variable and with the binary variables for education type as well as _engagement_ and _discipline_ as additional regressors.<sup>21</sup> Second, to improve identification, we leverage these two time points available and estimate the model with child fixed effects (ψi): where _educ_ stands for either _remote_ , _alternative_ or _inperson_ . At times, more than one of these variables (e.g., inpersonihct and remoteihct) are included simultaneously. Vector Wihct includes all explanatory variables of interest described above as well as the control variables in Xihct (from the previous equation) that vary over time.<sup>22</sup> Finally, we again adjust the (robust) standard error (vihct) to reflect the survey structure. In this case, we rely only on (within) variation over time for each individual, such that all factors that are constant between September 2020 and March 2021, and could confound our results, are eliminated.<sup>23</sup> Getting rid of the unobserved heterogeneity in this case comes with the additional advantage of mitigating potential issues of reporting bias that could affect the dependent variable, as some groups of the population may be more likely to report changes in their children’s behavior.

# 5. Results

## _5.1. Closed Schools_

Children were sent home from school in March 2020, and most had to wait until early January 2021 to return to the classroom. This section analyzes the consequences of these nine months of closed schools for children, and to what extent students relied on remote and alternative forms of learning during this period.<sup>24</sup>

### _5.1.1. Remote Learning_

RRPS data indicate that less than 40 percent of Kenyan households currently have internet access and around 10 percent reported owning a computer before the pandemic.<sup>25</sup> Moreover, only around 5 percent of children (who were not currently attending school) reported having access to remote learning between September and November 2020.<sup>26</sup> While over 11 percent of children in private schools learned remotely during that period, fewer than 4 percent of their peers in public schools did so (p=0.05).

Regression results corroborate the existence of a digital divide (Table 1). Children (aged three and older) living in poor households are on average 6 percentage points less likely to learn remotely when schools are closed than children living in non-poor households. This seems to be driven in particular by poor girls, who are particularly at risk (column 2).<sup>27</sup> Experiencing food insecurity is also associated with a reduced probability (between 4 and 4.6 percentage points) of having access to remote education.

> 21 In this part, we do not include the variable that accounts for the education of the household head, as it yields mostly insignificant coefficients and, due to limited availability, would reduce the sample by almost half. Results are nevertheless similar with and without this variable.

> 22 Vector Wihct includes: engagementhct, disciplinehct, poorihct, childhungerhct, urbanhct, powergridhct, internethct, publicihct, nchildhct, feelanxioushct. Moreover, including county fixed effects is redundant in this case, as very few children moved between waves.

> 23 Given that the analysis is limited to two periods, this is equivalent to specifying the model in first differences.

> 24 Figure A1, in the appendix, shows the adoption rates of remote and alternative forms of education by different groups of the population.

> 25 Data on internet access and computer ownership became available only in January 2021 (wave four), and therefore is not included in the baseline regressions throughout this section. In the 2019 Kenya Population and Housing Census, only 22.6 percent of individuals were reported using the internet and 10.4 using computers (KNBS 2019).

> 26 Between September and November 2020 (wave three), almost 60 percent of children who learned remotely did so via the internet.

> 27 Including other interaction terms, such as between female and age or female and number of hours spent on house chores, did not yield significant results (not shown).

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