- d. Listened to educational programs on radio

- e. Session / meeting with lesson teacher (tutor) f. Other (specify)

A key advantage offered by HFPS data is that it is based on self-reporting (by parents) of whether a particular child was learning during the period of school closures, and if so, what the learning modality was. To our knowledge, this is the only source of cross-country comparable household-level data on actual modalities of learning engagement of children in surveyed households throughout the period of school closures for a large set of developing countries. A second advantage is that the above information can be paired with household characteristics such as parental education and location (rural/urban), to estimate how learning losses were distributed between different socioeconomic groups within a country. While the extent of learning loss for each child must still be estimated using assumptions, the estimate is informed, crucially, by the type of learning engagement that the child participated in, as observed from the HFPS data. In comparison, Neidhofer et al. (2021) are not able to use any observational data on the education of children during the pandemic in their survey sample. Instead, they rely on national level information such as the duration of school closures, mitigation measures (e.g., availability of offline and online learning modalities) that were deployed, and group averages for indirect measures such as internet coverage among different socio-economic groups, to simulate the differences in learning losses of children from different types of households.

While HFPS data offer many advantages that this paper utilizes, it also has a number of limitations that the reader should be aware of. In particular, in light of the mode of data collection, the data is representative of the phone-owning population in each country, which excludes households without access to a phone. Second, in some countries the data was collected by Random Digit Dialing (RDD) while in others the sample was based on a pre-pandemic household survey that collected information on the head of the household, which implies that household heads are over-represented in the second group of countries. Household sampling weights were constructed to adjust for differential response rates among subgroups of the population, in order to approximate nationally-representative estimates. Earlier analysis by Kugler et al. (2021) shows that in countries where surveys collected employment information for all household members, estimates from phone surveys did reasonably well in capturing the disparities and changes in employment outcomes across a number of population groups, including gender, education and area of residence, but fare less well with respect to age group comparisons. Brunckhorst et al. (2022) also compare estimates of employment dynamics from HFPS data with employment estimates from LFS data for a subset of middle-income and high-income countries where LFS data was collected throughout the pandemic and find that employment trends observed in HFPS data are qualitatively similar between HFPS and ILOSTAT estimates with a strong correlation (R<sup>2</sup> =0.882). Moreover, as Brunckhorst et al. (2022) note, the discrepancies in absolute magnitudes of pre-pandemic employment and pandemic job losses between HFPS and LFS data are driven, in part, by the different framing of the prepandemic and pandemic employment questions in HFPS. The analysis in our paper does not rely in any way on those features of the data, as the information we use is limited to the learning modalities of children and parental education.

# **3.3 Simulating the loss in educational attainment from learning losses measured in LAYS**

We undertake three sets of simulations, based on the pre-COVID data on educational attainment and estimates of pandemic-related learning losses, to quantify losses in education attainment and resulting changes in intergenerational mobility for countries.

- (i) <u>Scenario 1 (Global uniform simulations): global simulations, that take national estimates of losses</u> in learning adjusted years of schooling and apply them uniformly to all children within a given country. Thus, these global simulations account for the cross-country heterogeneity in the extent

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