Economic resilience in times of public health shock: The case of the US states |
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| Institution: | 2. Department of Economics, Morgan State University, Baltimore, 10587, MD, USA;3. Lewis Honors College, University of Kentucky, 1120 University Dr., Lexington, 40526, Kentucky, USA;1. Department of Accounting, Economics, and Finance, Southeast Missouri State University, One University Plaza, Cape Girardeau, MO 63701 USA;2. Professor of Economics & Finance, School of Business, Baldwin Wallace University, 275 Eastland Road, Berea, OH 44017-2088 USA;3. Professor of Economics and Finance, Economics & Finance Department, Middle Tennessee State University, Murfreesboro, TN 37132 USA;1. Faculty of Economics, Ryukoku University, 67 Tsukamoto-cho, Fukakusa, Fuhimi-ku, Kyoto 612-8577, Japan;2. Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan;1. Department of Economics and Management, Thomas Sankara University, 12 PO Box 417 Ouagadougou 12, Burkina Faso;2. Department of Economic Policies and Internal Taxation, West African Economic and Monetary Union Commission, 01 PO Box 543 Ouagadougou 01, Burkina Faso |
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| Abstract: | Does adopting social distancing policies amid a health crisis, e.g., COVID-19, hurt economies? Using a machine learning approach at the intermediate stage, we applied a generalized synthetic control method to answer this question. We utilize state policy response differences. Cross-validation, a machine learning approach, is used to produce the “counterfactual” for adopting states—how they “would have behaved” without lockdown orders. We categorize states with social distancing as the treatment group and those without as the control. We employ the state time-period for fixed effects, adjusting for selection bias and endogeneity. We find significant and intuitively explicable impacts on some states, such as West Virginia, but none at the aggregate level, suggesting that social distancing may not affect the entire economy. Our work implies a resilience index utilizing the magnitude and significance of the social distancing measures to rank the states' resilience. These findings help governments and businesses better prepare for shocks. |
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| Keywords: | Machine Learning and Causal Inference |
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