for pay outside the household pre-COVID and having worked in the household enterprise or at the household farm pre-COVID (with or without pay).

In addition to individual level characteristics, we also study correlations with the household’s pre-COVID standard of living using an asset index and with the household’s current size and location of residence (urban/rural). See Table A2 in the Appendix for a list of all variables.

First, to capture how the probability and the main correlates of participation in school have changed during the COVID-19 pandemic, we estimate the following regression specification (equation 2): SchoolAtti,t = α+ βPostCOVID t + γX′i,t + δ(PostCOVID t × X<sup>′</sup> i,t) + εi,t where SchoolAtti,t is equal to 1 for children in school, and 0 for those out of school, PostCOVID t is an indicator variable, that takes the value 1 for post-COVID rounds with schools fully open (Uganda: partially open), X′i,t is a vector of the correlates of school attendance listed above, and PostCOVID t × X<sup>′</sup> i,t an interaction term of this term with the post-COVID indicator.

The coefficient β is interpreted as the impact of the pandemic on school attendance in the reference group, the vector of coefficients γ captures the pre-COVID correlates of school attendance, while δ captures the changes in these associations during the pandemic. The post-COVID correlation between certain covariate and school attendance is given by the sum of γ+ δ , we report this sum as the marginal effect of a certain covariate at the end of the regression Tables 2 and A4, with p values referring to the significance of this sum.

Second, in addition to the changes in the correlates of the main predictors of school attendance pre- and post-COVID, we are interested in the difference of these changes by gender, i.e., whether the way the association between our explanatory variables and school attendance changed over time was different for adolescent boys vs. girls. We therefore estimate the following regression (equation 3), which provides some insights into whether the pandemic impacted girls’ and boys’ school participation differently, and if there were any mitigating factors to this difference.

SchoolAtti,t = α+ β1PostCOVID t + β2gender i + γX′i,t + δ(PostCOVID t × X<sup>′</sup> i,t) + ε(gender i × X<sup>′</sup> i,t) + θ(PostCOVID t × gender i × X<sup>′</sup> i,t) + εi,t All terms are the same as in equation 2, the added terms are gender i × X<sup>′</sup> i,t , is the interaction between gender and all other covariates, and PostCOVID t × gender i × X<sup>′</sup> i,t , is a triple interaction term between post-COVID survey round indicator, gender and other covariates.

The coefficients ε indicate the difference in the effect of certain covariates between male and female adolescents pre-COVID, while θ captures if there were any changes in the gender-specific impacts of covariates over time. We also report the marginal effects of every single covariate on girls’ school attendance post-COVID in Tables 3 and A5. These marginal effects are hence equal to the sum of γ+ δ+ ε+ θ , and show the impact a covariate X on girls’ school attendance in the post-COVID survey round.

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