Trade, Migration, and Pandemics: the Effect of COVID-19 Containment Policies

Description

The 2019 novel coronavirus (COVID-19) pandemic has caused enormous loss of lives worldwide and brought the global economy into a deep recession. Due to the deepening of globalization and urbanization, infectious disease outbreaks have become more common in recent years, including SARS in 2003, H1N1 in 2009, MERS in 2012, Ebola in 2014, and Zika in 2015. How to cope with pandemics while reaping the benefits of globalization remains a long-term challenge for humanity.This project studies disease dynamics and the effect of containment policies in spatial economies with interregional trade and endogenous migration. We build a multi-region Susceptible-Infected-Recovered (SIR) model with production, trade, consumption, and migration. Agents weigh the risk of infection and economic opportunities across regions to choose an optimal location for work and life in each period. Hence, agents automatically practice social distancing by avoiding high-risk areas. Our model predicts that if migration cost is sufficiently low, then the global basic reproductive number R0 can be greater than one even if the local R0 is smaller than one. However, when global R0 coincides with local R0, further tightening of migration control becomes ineffective in reducing R0. Consequently, there is scope for optimal mobility control.We apply our theory to the US economy. We provide reduced-form evidence using smartphone user data, which suggests that the risk of infection at the destination reduces bilateral migration flows. We also show that US state policies to contain the pandemic effectively reduced the number of COVID-19 cases. Finally, we calibrate the model to 50 US states and run counterfactual simulations to evaluate the effect of observed as well as alternative containment policies on the pandemic and social welfare.