using the 2019 distribution from World Bank’s Poverty and Inequality Platform and the per capita GDP growth rates forecasted prior to the pandemic (World Bank, 2020). Poverty for both years is calculated using the same anchored-SPL threshold define above. The counterfactual poverty rates give us a baseline of poverty levels had there been no COVID-19 pandemic and are used to estimate the “excess” poverty due to the pandemic.

The estimate of _CPY_ – the additional number of people living in poverty each year – is the difference between the country specific number of poor estimated for 2020 and 2021 and the number of poor using the counterfactual distribution.

# _Future poverty from learning losses_

School closures, one of the primary social distancing policies enacted in many countries to limit COVID-19 transmission, can severely impact the future human capital of current school-age children. Various studies have estimated the aggregate income loss from reduced future earnings (if such learning loss is not remediated). Globally, estimates of the present value of future earning losses would range between US$10 trillion (Azevedo et al. 2021 and Psacharopoulos et al. 2021) and US$21 trillion (Schady et al. 2023), depending on the effectiveness of mitigation measures. More recently, Jedwab et al. (2023) project a welfare loss equivalent to a one-off loss of 63, 55, and 36 percent of the annual income in high-, middle-, and low-income countries, respectively.<sup>12</sup> Overall, this amounts to US$50 trillion.

Our alternative approach adopts a neoclassical growth model with a detailed treatment of human capital (the Long-Term Growth Model (LTGM) with the human capital extension (LTGM-HC)).<sup>13</sup> We apply the LTGM to evaluate the impact of school closures on future income levels _for each country in our sample_ due to lower human capital. The future income losses generated by the LTGM are then used to calculate the _FPY_ . We summarize the methodology below.

Cross-country data from UNESCO records the length and intensity of school closures in 2020-2021.<sup>14</sup> We then convert the school closure durations into Learning-Adjusted Years of Schooling (LAYS) lost - a measure that takes into account the national average quality of schooling (Kraay 2018 and Filmer et al. 2020). Next, we calculate the impact of school closures on the average human capital of the workforce from 2020 to 2100 in the LTGM-HC by tracking the human capital of successive population cohorts.<sup>15</sup> We then estimate future GDP per capita growth under the pre-pandemic baseline trend and the scenario with school closures from the pandemic. As a neoclassical growth model, GDP in the LTGM is calculated 12 To compare with Jedwab et al (2023), our estimates of future GDP losses (results below) would be equivalent to a one-off loss of 28, 45, 54 and 46 percent of the 2020 GDP in high, upper-middle, lower-middle, and low-income countries, respectively. The main reason for the different pattern is that Jedwab et al (2023) include losses in the return to experience, which is more important for high-income countries.

13 For more details about the LTGM, see Loayza and Pennings (2022) or visit https://www.worldbank.org/LTGM. 14 The UNESCO data records for each day of 2020 and 2021 whether schools are fully closed, partially closed, or open.

15 For example, consider a one-year loss of formal schooling for 15–19-year-olds in 2020-2021. Adjusting for the (median) quality of education, the cohort losses are 2/3 of one LAYS, leading to an 8% fall in the cohort’s future productivity – based on international empirical evidence of an 8% return to raw years of schooling, the LTGM-HC applies a 12% return to one LAYS. The oldest cohort members enter the workforce immediately, leading to an instant reduction in human capital of the workforce. Human capital growth continues to be depressed until 2035, when the youngest cohort at school age during the pandemic enters the labor force.

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