post-pandemic gender gap. While the results from this descriptive analysis do not strictly measure the causal impact of the COVID-19 pandemic on the gender gap, they provide compelling evidence for the direction of change in the educational gender gaps across different countries of our sample during the time of school closures. Given the salience of the pandemic in driving these school closures, it is highly likely that the observed pre- and post-pandemic changes are connected to the COVID-19 crisis.

Subsequently, we estimate multivariate regressions with interactions to analyze the correlations of individual- and household-level characteristics with school attendance, and the differences in these correlations by gender and time period (pre- and post-pandemic). In contrast to the regressions in the descriptive analysis, these regressions only include data from the baseline, and the last available round of the HFPS when schools were fully open, with the exception of Uganda, where the sample includes the last available HFPS round with education data, with schools still only partially open. This part of the analysis covers only five countries, Burkina Faso, Ethiopia, Malawi, Nigeria and Uganda. As the Kenyan RRPS dataset’s sampling is not solely based on the KIHBS dataset we use as a baseline for Kenya in the descriptive analysis, we cannot match all RRPS households and individuals to their baseline data<sup>12</sup> and needed to exclude the country from the multivariate regressions.

We follow the literature in considering typical individual and household determinants of school attendance, conditional on availability in our data. In addition to gender, we look at correlations with the age of children, because we expect that older children have dropped out of school with a higher probability, for multiple reasons such as being above the compulsory school age or having a higher opportunity cost of schooling. The child’s ranking in the household, i.e., whether they are the oldest sibling, could also influence school attendance, if parents are more or less likely to spend all their resources on the education of one chosen child (see, for example, Kaul 2018 in India). Similarly, children who are direct descendants (children or grandchildren) of the household head may be privileged in their school attendance if the household cannot afford to pay for the education of all children.

We also look at parental education, as children with a mother or father with more than primary education have been shown to attend school longer in most country contexts (e.g., Roby et al, 2016). Since parental education is not available in the post-COVID HFPS rounds we exploit the panel dimension of the surveys to complement the post-COVID data with information from the country-specific baseline. We consider parental education as generally time-invariant, so that it should not make much of a difference if the data are collected pre- or post-COVID.

In our analysis, we also examine the relationship between children's employment status and their school attendance. Based on previous research, we anticipate a negative correlation between employment and school attendance (e.g., Boozer and Suri, 2001; Heady, 2003). Like our approach for parental education, we use pre-COVID employment status to predict post-COVID school attendance, since we do not observe children post-COVID employment status. However, while parental education is essentially time variant, employment status is expected to change over time. Despite this limitation, we anticipate that the child's pre-pandemic employment status will have a direct and indirect impact on post-COVID school attendance.<sup>13</sup> To capture children’s employment status, we use two indicator variables: having worked

> 12 This means the following variables are not available for Kenya: pre-COVID employment of the adolescent, parental education (mother’s and father’s), and the pre-COVID asset index.

> 13 For a discussion on the potential long-term effects of child labor on school attendance and achievement, see for example Beegle et al., 2008; and Udry, 2006.

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