IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/26992.html
   My bibliography  Save this paper

Did California’s Shelter-in-Place Order Work? Early Coronavirus-Related Public Health Effects

Author

Listed:
  • Andrew I. Friedson
  • Drew McNichols
  • Joseph J. Sabia
  • Dhaval Dave

Abstract

On March 19, 2020, California Governor Gavin Newsom issued Executive Order N-33-20 2020, which required all residents of the state of California to shelter in place for all but essential activities such as grocery shopping, retrieving prescriptions from a pharmacy, or caring for relatives. This shelter-in-place order (SIPO), the first such statewide order issued in the United States, was designed to reduce COVID-19 cases and mortality. While the White House Task Force on the Coronavirus has credited the State of California for taking early action to prevent a statewide COVID-19 outbreak, no study has examined its impact. This study is the first to estimate the effect of SIPO adoption on health. Using daily state-level coronavirus data and a synthetic control research design, we find that California’s statewide SIPO reduced COVID-19 cases by 125.5 to 219.7 per 100,000 population by April 20, one month following the order. We further find that California’s SIPO led to as many as 1,661 fewer COVID-19 deaths during this period. Back-of-the-envelope calculations suggest that there were about 400 job losses per life saved during this short-run post-treatment period.

Suggested Citation

  • Andrew I. Friedson & Drew McNichols & Joseph J. Sabia & Dhaval Dave, 2020. "Did California’s Shelter-in-Place Order Work? Early Coronavirus-Related Public Health Effects," NBER Working Papers 26992, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26992
    Note: AG CH DEV EH LE LS PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w26992.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    2. Benjamin Hansen & Keaton Miller & Caroline Weber, 2020. "Early Evidence On Recreational Marijuana Legalization And Traffic Fatalities," Economic Inquiry, Western Economic Association International, vol. 58(2), pages 547-568, April.
    3. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    4. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. James G. MacKinnon, 2019. "How cluster‐robust inference is changing applied econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 52(3), pages 851-881, August.
    2. Sviták, Jan & Tichem, Jan & Haasbeek, Stefan, 2021. "Price effects of search advertising restrictions," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    3. Sean Lyons, 2017. "Are Employer Mandates to Offer Health Insurance Effective in Reducing Subsidized Coverage Crowd-Out of Employer-Sponsored Insurance?," American Journal of Health Economics, MIT Press, vol. 3(3), pages 370-391, Summer.
    4. Taylor K. Odle & Jennifer A. Delaney, 2022. "You are Admitted! Early Evidence on Enrollment from Idaho’s Direct Admissions System," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(6), pages 899-932, September.
    5. Christian Kümpel, 2019. "Do financial incentives influence the hospitalization rate of nursing home residents? Evidence from Germany," Health Economics, John Wiley & Sons, Ltd., vol. 28(11), pages 1235-1247, November.
    6. James G. MacKinnon & Matthew D. Webb, 2017. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 233-254, March.
    7. Anderson, D. Mark & Rees, Daniel I., 2015. "Per se drugged driving laws and traffic fatalities," International Review of Law and Economics, Elsevier, vol. 42(C), pages 122-134.
    8. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    9. Andrew C. Johnston & Alexandre Mas, 2018. "Potential Unemployment Insurance Duration and Labor Supply: The Individual and Market-Level Response to a Benefit Cut," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2480-2522.
    10. Maximilian von Ehrlich & Doina Radulescu, 2017. "The taxation of bonuses and its effect on executive compensation and risk‐taking: Evidence from the UK experience," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 26(3), pages 712-731, September.
    11. Gregory DeAngelo & Benjamin Hansen, 2014. "Life and Death in the Fast Lane: Police Enforcement and Traffic Fatalities," American Economic Journal: Economic Policy, American Economic Association, vol. 6(2), pages 231-257, May.
    12. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    13. Bruno Ferman & Cristine Pinto, 2019. "Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 452-467, July.
    14. Anukriti, S & Chakravarty, Abhishek, 2015. "Political Aspirations in India: Evidence from Fertility Limits on Local Leaders," IZA Discussion Papers 9023, Institute of Labor Economics (IZA).
    15. Saffer, Henry & Gehrsitz, Markus, 2016. "The effect of post-and-hold laws on alcohol consumption," International Review of Law and Economics, Elsevier, vol. 45(C), pages 63-71.
    16. Ham, Andrés & Michelson, Hope C., 2018. "Does the form of delivering incentives in conditional cash transfers matter over a decade later?," Journal of Development Economics, Elsevier, vol. 134(C), pages 96-108.
    17. Thomas Buchmueller & Sarah Miller & Marko Vujicic, 2016. "How Do Providers Respond to Changes in Public Health Insurance Coverage? Evidence from Adult Medicaid Dental Benefits," American Economic Journal: Economic Policy, American Economic Association, vol. 8(4), pages 70-102, November.
    18. Jeffrey P. Clemens & Parker Rogers, 2020. "Demand Shocks, Procurement Policies, and the Nature of Medical Innovation: Evidence from Wartime Prosthetic Device Patents," CESifo Working Paper Series 8781, CESifo.
    19. Sebastian Galiani & Stephen Knack & Lixin Colin Xu & Ben Zou, 2017. "The effect of aid on growth: evidence from a Quasi-experiment," Journal of Economic Growth, Springer, vol. 22(1), pages 1-33, March.
    20. Joseph J. Sabia & Richard V. Burkhauser & Benjamin Hansen, 2012. "Are the Effects of Minimum Wage Increases Always Small? New Evidence from a Case Study of New York State," ILR Review, Cornell University, ILR School, vol. 65(2), pages 350-376, April.

    More about this item

    JEL classification:

    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:26992. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.