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We estimate the economic impacts of COVID-19 in the U.S. using a disaster economic consequence analysis framework implemented by a dynamic computable general equilibrium (CGE) model. This facilitates identification of relative influences of several causal factors as "shocks" to the model, including mandatory business closures, disease spread trajectories, behavioral responses, resilience, pent-up demand, and government stimulus packages. The analysis is grounded in primary data on avoidance behavior and healthcare parameters. The decomposition of the influence of various causal factors will help policymakers offset the negative influences and reinforce the positive ones during the remainder of this pandemic and future ones., (© 2022 Elsevier B.V. All rights reserved.)

We present a formal analysis of the macroeconomic impacts of the COVID-19 pandemic in the U.S., China and the rest of the world. Given the uncertainty regarding the severity and time-path of the infections and related conditions, we examine three scenarios, ranging from a relatively moderate event to a disaster. The study considers a comprehensive list of causal factors affecting the impacts, including: mandatory closures and the gradual re-opening process; decline in workforce due to morbidity, mortality and avoidance behavior; increased demand for health care; decreased demand for public transportation and leisure activities; potential resilience through telework; increased demand for communication services; and increased pent-up demand. We apply a computable general equilibrium (CGE) model, a state-of-the-art economy-wide modeling technique. It traces the broader economic ramifications of individual responses of producers and consumers through supply chains both within and across countries. We project that the net U.S. GDP losses from COVID-19 would range from $3.2 trillion (14.8%) to $4.8 trillion (23.0%) in a 2-year period for the three scenarios. U.S. impacts are estimated to be higher than those for China and the ROW in percentage terms. The major factor affecting the results in all three scenarios is the combination of Mandatory Closures and Partial Reopenings of businesses. These alone would have resulted in a 22.3% to 60.6% decrease in U.S. GDP across the scenarios. Pent-up Demand, generated from the inability to spend during the Closures/Reopenings, is the second most influential factor, significantly offsetting the overall negative impacts., Competing Interests: Conflict of InterestThe authors declare no potential conflicts of interest with respect to the research, authorship and/or publication of this article., (© Springer Nature Switzerland AG 2020.)

While most of the attention to COVID-19 is being focused on the physical transmission of the virus across country borders, there is also an analogous spatial transmission of economic impacts through international trade and global supply chains. This paper presents an analysis of the extent to which the economic shocks of mandatory closures to mitigate the pandemic ripple through the world economy. We utilize a state-of-the-art computable general equilibrium (CGE) model to analyze these interconnections through international trade. We compare estimates of the impacts on US GDP in isolation and then examine the impacts taking into account US trade with China and the rest of the world (ROW). Our analysis indicates that these international trade linkage impacts are generally negative and range from near zero to very large overall, depending on the region, and that own- and cross-country impacts differ by region as well. At the same time, we find that China is able to capitalize on the situation by actually being able to increase its exports through international trade following mandatory closures in other regions. We also confirm that the US economy was relatively insulated from trade linkages with the rest of the world. Sectoral impacts provide further insight into the results., Competing Interests: Conflict of interestThe authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)