Economic Resilience in the times of Public Health Shock: The Case of the US States

Does adoption of social distancing policy during a health crisis e.g., COVID-19 hurt economies compared to those that did not? We apply Generalized Synthetic Control Method—based on a machine learning approach in the intermediate stage —to assess the economic impact. We do so by exploiting the variations in states’ response to the policy. Popular machine learning technique of cross-validation is used in a preliminary stage to create the ‘counterfactual’ for the adopting states – how these states ‘would have behaved had they not adopted stay-home/lock-down orders. We categorize states that have undertaken social distancing (SD) policies as the treatment group; and those that did not as control; and use the state time-period for fixed effects, adjusted to eliminate potential selection bias and endogeneity. We find significant and intuitively explicable policy impact on some states e.g., West Virginia but none at the aggregate level, suggesting that SD policy might not hurt the overall economy as anticipated by some quarters. We construct a resilience index using the magnitude and significance of the impact of SD measures to identify the states that exhibited stronger resilience; and ranked them based on responses. The findings provide policymakers and businesses with insights that might assist them to better prepare for shocks.