not statistically significant (see the Appendix for more details). We focus on this panel dataset to assess the change in learning from 2019 to 2021 and to identify student and teacher characteristics associated with the change in learning outcomes.

The effects of the COVID-19 pandemic on the mathematics achievements of children were estimated using a simple difference in average learning outcomes in 2019 (pre-COVID) and 2021. We compare this average change to the benchmark for annual progress as obtained from the literature review to assess the extent to which students learned over time during COVID as compared to non-COVID years.

As different schools were closed for different time periods due to COVID-19 and for other nonpandemic-related reasons, we add a variable for the duration of school closure Zi to analyze the effect of duration of school closure on learning trends and test our second hypothesis (H2). Furthermore, we add a set of student characteristics Xi to test the effect of student characteristics on learning progression and test H3 to H6. The overall empirical equation is represented by: 2021 2019 Where ∆yij = yij - yij is change in learning outcomes of an individual student (i) in school (j) from 2019 to 2021 and εij is an error term clustered at the school level. The constant α captures the average change in learning due to the pandemic. In this setup, we add one variable at a time to assess heterogeneity in the trend in learning outcomes by each student characteristic; Xi includes student sex, parental education, index of student access to and usage of digital resources including internet, index of student-level information on learning continuity during COVID-19 induced school closures, indices of student-reported teachers’ pedagogical and classroom management skills and family support in learning. At the end, we include all variables together to evaluate the strength of the different factors on trends in learning outcomes.

# _Benchmarks for Annual Progress_

As we have data for only two time periods, we compare the learning trend observed from 2019 to 2021 for students in Uzbekistan to the average expected rate of learning per year. The World Bank’s simulations of COVID-19-induced learning loss assume a progress rate of 0.40 SD per year (Azevedo et al., 2021) based on student outcomes data in the Programme for International Student Assessment (PISA). In the United States, Hill et al. (2008) report annual gains for the age range of 8-11 years based on nationally normed tests in math and reading. Their reported annual gains in math are from 0.89 SD at age 8 to 0.41 SD at age 11. Analysis of growth trajectories of reading and math achievement drawing upon multiple sources of national assessment data in the United States shows annual gains of 0.40 standard deviation in math for grades 5 to 8 during the past two decades (Lee 2010). Similarly, in low-middle-income settings in Pakistan, Bau et al. (2021) estimate that when controlling for students appearing in consecutive assessments and for family characteristics, the value of annual learning gains is approximately 0.39 SD. Based on this review, we use the benchmark average annual learning gain of 0.40 SD to compare the learning trend observed for students in Uzbekistan.

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