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COVID-19 and Global Economic Growth: Policy Simulations with a Pandemic-Enabled Neoclassical Growth Model

During the COVID-19 pandemic of 2019/2020, authorities have used temporary ad-hoc policy measures, such as lockdowns and mass quarantines, to slow its transmission. However, the consequences of widespread use of these unprecedented measures are poorly understood. To contribute to the understanding of the economic and human consequences of such policy measures, we therefore construct a mathematical model of an economy under the impact of a pandemic, select parameter values to represent the global economy under the impact of COVID-19, and perform numerical experiments by simulating a large number of possible policy responses. By varying the starting date of the policy intervention in the simulated scenarios, we find that the most effective policy intervention occurs around the time when the number of active infections is growing at its highest rate -- that is, the results suggest that the most severe measures should only be implemented when the disease is sufficiently spread. The intensity of the intervention, above a certain threshold, does not appear to have a great impact on the outcomes in our simulations, due to the strongly concave relationship that we identify between production shortfall and infection rate reductions. Our experiments further suggest that the intervention should last until after the peak established by the reduced infection rate, which implies that stricter policies should last longer. The model and its implementation, along with the general insights from our policy experiments, may help policymakers design effective emergency policy responses in the face of a serious pandemic, and contribute to our understanding of the relationship between the economic growth and the spread of infectious diseases.

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