5 schooling and development (Klenow and Rodriguez-Clare, 1997; Hall and Jones, 1999; Bils and Klenow, 2000; Hendricks, 2002; Caselli, 2005; Hanushek and Woessmann, 2012; Schoellman, 2012; Caselli and Ciccone, 2013, 2019; Jones, 2014, 2019; Hanushek et al., 2017; Flabbi and Gatti, 2018; Hendricks and Schoellman, 2018, 2022). While the macroeconomics literature has looked at the long-term impact of economic downturns or economic transitions or the impact of structural transformation through human capital,<sup>8</sup> the long-term impact of disruptions to human capital accumulation due to pandemics has, to our knowledge, not been studied in this literature. We highlight the importance of experience when estimating global learning losses due to COVID-19.<sup>9</sup> In addition, we contribute to the literature on gender inequality and economic development,<sup>10</sup> as well as the literature on macroeconomics and gender. The latter literature focuses on understanding the causes of increased female labor force participation over time, or the differences in labor market outcomes by gender over the business cycle.<sup>11</sup> Alon et al. (2020), Alon et al. (2021) and Albanesi and Kim (2021) find that, in the U.S., the COVID-19 shock had a disproportionately severe impact on female employment compared to male employment. In contrast, Goldin (2021) argues that the main difference in the impact on employment is by education group, not by gender. We take into account gender differences in schooling, as well as in the returns to human capital, finding that the short run impact is greater on women, but that the long run impact is similar to men, across countries at different levels of development.

Furthermore, we focus on understanding the global and long-lasting effects pandemics might have on income for men and women through disruptions to human 8Studies on downturns include Ljungqvist and Sargent (1998), Davis and Von Wachter (2011) and Kahn (2010). Studies on economic transitions include Dauth et al. (2019) and Jedwab et al. (2021c). Studies on structural change include Schoellman and Hobijn (2017), Islam et al. (2019) and Buera et al. (2020). 9Human capital could be accumulated at work _passively_ or _actively_ . There is a long tradition of modeling growth through learning-by-doing, see Arrow (1962), Uzawa (1965), Lucas (1988) and Jovanovic and Nyarko (1996). An alternative would be to assume that human capital requires investment and that agents are compensated for hours worked, as in Ben-Porath (1967) and Rosen (1972). In such a framework it might be that, since active learning is front-loaded, initial wages are actually higher than measured (assuming reported hours include hours spent studying at work) so the returns to experience might be biased upwards in richer nations if there is more learning there. See Jedwab et al. (2021c) for a discussion of why this bias is unlikely to be significant. See Ma et al. (2021) for contradictory evidence.

> 10See Cuberes and Teignier (2014) for a survey of the literature. Other studies include: Galor and Weil (1996); Lagerlof (2003); Greenwood et al. (2005); Doepke and Tertilt (2009, 2018); Hyland et al. (2020).

> 11See Acemoglu et al. (2004), Fernandez et al. (2004), Olivetti (2006), Attanasio et al. (2008), Ngai and Petrongolo (2017), Erosa et al. (2017), Attanasio et al. (2018) and Albanesi and Sahin (2018).