a few cases do the questionnaires allow identifying if a child attends school in person. This is an important caveat when interpreting the data presented in this study.

Besides the HFPS (and RRPS) and LSMS data, we also draw on UNESCO’s Dashboard on the Global Monitoring of School Closures (already referenced in the introduction). The data show for each country and day of the pandemic (starting on Feb. 16, 2020), whether schools were closed due to COVID-19, closed due to an academic break, partially open or fully open (UNESCO 2022).<sup>9</sup>

# **3. Methodology**

Our methodology consists of 1) a descriptive analysis of gender gaps before and after the COVID-19 pandemic,<sup>10</sup> and 2) multivariate regressions that analyze the main correlates of school attendance, and how these correlates differ by gender and time period (before and after-pandemic).

First, we test if there were any significant differences between the school attendance of adolescent girls and boys before and after the full reopening of schools (or during the last available round of data). In this analysis, we aim to capture the size of the gender gap before and after the pandemic, and especially whether there were any changes in the school attendance gender gap during the COVID-19 pandemic.

For all countries and rounds separately, we estimate the respective weighted<sup>11</sup> mean school attendance for boys and girls between the age of 10 and 18. An adolescent is defined as attending school if he or she has been reported to currently going to school at the time of the survey. Then we test whether these weighted means are significantly different from each other by gender in each survey round. We show the results of these tests in bar graphs, where the significance of the gender gap is captured by the color intensity of the bars. In appendix (A2), we also run simple regressions that estimate school attendance as a function of gender, a time indicator for the survey round, and an interaction of these two. The regression equation is as follows (equation 1): SchoolAtti,t = α+ β Round t + γ genderi + δ (genderi × Round t) + εi,t where SchoolAtti,t is an indicator variable equal to 1 for children in school, and 0 for those out-of school, Round t is an indicator for the survey round, genderi is a gender indicator, equal to 0 for male school children / students and 1 for female school children / students, and genderi × Round t is an interaction of these latter two. γ captures the pre-pandemic gender gap, β the change in boy’s school attendance post-pandemic, δ the change in the school attendance gender gap pre- vs. post-pandemic and γ+ δ the programs on the radio, across countries. For instance, in the countries examined in this paper, the proportion of households where children continued learning during school closure varies from 87.6% in Burkina Faso to 19.6% in Kenya, with the proportion reaching 66.6% in Kenya, 73.4% in Malawi, 61.8% in Nigeria, and 58.9% in Uganda (Bundervoet et al. 2021).

9 Partially open can imply that schools were open in some regions, for some grade levels, age groups, etc. or following a hybrid approach (e.g., a combination of in-person and distance learning).

10 Throughout this paper, we refer to these as pre- and post-pandemic gender gaps. It is important to note that all survey rounds that were conducted after the start of the pandemic are described as post-pandemic. In other words, post-pandemic does not mean after the pandemic had ended, but rather refers to after the start of the pandemic. 11 All our estimates reported in this paper use weights constructed in the processing of the survey data. The information reported by households is adjusted by sampling weights based on the baseline survey for each country. Weights are in addition adjusted for attrition, and post-stratified.

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