4 Thus, while the shock has a persistent impact on GDP decades after the COVID-19 shock is gone, there are no persistent _differences_ after ten years.

Our study makes several contributions to the literature, for example on the macroeconomic impact of pandemics (e.g., Barro et al., 2020; Coibion et al., 2020; Fernandez-Villaverde and Jones, 2020; Guerrieri et al., 2020; Jorda et al., 2020; Ng, 2021).<sup>5</sup> Given new technologies for mitigating schooling loss, these should be taken into account when assessing the impact of future pandemics on human capital.

While other studies on the global learning losses due to COVID-19 have appeared independently of this study – Azevedo et al. (2020, 2021), Hanushek and Woessmann (2020) and Psacharopoulos et al. (2021) estimate the impact of disruptions to schooling – we are to our knowledge the first to thoroughly quantify the human capital effects of COVID-19 by income level: (i) considering _both_ education and employment/experience losses; (ii) relying on returns to education and experience that were consistently estimated for the world using 1,084 household surveys across 145 countries;<sup>6</sup> (iii) performing a thorough analysis of the education and experience shocks using evidence on school closures, access to and effectiveness of distance learning, and on disruptions to employment for different types of workers;<sup>7</sup> (iv) examining the output dynamics associated with the shocks; and (v) studying how they vary by gender. Lastly, Hanushek and Woessmann (2020) study OECD economies only and Fuchs-Sch ̈undeln et al. (2020) and Jang and Yum (2022) focus on the distributional effects of school closures.

We also contribute to a sparse literature on the relationship between experience and economic development (Manuelli and Seshadri, 2014; Lagakos et al., 2018b,a; Islam et al., 2019; Jedwab et al., 2021c), as well as a large literature on the link between 5See Jedwab et al. (2020b) and Beach et al. (2020) for recent surveys of the literature on past pandemics. 6In contrast, the three studies assume similar returns to education across countries. In Azevedo et al. (2020, 2021) the returns also do not vary across education levels. Hanushek and Woessmann (2020) then consider measures of the returns to learning (test scores), which are typically only available for highincome countries. Lastly, we discuss how sensitive our results are to implementing important robustness checks when estimating the returns, for example related to cohort effects and self-employment.

> 7Psacharopoulos et al. (2021) have a uniform assumption across countries about access to and effectiveness of distance learning. Azevedo et al. (2020, 2021) consider several school closure scenarios but do not study scenarios by income group. They also only use data on access, not effectiveness. Our results (first circulated in September 2020) were developed independently of the estimates provided by Psacharopoulos et al. (2021) (May 2020), Azevedo et al. (2020) (June) and Hanushek and Woessmann (2020) (September). While these studies have done a thorough job studying learning losses from COVID19, their objective was to rapidly share their results with policy makers. Now two years into the pandemic, we use novel information on the various dimensions listed in the text.