The Immediate Effect of COVID-19 Policies on Social-Distancing Behavior in the United States

  1. Rahi Abouk
  2. Babak Heydari

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Abstract

Although anecdotal evidence indicates the effectiveness of coronavirus disease 2019 (COVID-19) social-distancing policies, their effectiveness in relation to what is driven by public awareness and voluntary actions needs to be determined. We evaluated the effectiveness of the 6 most common social-distancing policies in the United States (statewide stay-at-home orders, limited stay-at-home orders, nonessential business closures, bans on large gatherings, school closure mandates, and limits on restaurants and bars) during the early stage of the pandemic.

Methods

We applied difference-in-differences and event-study methodologies to evaluate the effect of the 6 social-distancing policies on Google-released aggregated, anonymized daily location data on movement trends over time by state for all 50 states and the District of Columbia in 6 location categories: retail and recreation, grocery stores and pharmacies, parks, transit stations, workplaces, and residences. We compared the outcome of interest in states that adopted COVID-19–related policies with states that did not adopt such policies, before and after these policies took effect during February 15–April 25, 2020.

Results

Statewide stay-at-home orders had the strongest effect on reducing out-of-home mobility and increased the time people spent at home by an estimated 2.5 percentage points (15.2%) from before to after policies took effect. Limits on restaurants and bars ranked second and resulted in an increase in presence at home by an estimated 1.4 percentage points (8.5%). The other 4 policies did not significantly reduce mobility.

Conclusion

Statewide stay-at-home orders and limits on bars and restaurants were most closely linked to reduced mobility in the early stages of the COVID-19 pandemic, whereas the potential benefits of other such policies may have already been reaped from voluntary social distancing. Further research is needed to understand how the effect of social-distancing policies changes as voluntary social distancing wanes during later stages of a pandemic.

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  1. Version published to 10.1177/0033354920976575
  2. ScreenIT

    SciScore for 10.1101/2020.04.07.20057356: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data were gathered by Google from users who have enabled the Location History setting on their accounts and were the same data used by Google Maps to track human traffic at various restaurants and other locations.
    Google Maps
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has a number of limitations. First, the Google database is not based on the universe of all smartphone users and it only includes those individuals who have enabled the Location History setting on their account. However, given that around 90 percent of users keep their location services on (“Smartphone Users Keep Location Services Open”, 2020), our estimates should not be largely affected. Similarly, the data are imperfect since they don’t include people without smartphones and those who don’t carry their phones to certain places. However, this should not affect changes in recorded behavior and is expected to have little impact on our results. Finally, it is worth noting that measuring the effectiveness of social distancing policies based on confirmed cases hinges on how the tests are conducted in different states, requiring more than controlling for just the number of conducted tests that we used in this study. This problem can be in part mitigated by analyzing the policy effects on the number of COVID-19 deaths. Given that the median time from infection to death is reported to be close to 17 days, and since many states have issued their strongest policies in the last week of March, that study needs to wait until enough reliable data are collected.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.04.07.20057356v2 on medRxiv
  4. Version published to 10.1101/2020.04.07.20057356v1 on medRxiv