Difang Huang, Ying Liang, Boyao Wu, Yanyi Ye
arXiv 20 May 2024 · Econometrics · publishedEmpirical Economics (2024) · 1 citations (OpenAlex)
arXiv:2405.12180 · PDF · DOI · OpenAlex · Extracted main text
We identify the effectiveness of social distancing policies in reducing the transmission of the COVID-19 spread. We build a model that measures the relative frequency and geographic distribution of the virus growth rate and provides hypothetical infection distribution in the states that enacted the social distancing policies, where we control time-varying, observed and unobserved, state-level heterogeneities. Using panel data on infection and deaths in all US states from February 20 to April 20, 2020, we find that stay-at-home orders and other types of social distancing policies significantly reduced the growth rate of infection and deaths. We show that the effects are time-varying and range from the weakest at the beginning of policy intervention to the strongest by the end of our sample period. We also found that social distancing policies were more effective in states with higher income, better education, more white people, more democratic voters, and higher CNN viewership.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bai, Jushan, and Serena Ng (2021) Matrix completion, counterfactuals, and factor analysis of missing data, Journal of the American Statistical Association\/ 116,… | 0.941 | 6 | 4 | 83% |
| 2 | Chernozhukov, Victor, Hiroyuki Kasahara, and Paul Schrimpf (2021) Causal impact of masks, policies, behavior on early covid-19 pandemic in the u.s., Journal of Econometrics\/ 220, 23–62 | 0.874 | 6 | 2 | 100% |
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| 4 | Bai, Jushan (2009) Panel data models with interactive fixed effects, Econometrica\/ 77, 1229–1279 | 0.737 | 4 | 4 | 50% |
| 5 | Manski, Charles F., and Francesca Molinari (2021) Estimating the COVID-19 infection rate: Anatomy of an inference problem, Journal of Econometrics\/ 220, 181–192 | 0.737 | 3 | 3 | 67% |
| 6 | Cho, Sang-Wook (Stanley (2020) Quantifying the impact of nonpharmaceutical interventions during the COVID-19 outbreak: The case of sweden, The Econometrics Jou… | 0.737 | 3 | 2 | 100% |
| 7 | Fang, Hanming, Long Wang, and Yang Yang (2020) Human mobility restrictions and the spread of the novel coronavirus (2019-ncov) in china, Journal of Public Economics\/ 191, 104… | 0.737 | 3 | 2 | 100% |
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