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Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission

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  • M. Keith Chen
  • Yilin Zhuo
  • Malena de la Fuente
  • Ryne Rohla
  • Elisa F. Long

Abstract

Accurately estimating the effectiveness of stay-at-home orders (SHOs) on reducing social contact and disease spread is crucial for mitigating pandemics. Leveraging individual-level location data for 10 million smartphones, we observe that by April 30th---when nine in ten Americans were under a SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this decline is causally attributable to SHOs, with wide demographic differences in compliance, most notably by political affiliation. Likely Trump voters reduce movement by 9% following a local SHO, compared to a 21% reduction among their Clinton-voting neighbors, who face similar exposure risks and identical government orders. Linking social distancing behavior with an epidemic model, we estimate that reductions in movement have causally reduced SARS-CoV-2 transmission rates by 49%.

Suggested Citation

  • M. Keith Chen & Yilin Zhuo & Malena de la Fuente & Ryne Rohla & Elisa F. Long, 2020. "Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission," Papers 2005.05469, arXiv.org.
    • Handle: RePEc:arx:papers:2005.05469
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    References listed on IDEAS

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    1. Fang, Hanming & Wang, Long & Yang, Yang, 2020. "Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China," Journal of Public Economics, Elsevier, vol. 191(C).
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    Cited by:

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    2. Auld, M. Christopher & Toxvaerd, Flavio, 2021. "The Great Covid-19 Vaccine Rollout: Behavioural And Policy Responses," National Institute Economic Review, National Institute of Economic and Social Research, vol. 257, pages 14-35, August.
    3. Hunt Allcott & Levi Boxell & Jacob C. Conway & Billy A. Ferguson & Matthew Gentzkow & Benjamin Goldman, 2020. "What Explains Temporal and Geographic Variation in the Early US Coronavirus Pandemic?," NBER Working Papers 27965, National Bureau of Economic Research, Inc.
    4. Desmet, Klaus & Wacziarg, Romain, 2022. "JUE Insight: Understanding spatial variation in COVID-19 across the United States," Journal of Urban Economics, Elsevier, vol. 127(C).
    5. Goolsbee, Austan & Syverson, Chad, 2021. "Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020," Journal of Public Economics, Elsevier, vol. 193(C).
    6. Sumedha Gupta & Kosali I. Simon & Coady Wing, 2020. "Mandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review," NBER Working Papers 28139, National Bureau of Economic Research, Inc.
    7. Anna Godøy & Maja Weemes Grøtting & Rannveig Kaldager Hart, 2022. "Reopening schools in a context of low COVID-19 contagion: consequences for teachers, students and their parents," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(3), pages 935-961, July.
    8. Qifeng Wan & Xuanhua Xu & Kyle Hunt & Jun Zhuang, 2022. "Stay Home or Not? Modeling Individuals’ Decisions During the COVID-19 Pandemic," Decision Analysis, INFORMS, vol. 19(4), pages 319-336, December.
    9. Fayaz Farkhad, Bita & Albarracín, Dolores, 2021. "Insights on the implications of COVID-19 mitigation measures for mental health," Economics & Human Biology, Elsevier, vol. 40(C).

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