, we also rely on information from High-Frequency Phone Surveys (HFPS) collected by the World Bank on the learning experience of children during the periods of school closures. In particular, for a sample of 30 countries, we have nationally-representative estimates on the following questions:

- (i) Were any children attending school before schools were closed due to coronavirus?

- (ii) Have the children been engaged in any education or learning activities in the last week?

- (iii) In what types of education or learning activities have the children been engaged in the last week?

- a. Completed assignments provided by the teacher

- b. Used mobile learning apps

- c. Watched educational TV programs

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- d. Listened to educational programs on radio

- e. Session / meeting with lesson teacher (tutor) f. Other (specify)

A key advantage offered by HFPS data is that it is based on self-reporting (by parents) of whether a particular child was learning during the period of school closures, and if so, what the learning modality was. To our knowledge, this is the only source of cross-country comparable household-level data on actual modalities of learning engagement of children in surveyed households throughout the period of school closures for a large set of developing countries. A second advantage is that the above information can be paired with household characteristics such as parental education and location (rural/urban), to estimate how learning losses were distributed between different socioeconomic groups within a country. While the extent of learning loss for each child must still be estimated using assumptions, the estimate is informed, crucially, by the type of learning engagement that the child participated in, as observed from the HFPS data. In comparison, Neidhofer et al. (2021) are not able to use any observational data on the education of children during the pandemic in their survey sample. Instead, they rely on national level information such as the duration of school closures, mitigation measures (e.g., availability of offline and online learning modalities) that were deployed, and group averages for indirect measures such as internet coverage among different socio-economic groups, to simulate the differences in learning losses of children from different types of households.

While HFPS data offer many advantages that this paper utilizes, it also has a number of limitations that the reader should be aware of. In particular, in light of the mode of data collection, the data is representative of the phone-owning population in each country, which excludes households without access to a phone. Second, in some countries the data was collected by Random Digit Dialing (RDD) while in others the sample was based on a pre-pandemic household survey that collected information on the head of the household, which implies that household heads are over-represented in the second group of countries. Household sampling weights were constructed to adjust for differential response rates among subgroups of the population, in order to approximate nationally-representative estimates. Earlier analysis by Kugler et al. (2021) shows that in countries where surveys collected employment information for all household members, estimates from phone surveys did reasonably well in capturing the disparities and changes in employment outcomes across a number of population groups, including gender, education and area of residence, but fare less well with respect to age group comparisons. Brunckhorst et al. (2022) also compare estimates of employment dynamics from HFPS data with employment estimates from LFS data for a subset of middle-income and high-income countries where LFS data was collected throughout the pandemic and find that employment trends observed in HFPS data are qualitatively similar between HFPS and ILOSTAT estimates with a strong correlation (R<sup>2</sup> =0.882). Moreover, as Brunckhorst et al. (2022) note, the discrepancies in absolute magnitudes of pre-pandemic employment and pandemic job losses between HFPS and LFS data are driven, in part, by the different framing of the prepandemic and pandemic employment questions in HFPS. The analysis in our paper does not rely in any way on those features of the data, as the information we use is limited to the learning modalities of children and parental education.

# **3.3 Simulating the loss in educational attainment from learning losses measured in LAYS**

We undertake three sets of simulations, based on the pre-COVID data on educational attainment and estimates of pandemic-related learning losses, to quantify losses in education attainment and resulting changes in intergenerational mobility for countries.

- (i) <u>Scenario 1 (Global uniform simulations): global simulations, that take national estimates of losses</u> in learning adjusted years of schooling and apply them uniformly to all children within a given country. Thus, these global simulations account for the cross-country heterogeneity in the extent

14
of continued learning during the pandemic, but not the heterogeneity within countries due to unequal engagement with learning during school closures for children from different types of households. These results essentially act as a benchmark scenario showing the impact of school closures on intergenerational mobility in education globally _if_ the learning losses were distributed uniformly within every country.

-

- (ii) <u>Scenario 2 (Distributionally sensitive simulations): simulations account for the heterogeneity in</u> learning losses not only across countries but also within countries, by adding the information from HFPS on learning engagement for every child (and their parental/household characteristics) to estimate a distribution of national learning losses (obtained in Scenario 1) across socioeconomic groups for each country. Limited by the availability of HFPS data, this simulation can be conducted for 30 countries in our HFPS sample.

-

- (iii) <u>Scenario 3 (Global distributionally sensitive simulations): these simulations extrapolate the</u> distributionally-sensitive losses from Scenario 2 to other countries that are not in our HFPS sample, to obtain global estimates of changes in educational mobility that account for differential learning experiences within countries.

In these simulations, the key difficulty with imputing losses in learning-adjusted years of schooling (LAYS) into the GDIM data on which inter-generational mobility estimates are based, is that the cardinal measures of years of schooling are not exactly the same. In the GDIM, we observe self-reported completed years of education for those born between 1980 and 1989. In contrast, in the World Bank/UNESCO data, COVID-related changes in the learning-adjusted expected years of schooling (LAYS) are based on the most recent School Census and relate to the expected (but yet unobserved) years of schooling for the current education cohort. Likewise, the stock of, and changes in, learning-adjusted years of schooling take account of both the quantity and the quality of schooling, combined into a single metric, whereas the GDIM makes no quality adjustments.

Expected years of schooling (EYS) is a cohort measure, which is prospectively measured using a synthetic cohort. The actual years of schooling from age 7 to 14 can be observed and known; however, this procedure requires 7 years of observation over the span of the cohort’s time in school. Alternatively, it is possible to carry out a retrospective reconstitution of this cohort, but data used to do so would be outdated for older individuals. The synthetic cohort borrows measurements of several real cohorts, observed during a period _t_ , projected as though it would have been observed for ages at the respective times. Since period measurements can be taken in a single year and refer to data that are updated for that year, the synthetic cohort makes it possible to overcome the difficulties of real cohorts related to lengthy observation times and outdated data. EYS can also be calculated for periods as short as a single year (Figure 5).

_Figure 5: Diagram Representing the Composition of Multicohorts, in the Period Measurement and in the Synthetic Cohort_

||14|||8|||||||8||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
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|||2020|2021|2022|2023|2024|2025|2026|2027|2028|2029|2030|
||||Year||||S|ynthetic|Cohort||||

Source: Author’s illustration.

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<!-- Start of picture text --> Inputs: | | Estimated national average loss in Information on children’s engagement in learning National average loss in years of education in<br>ofexpected schooling, and Source:in learning WAG adjusted years estimates activitieseducation. Source:during the HEPS pandemic, by parental GDIM dataset. Source: Output in column A<br>National r Average learning loss index,by parental<br>1980's cohort.average Source: years GDIM of educd at asetion in education. Source: Output in column B<br>Share of children in each parental education<br>group (to be used as weights). Source: HAYS<br>+ Eatienate ¢ Estimate ¢ Estimate<br>Rules: + Dy follwing) $ By following: $ By following<br>Impute the relative loss in learning adjusted Assign a “loss score” to each type of learning |) For each parental education group, the<br>relativeyearsof schooling,into an equivalent activity,and calculate the indox as the woighted averageloss of yearsafeducationin GDIM data<br>GDIM data.loss inThenthe years subtract thatof schooling average.In the averageparental learning educationloss score group for children in each isindex proportionalJusing same to theratio averagefor all learningeducationloss groups)<br>ii) The national average loss in years of<br>education in GOIM is equal to the weighted<br>average of the average loss of years of<br>education in GDIM by parental education<br><!-- End of picture text -->
values of δj that sum up to the national estimate of the learning losses, given the educational distribution in that country.

# **4. Main results**

In analyzing the impact of learning losses on inter-generational mobility, we draw on the mobility measures in Van der Weide et al. (2021). Specifically, we consider mobility in the space of all children and the maximum level of education among the parents, as it provides a more accurate measure of parental human capital and resources available to the household. For absolute mobility, our main measure is the share of respondents who have attained more years of education than their parents, conditional on their parents not having the maximum number of years of education, such that everyone has a chance of surpassing their parents. For relative mobility, we consider the correlation coefficient between the years of schooling of the respondent and of the most educated parent as the measure of inter-generational persistence, and (1-COR) is then a measure of relative mobility. In this section, we present three sets of results, as stated earlier: (i) simulations with uniform learning losses within countries; (ii) simulations that account for within-country heterogeneity in learning across different households; and (iii) global simulations that extrapolate the distributional patterns to non-HFPS sample countries.

## **4.1. COVID-19 impacts on mobility: Uniform learning losses within countries**

We start with the benchmark case in which the average loss in learning for each country is applied uniformly to all children in that country. While this may not be a realistic scenario for reasons we discuss in detail below, it offers a useful benchmark for two reasons: first, it does not rely on quite as stringent data requirements necessary for capturing the within-country heterogeneity in learning modalities that we only observe for a subset of countries, thus allowing us to estimates the impacts of COVID-19 on intergenerational mobility across all countries in the GDIM; and second, it allows us to focus on the significant heterogeneity in country-level responses to the pandemic and resulting differences in the duration of school closures or availability of mitigation measures.

We estimate that children’s educational attainment, as measured in GDIM in years of education of the 1980s birth cohort, could fall by more than a full year (1.2 years overall) on average, and this impact is more pronounced in middle income countries (particularly in UMICs), whereas it is relatively smaller in LICs and HICs. Across regions, larger losses in educational attainment are estimated for LAC (over 2 years), whereas losses are relatively small at 0.7 years in the SSA regions. These patterns are a reflection of the heterogeneity in the extent of school closures across regions and country groups; for instance, schools were closed for 70 percent of the school year weeks in LAC and SAR, compared to only a third of the school year weeks in regions such as SSA, ECA and EAP.

To understand the magnitude of the implied changes in educational attainment, it helps to examine them against their historic evolution (Figure 11). Across all country groups we can observe a steady increase in educational attainment from the 1940s birth cohort to the 1980s birth cohort. The simulated impacts of COVID-19, which take the educational attainment of the of the 1980s birth cohort as its baseline, imply, for instance, that for the UMIC and HIC countries, the loss in educational attainment induced by the pandemic would be equivalent to reversing the progress made by the 1970s and 1980s cohorts compared to the 1960s cohort. For LMICs and LICs the impact is less pronounced, but even for LICs, where the loss magnitude is the smallest, the loss in educational attainment is equivalent to reversing most of the progress made by the 1980s cohort over the 1970s cohort.

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the effectiveness of alternative learning methods vis-à-vis scenario 2. Finally, in scenario 4 {0.1; 0.3; 1}, we make still more optimistic assumptions about the effectiveness of alternative learning, making it relatively closer to the effectiveness of in-person interactions vis-à-vis scenarios 1-3, where it remained relatively closer to the outcomes of no learning.

The loss indices assigned to each learning modality do not have an intrinsic socio-economic gradient. A child from any household who is engaged in learning by way of direct interactions with their teachers during the period of school closures is assigned the same loss index (e.g. a loss index of 0.3 in Scenario 1). However, the incidence of learning modalities is not uniform across different population groups. Students whose parents have higher levels of education tend to have lower loss index, on average, across all regions with the exception of ECA, where the loss index varies very little by parental education (Figure 16). This is driven by the fact that students whose parents have lower levels of education are more likely to report no learning during school closures, or less effective learning modalities, whereas students with better educated parents have a higher prevalence of learning based on direct interactions or completing assignments from their teachers.

Accounting for the differential effectiveness of different learning modalities, and differences in the use of each modality across population groups, reveals a socio-economic gradient in the magnitude of learning losses. For instance, under Scenario 1, while the mean absolute loss in years of education in our sample is 1.4 years, this varies from 1.5 years for children whose parents have less than primary education to 1.33 years for children with parents who have tertiary education – a 13 percent differences between the top and bottom parental educational categories. Notably, the difference in relative losses is even greater, given the much lower average years of schooling attained by these two groups of children when they reach adulthood (6.9 years of education and 13.3 years of education respectively). For those whose parents have at most pre-primary education (ISCED0), the COVID-19 related loss in educational attainment amounts to 23 percent of the no-COVID baseline, compared to 10 percent among those whose parents have tertiary education (ISCED5). The strong socio-economic gradient in learning losses, both absolute and relative, quantifies what has been suspected and speculated often but not estimated in the case of most developing countries – that education disruptions due to COVID-19 is likely to have worsened pre-existing educational inequality between socio-economic groups in most countries. Notably, such inequality is commonly considered to be a key component of inequality of opportunity in a society.

How do these losses of educational attainment translate into changes in inter-generational educational mobility? As before, we examine the impact of learning losses associated with school closures on both absolute mobility and relative mobility. Figure 17 plots the share of children who either have more years of education than their parents, without the impacts of COVID-19 school closures, and also accounting for distributional losses under two scenarios (i) uniform losses; and (ii) distributionally sensitive losses. The estimates suggest relaxing the assumption made in the previous section of uniform losses within countries worsens the decline in absolute mobility in a number of countries (e.g. Argentina, Costa Rica, Georgia, Myanmar); but in most countries the main loss in absolute mobility is on account of the overall loss in educational attainment, rather than the differential impacts across groups with different education levels of parents. Thus, the more pronounced relative decline in the number of years of education lost due to COVID-19 does not translate automatically into a smaller share of children with more education than their parents in most countries.<sup>18</sup>

> 18 These results are not affected by alternative loss index assumptions for the vast majority of countries, with the exception of Guatemala and Chile, where absolute mobility declines further in alternative scenarios.

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The simulations suggest that the extensive school closures and associated learning losses are likely to have a significant impact on both absolute and relative inter-generational educational mobility. This is a direct result of the uneven distribution of learning losses, in both absolute and relative terms within countries. For example, children with parents who have less than primary education are, under certain assumptions, estimated to experience almost double the average absolute loss in years of education relative to pre-pandemic levels compared to children of parents with tertiary education. This is driven by differences in extent and type of learning engagement of children from different socioeconomic groups during school closures. Notably, the uneven distribution of relative losses within countries would have occurred even if aggregate losses of a country were uniformly distributed, just because the same absolute loss is much larger in relative terms among those with lower levels of expected educational attainment. On the whole, education disruptions due to COVID-19 are likely to have worsened pre-existing educational inequality between socio-economic groups in most countries, which would in turn increase inequality of opportunity in the society.

The effects on intergenerational mobility are significant as a result. The decline in absolute mobility that is attributable to the pandemic is particularly strong for high and upper-middle income countries, which reinforces their declining trend in absolute mobility between the cohorts of the 1970s and 1980s. While a smaller decline in absolute mobility due to the pandemic is estimated for low and lower-middle income countries, the impacts should still be a cause for concern. After all, these are groups of countries where absolute mobility has grown very slowly since the 1960s cohort and remains lower than in high and upper-middle income countries, in spite of their much lower levels of educational attainment that leave plenty of scope for members of each generation to exceed the education levels of their parents. Across developing regions, a COVID-induced decline of absolute mobility amounts to 9 percentage points in LAC, by 7 percentage points in ECA and over 5 percent in the EAP and MENA regions, relative to the baseline of no-COVID. This is a significant impact in terms of its potential impact on upward mobility of families. To a large extent, the impacts on absolute mobility are driven by the extent of aggregate learning losses in each country rather than the distribution of learning losses (by parental education) within countries. The differences in the pandemic’s impacts on absolute mobility, therefore, largely reflect differences in the aggregate impact of school closures across countries.

In contrast to the impact of the pandemic on absolute mobility, the impact on relative mobility is driven entirely by the distribution of learning losses within countries. This in turn is a consequence of unequal access to (or participation in) continued learning during school closures across children of different socio-economic backgrounds, proxied by the (maximum) education level of their parents. The impacts are significant, considering the historically slow-moving nature of this measure.<sup>19</sup> Intergenerational persistence (which is inversely related to relative mobility) is projected to increase by an average of almost 4 percent in middle-income countries, and even higher under some more optimistic learning effectiveness scenarios. For developing countries, the impact is equivalent to 40 percent of the change in mobility between the 1970s and 1980s cohorts, and up to 80 percent under alternative scenarios. Note that the fact that more optimistic assumptions about the effectiveness of alternative learning methods lead to more negative impacts of the pandemic on relative mobility reinforces the concerns related to the fact that in low-income families there was less learning of any kind during the period of school closures.

Importantly, relative mobility in high-income economies was improving before the pandemic, while, at the same time, falling in developing economies. This implies that while the pandemic may have reversed some of the gains in relative mobility made by high-income countries since the generation of the 1970s, in developing countries the pandemic may have worsened an already declining trend in relative mobility. Across developing regions, the pandemic would have led to an estimated decline in relative mobility of more than 4 percent in LAC, 3 percent in EAP, and more than 2 percent in the MENA region.

> 19 See Narayan et al (2018), since updated by Van Der Weide et al (2021) for past trends in intergenerational mobility.

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These estimates are subject to a number of important caveats, which are typical of simulations of a long-term indicator like social mobility. Such simulations are on the one hand necessary, since the evolution of such indicators cannot be observed till well into the future, which would be too late to take mitigating policy steps. But on the other hand, simulations require strong assumptions, as described in detail in earlier sections of the paper. The results are not highly sensitive to some of our assumptions, such as the exact choice of “loss-ratios” assigned to different modalities of learning while schools were closed. Other assumptions, however, are likely to be significant, such as those that use the educational attainment of the 1980s cohort and estimated learning losses in LAYS to simulate the expected outcomes of the pandemic cohort (with and without the pandemic). While different ways of modeling these relationships may well lead to different results, our assumptions attempt to strike the right balance between what is theoretically and empirically defensible and doable for a sizeable number of developing countries. Given this, our findings are best seen as illustrative and reflecting what might happen under the assumptions of the model, rather than being a statistical prediction of the future. For these reasons, the qualitative interpretation of our results, in terms of their patterns across countries and relative magnitudes compared to historical trends, are more instructive than the precise numbers they generate. The latter could also change (with the exact same model and assumptions) as new data or data for missing countries become available, as they inevitably will over time.

Interpreted thus, the results from our simulations highlight serious implications for long-term poverty and inequality through the channel of education disruptions of children, even when other potential channels of impact of school closures (such as nutrition, health, mental health, social and behavioral development of children) on human capital development are ignored. Stalling or declining absolute mobility in education, particularly in developing countries, can be a driver of intergenerational poverty traps since education offers the most significant ladder to upward mobility. Declining relative mobility, particularly in countries where it was already low and declining, represents worsening of inequality of opportunity that could lead to higher income inequality over time. The worsening of both types of mobility also implies a steep cost in potential longterm economic growth, as human potential is underutilized. In societies already suffering from the stress caused by the pandemic followed by the recent economic woes including the effects of war and inflation, lower social mobility can also increase the threat to social stability.

Finally, it is important to recognize that the effects estimated here on long-term outcomes are not inevitable or irreversible. Loss of learning, however distributed, need not become a permanent loss in educational attainment if the right remedial measures are taken in time. But the time window is limited, and the more delayed these interventions are, the more costly and less effective they are likely to be to recover the learning losses. The urgency of addressing such longer-term risks can also sometimes get lost or de-emphasized in the face of pressing economic challenges whose effects are more apparent, which would be a mistake. Our simulation results hopefully will help strengthen the arguments for focusing on the human development impacts of the COVID-19 pandemic on children, even as other pressing short-term challenges are tackled; and also spur the development of policies that are able to minimize the education disruptions caused by future shocks including pandemics.

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