declines in relative mobility are consistent with those previously estimated for Latin American countries (Neidhofer et al., 2021) and significant, given the evolution of relative mobility over the 1950-1980 birth cohorts.<sup>4</sup> Notably, the simulations presented in this paper, like the ones in Neidhofer et al. (2021) present the impacts on mobility in the absence of remedial interventions in post-pandemic years, and should thus be viewed as upper bound estimates of the long-term damage that the school closures could inflict and not as predictions for the future. That said, some of the emerging evidence that we discuss suggests that broad-based and rapid recovery of learning losses, particularly in developing countries, and particularly among children from disadvantaged households, is unlikely.

The rest of the paper is structured as follows. Section 2 provides the context and rationale for the choice of intergenerational mobility in education as the outcome of interest and discusses how the paper fits into and adds value to the literature on longer-term socioeconomic impacts of the pandemic. Section 3 provides a description of the data employed in the analysis and the empirical methodology. Section 4 presents an overview of our main results of uniform loss simulations, as well as distributionally-sensitive simulations that employ additional information on pandemic learning from high frequency phone surveys. Section 5 extrapolates from the distributionally-sensitive simulations to inform global simulations of distributionally-sensitive pandemic impacts on intergenerational mobility. Section 6 concludes.

# **2. Context and motivation**

Estimates from the World Bank for a sample of 157 countries suggest that COVID-19 could lead to a loss of between 0.3 and 0.9 years of schooling adjusted for quality; close to 7 million students from primary up to secondary education could drop out due to pandemic-related income shocks; and students from the current cohort could face a loss in lifetime earnings that is equivalent to a 5 percent annual reduction of income (Azevedo et al 2021). In several countries in Sub-Saharan Africa, fewer than 1 in 5 primary school students have maintained contact with their teacher since school closures (Josephson et al., 2020).

Our study fits well with the growing literature on assessing the possible longer-term impacts of the pandemic through the channel of human capital losses. The most comprehensive attempt to do so on a global scale so far has been by Samaniego et al (2022), who quantifies the potential long-term economic impact of disruptions in schooling and work experience caused by the pandemic across 145 countries at different levels of development.<sup>5</sup> Both lost schooling and experience are estimated to contribute to significant losses in global learning and output, with developed countries incurring greater losses than developing countries because they have more schooling to start with and higher returns to experience.<sup>6</sup> Fuchs-Schundeln (2022) estimates that school closures in the US on average lead to a reduction of life-time earnings of 1.8 percent for affected children. Another recent study by Buffie et al (2022) analyzes the medium-term macroeconomic impact of the pandemic and associated lock-down measures on low-income countries caused by the degradation of human capital, using a dynamic general equilibrium model. They show that the persistence of loss-of-learning effects on labor productivity is likely to make the post-COVID recovery significantly slower, with large losses in potential output

> 4 See Narayan et al (2018), since updated by Van Der Weide et al (2021) for past trends in intergenerational mobility.

> 5 This is estimated using returns to education and experience by college status, which are globally estimated using more than 1,000 household surveys across 145 countries (Samaniego et al, 2022).

> 6 Note that such simulation exercises are reliant on a set of underlying assumptions about the future. In this case, the simulations do not account for the possible future compensation of current education losses through accelerated and remedial learning, that learning losses impacts do not vary by age, and that past estimates of returns to education will similarly apply in the future.

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