of continued learning during the pandemic, but not the heterogeneity within countries due to unequal engagement with learning during school closures for children from different types of households. These results essentially act as a benchmark scenario showing the impact of school closures on intergenerational mobility in education globally _if_ the learning losses were distributed uniformly within every country.

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- (ii) <u>Scenario 2 (Distributionally sensitive simulations): simulations account for the heterogeneity in</u> learning losses not only across countries but also within countries, by adding the information from HFPS on learning engagement for every child (and their parental/household characteristics) to estimate a distribution of national learning losses (obtained in Scenario 1) across socioeconomic groups for each country. Limited by the availability of HFPS data, this simulation can be conducted for 30 countries in our HFPS sample.

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- (iii) <u>Scenario 3 (Global distributionally sensitive simulations): these simulations extrapolate the</u> distributionally-sensitive losses from Scenario 2 to other countries that are not in our HFPS sample, to obtain global estimates of changes in educational mobility that account for differential learning experiences within countries.

In these simulations, the key difficulty with imputing losses in learning-adjusted years of schooling (LAYS) into the GDIM data on which inter-generational mobility estimates are based, is that the cardinal measures of years of schooling are not exactly the same. In the GDIM, we observe self-reported completed years of education for those born between 1980 and 1989. In contrast, in the World Bank/UNESCO data, COVID-related changes in the learning-adjusted expected years of schooling (LAYS) are based on the most recent School Census and relate to the expected (but yet unobserved) years of schooling for the current education cohort. Likewise, the stock of, and changes in, learning-adjusted years of schooling take account of both the quantity and the quality of schooling, combined into a single metric, whereas the GDIM makes no quality adjustments.

Expected years of schooling (EYS) is a cohort measure, which is prospectively measured using a synthetic cohort. The actual years of schooling from age 7 to 14 can be observed and known; however, this procedure requires 7 years of observation over the span of the cohort’s time in school. Alternatively, it is possible to carry out a retrospective reconstitution of this cohort, but data used to do so would be outdated for older individuals. The synthetic cohort borrows measurements of several real cohorts, observed during a period _t_ , projected as though it would have been observed for ages at the respective times. Since period measurements can be taken in a single year and refer to data that are updated for that year, the synthetic cohort makes it possible to overcome the difficulties of real cohorts related to lengthy observation times and outdated data. EYS can also be calculated for periods as short as a single year (Figure 5).

_Figure 5: Diagram Representing the Composition of Multicohorts, in the Period Measurement and in the Synthetic Cohort_

||14|||8|||||||8||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||13|||7||||||7|||
||12|||6|||||6||||
|ge<br>|11|||5||||5|||||
|A<br>|10|||4|||4||||||
||9|||3||3|||||||
||8|||2|2||||||||
||7|||1|||||||||
|||2020|2021|2022|2023|2024|2025|2026|2027|2028|2029|2030|
||||Year||||S|ynthetic|Cohort||||

Source: Author’s illustration.

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