and individuals. These surveys were typically designed to be nationally representative but may undercover groups with poor network connection or limited access to mobile phones. In addition to the HFPS, corresponding LSMS baseline surveys are used from 2018-2020 as a reference of the pre-pandemic time period.

While HFPSs were conducted in 27 Sub-Saharan African countries, we had to restrict our analysis to surveys that (a) were accessible to the research team, and (b) collected at least in one round individuallevel information on school attendance (as opposed to asking household-level questions about all the household’s children combined). The latter criterion is extremely important for the purpose of this study because only individual-level data allow for sex-disaggregation and the analysis of potential intrahousehold heterogeneity in school attendance. Based on these two exclusion restrictions, we can draw on 13 rounds of HFPS data collection for five African countries (Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda) and 4 rounds of RRPS for Kenya (see Table A1, Appendix, for additional information on each survey).

The HFPSs conducted in Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda were implemented with support from the World Bank’s Living Standard Measurement Study (LSMS) team. These five surveys share several common features – in particular, they all interviewed a subset of respondents from a previous inperson survey that was conducted under the umbrella of the Living Standard Measurement Study Integrated Surveys on Agriculture (LSMS-ISA) program.<sup>6</sup> This allows linking data collected on households and respondents in the HFPS to a rich set of pre-COVID 19 data (conducted between 2018 and early 2020). Moreover, the survey operations allowed for specific survey modules being rotated in and out over time to answer the most pressing policy-relevant questions (Zezza et al. 2021). For this study, it is particularly important that some HFPS rounds collected data on school attendance for all children aged 5 to 18 in the household, which allows for sex-disaggregated analysis.<sup>7</sup> The Kenya COVID-19 Rapid Response Phone Survey differs from the HFPSs in several ways. First, it used a more complex sampling approach, with one part of the sample being drawn from the 2015/16 Kenya Integrated Household Budget Survey (KIHBS), and the other part of the sample being obtained through Random Digit Dialing. The latter involved a random selection of active phone numbers from the 2020 Numbering Frame of the Kenya Communications Authority (Sinha 2022). Because of this sampling approach, households in the Kenya RRPS cannot be easily linked to a pre-COVID 19 survey. The second difference concerns the questionnaire, which uses a different (though broadly comparable) set of questions and only collects information on school attendance for children aged up to 17 years.

Most HFPSs ask, for each age-eligible child, “Is [NAME] currently going to school?” to identify if a child is attending school or not (though with some variations across country-rounds, see Table A1.B). This, however, does not necessarily establish that children were attending school in person but could also imply, for example, continued engagement with school content (e.g., through distance learning).<sup>8</sup> Only in

> 6 The Living Standards Measurement Study, and its Integrated Surveys on Agriculture extension project provide nationally representative, high-quality household survey data on Sub-Saharan African countries, with thematic modules on education, labor market participation, agriculture and other relevant topics.

> 7 In Malawi, however, this information was only collected for children aged 15-18.

> 8 Harmonized household-level indicators from the HFPS reveal significant variation in children's engagement in any learning activities during school closures, including attending remote teaching sessions or listening to educational

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