lower than before the pandemic (see Figure 2). In Burkina Faso, Ethiopia, Malawi and Nigeria, attendance rates in the latest post-COVID 19 survey were higher than those in the pre-pandemic LSMS survey. Only in Uganda, where schools had only partially re-opened when the latest HFPS round with an education module was fielded, had school attendance rates not (yet) returned to pre-pandemic levels. These aggregate rates, however, may mask significant heterogeneity in the impacts of COVID-19 on different groups of adolescents, which are the focus of this section.

Similar to the regressions in Appendix Table A3, our dependent variable is an indicator variable of whether a child attends school. In addition to a post-COVID 19 indicator variable, our vector of independent variables includes a host of current individual and household-level characteristics, such as sex, age, household size, whether the child is a descendent of the household head (i.e., a child or grandchild), whether the child is the oldest adolescent in the household, and urban locality, as well as time-invariant parental characteristics (i.e., mother’s and father’s education).<sup>15</sup> We further include a set of indicator variables that capture the child’s pre-COVID employment status and whether the household had above median-assets pre-pandemic. While both children’s employment status and family wealth are likely to have changed over the course of the COVID-19 crisis, they provide important baseline information. All explanatory variables are interacted with the post-COVID 19 indicator variable to assess whether there are differences in the associations between these variables and children’s school attendance pre- and post-COVID 19. The regressions are run separately for each of the five countries included in this section.

Table 2 summarizes the results, with a focus on whether the coefficient estimate is positive or negative, and its significance level (see Table A4 for the full set of regression results). For each individual and household-level characteristic included in the regressions, we show the sign and significance of the coefficient estimate (indicating the pre-COVID correlation, rows labeled #1), of the interaction with the post-COVID indicator variable (indicating how the correlation changed pre- vs post-COVID, rows labeled #2) and of the combined effect (i.e., the sum of the two, indicating the post-COVID correlation, rows labeled #3).

Overall, the correlates of school attendance pre-COVID 19 are broadly in line with expectations. In general, younger adolescents are more likely to be in school than older adolescents. Being a descendant of the household head is associated with an increased probability of attending school in all countries but Malawi (where this coefficient is positive but not significant), which could mirror that household heads favor their own offspring over other children in the household (i.e., children of other household members). Being employed pre-pandemic strongly decreased a child’s likelihood of attending school, and this association holds for children who worked for pay outside the household as well as for children who worked in household enterprises or family farms (except in Ethiopia in the latter case). Children whose parents had at least primary education were much more likely to attend school, compared to children with parents without any education, and this association holds for both mothers’ and fathers’ education. Except for Ethiopia, children in wealthier households were more likely to attend school than children in household with below-median assets. For some of the other characteristics – e.g., household size, urban-rural locality and whether the child is the oldest child in the household – the results were less conclusive, as the coefficient estimates were either insignificant and/or varied across countries. It is worth noting however,

> 15 Information on parental education is not available for Uganda.

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