A third set of assumptions are related to the effectiveness of mitigation _(m)_ strategies. We assume that remote learning is never as effective as classroom instruction. It is hard to keep children engaged cognitively with all the distractions in the household, devices have to be shared between siblings, and it can be hard for families to decipher television programming. Moreover, access to a television or internet (the main channels of delivering remote learning) is highly unequal. We also assume that the economic shock that families are experiencing will also have detrimental effects on the ability of children to make effective use of any available mitigating strategies, especially as family incomes drop, family and child food security worsen, and household stress increases.

In our simulation, we bring together three elements, the government supply (or expected coverage) of alternative education modalities (G), the ability of households to access (or take-up) these alternative modalities (A), and the effectiveness of the alternative modalities (E). Building on existing household surveys, such as the MICS, DHS, and other multitopic household surveys, we were able to identify the share of households with access to internet, computer, mobile phones, land lines, radio, and television (Table 2). This information helped us shape some of our main scenarios. We assumed that all governments (G) were offering some type of alternative modality, but household access (A) and the effectiveness (E) of these modalities were heterogenous depending on the income of the country.

**Table 2: Household access to technology**

||||**Sh**|**are of house**|**holds possess**|**ing:**<br>||
|---|---|---|---|---|---|---|---|
|||**mobile**||||**internet**|**personal**|
|**Income Level**|**Indicator**|**telephone**|**radio**|**telephone**|**television**|**access**|<br>**computer**|
|HIC|share (%)|||||78.8|80.8|
||countries|||||48|48|
|UMC|share (%)|92.1|51.2|18.7|83.3|41.2|43.5|
||countries|12|12|12|12|41|42|
|LMC|share (%)|84|43.7|7|58.5|19|20.9|
||countries|23|23|23|23|33|33|
|LIC|share (%)|74.8|49|3.1|34.8|6|6.6|
||countries|24|24|24|24|20|21|
|Column Average|share (%)|81.8|47.3|7.8|53.9|43.8|45.3|
|Column Total|countries|59|59|59|59|142|144|
|Source: UNICEF as|of May 28<sup>th</sup>2|020(https://publ|ic.tableau.c|om/profile/unice|fdata#!/vizhome|/EduViewv1_|0/home)|

In many lower-income countries, online learning options have limited utility. Not only do many households lack internet connections, but when available, these may not be fast enough for downloading.<sup>52</sup> In addition, households may have no computer or, might not have a sufficient number for the parents and children to use, or for multiple children to use. We use the information

> similar range is observed in developing countries. Singh (2019) estimates a much higher productivity in Vietnam (0.45sd) than in Peru (0.2sd), and intermediate values for India and Ethiopia. Jones (2017) estimates schooling productivity of 0.2-0.3sd in Tanzania, Uganda, and Kenya. In Brazil, both states and municipalities have responsibility for education within their jurisdictions, with the municipality being the dominant provider of primary education. For Brazil, Azevedo, and Goldemberg (2020) estimate schooling productivity at the municipal level, finding a range of 0.04-0.56sd and an average of 0.3sd, also in line with the literature.

> 52 1.5M bps are needed according to the EdTech team of the World Bank.

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