The Long-Term Effects of Covid-19 School Closures
After the outbreak of the COVID-19 crisis in the spring of 2020, politicians worldwide closed schools and child-care centers in an effort to contain the virus. According to the World Bank, around 1.6 billion school children were affected by these closures at their peak (World Bank 2020). Education is a crucial determinant of future wages, and schools are an important driver of intergenerational mobility (Kotera and Seshadri, 2017; Lee and Seshadri, 2019). We thus ask the question: what are the long-run economic impacts of Covid-related school closures on the affected children?
To answer this question, we build a model that features public schooling and monetary and time investment of parents as inputs into the human capital production of children. The human capital production function features self-productivity, i.e. human capital builds on itself, and complementarity, i.e. the higher the human capital, the more productive is investment into human capital (Cunha and Heckman, 2007; Cunha, Heckman and Schennach, 2010). At the age of 16, high-school students decide whether to stop schooling and start working, to complete high school, or to obtain a college degree. The terminal school degree as well as the human capital accumulated during the schooling period together determine the wages of the children once they enter the labor market. We calibrate the model to data from the US, and take heterogeneity of parental households with respect to marital status, education, and assets into account. Parental characteristics affect not only the innate ability of the children, but also the optimal investment into the children. In this framework, we model the school and child-care closures as a drop in governmental investment into children corresponding to school closures of six months. While on average schools in the US were closed longer, this baseline takes into account at least partial effectiveness of remote schooling.
We find that the children affected by the school closures on average suffer long-run earnings losses of -0.7%. Summing the net present value of these losses up for all affected children, they amount to -1.1% of the 2019 US GDP. Thus, the earnings losses caused by the school closures are substantial. For the children, they translate into welfare losses of on average -0.45% as measured by the consumption equivalent variation. An important driver of the long-term wage losses are changes in the final educational attainment of the children: the share of college educated children falls by -2.7%, and the share of high school drop-outs increases by 4.6%. These negative effects emerge despite the efforts of parents to offset the impact of school closures by increased parental time and resource investments into their children’s education. For the children, the negative effects of the temporary school closures are much more important than the negative effects caused by the economic recession. Younger children are affected more than older children, given that human capital builds on itself. The loss of schooling is thus larger the earlier in the schooling period it occurs.
Parental characteristics are a crucial determinant of the magnitude of the earnings and welfare losses for children from the COVID-19 crisis. Married, better educated, and asset-rich parents have more financial means to provide additional investment into their children during the school closures: children from the most disadvantaged households experience three times larger welfare losses than children from the most privileged households. This difference becomes even larger if one takes into account that children from disadvantaged households experience longer effective school closures. There is evidence that they are more likely to live in school districts that closed schools and at best implemented remote learning, that they participate less in remote schooling, and that they return to opened schools later. Assuming that the effective school closures amount to 12 months for children from the most disadvantaged households, but only 3 months for children from the most privileged households, the welfare difference more than quadruples to 14 times higher welfare losses for the most disadvantaged children.
Cunha, F and J Heckman (2007): The Technology of Skill Formation, American Economic Review, 97(2), 31-47.
Cunha F, J Heckman and S Schennach (2010): Estimating the Technology of Cognitive and Noncognitive Skill Formation, Econometrica, 79(3), 883-891.
Kotera, T and A Seshadri (2019): Educational Policy and Intergenerational Mobility, Review of Economic Dynamics, 25, 187-207.
Lee, S Y and A Seshadri (2019): On the Intergenerational Transmission of Economic Status, Journal of Political Economy, 127(2), 855-921.
World Bank (2020): The Human Capital Index 2020 Update: Human Capital in the Time of COVID-19.