fluid situation by presenting a range of estimates that come from simulations based on three scenarios:<sup>8</sup>

1. Optimistic – schools are closed only for 3 months of a 10-month school year, and the effectiveness of mitigation measures put in place by governments (such as remote learning) is high.

2. Intermediate – schools are closed for 5 months, and the mitigation measures have a middle level of effectiveness.

3. Pessimistic – schools are closed for 7 months, and the mitigation measures have low levels of effectiveness.

The goal is to provide a reasonable range of estimates that can help ministries of education and their development partners plan recovery strategies when schools reopen. Such strategies, if well planned and executed, can prevent these learning losses from becoming permanent.<sup>9</sup> This paper differentiates between the mitigation strategies that countries have put in place during school closures and the steps they may take to provide compensatory education to students once schools open. It does not focus on remediation, and the results here should be seen as evidence of its importance.<sup>10</sup> The policy responses for the various phases of coping with the pandemic, transitioning back to open schools, and having schools operating are laid out well in Rogers and Sabarwal (2020).

The paper is structured as follows. Section 2 provides a brief review of relevant literature. Section 3 describes the analytical framework and empirical methodology. Sections 4 and 5 present the results and discuss the main findings, respectively. Section 6 concludes. Methodological details and a detailed description of the main indicators are outlined in the annexes.

# **2. Literature Review**

_Related simulations of the impact of COVID-19 on educational outcomes_ A number of teams have recently undertaken analysis of likely learning losses stemming from COVID-19. Most have focused on the United States and other high-income countries<sup>11</sup> but estimates have also been developed for a selection of low- and middle-income countries.<sup>12</sup> These analyses have focused on a range of grades and subjects. The effects of these analyses have mostly

> 8 In all three scenarios the paper utilizes a conservative estimate of school dropouts based exclusively on expected losses to national income derived from global macro projections such as the World Bank Macro Poverty Outlook (MPO) from June 2020. These dropout-income elasticities are computed for children 4-11 as well as for children 1217. This dropout effect would likely be higher if it were to include non-income related channels such as school safety concerns and school disengagement. However, available data do not allow either of these channels to be quantified convincingly at a global level. We are also making assumptions on availability, take-up, and effectiveness of remote learning, which are based on the scarce literature on the effectiveness of remote learning and household information on access of alternative learning modalities such as television and internet using the PISA, DHS, and MICS household questionnaire. In addition, we are also making assumptions regarding the expected learning observed in one school year, these are made based on the literature on school productivity, unexpected school closures, and summer learning loss (for more information see Annex A.1).

> 9 See for instance the policy response options described in Rogers and Sabarwal, 2020.

> 10 Although much of the same framework used here would still hold, allowing for a future extension to include an explicit component of remediation.

> 11 Kuhfeld and Tarasawa (2020), Kuhfeld et al. (2020) and Dorn et. al (2020).

> 12 Cummiskey et al. (2020).

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