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Wireless alerts for extreme weather and the impact on hazard mitigating behavior

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  • Ferris, Jeffrey S.
  • Newburn, David A.

Abstract

Wireless alerts delivered through mobile phones are a recent innovation in regulatory efforts toward preparation for extreme weather events including flash floods. In this article, we use difference-in-differences models of the number of car accidents from days with government issued alerts for flash flood events in Virginia. We find that wireless alert messages for flash flood warnings reduced car accidents by 15.9% relative to the counterfactual with non-wireless alert protocols. We also use a regression discontinuity model to analyze hourly traffic volume data immediately before and after a flash flood warning message is issued. We find that traffic volume is reduced by 3.1% immediately following the issuance of a wireless alert relative to before the alert. These results imply that wireless alert messages effectively reduce exposure to hazards associated with extreme weather.

Suggested Citation

  • Ferris, Jeffrey S. & Newburn, David A., 2017. "Wireless alerts for extreme weather and the impact on hazard mitigating behavior," Journal of Environmental Economics and Management, Elsevier, vol. 82(C), pages 239-255.
    • Handle: RePEc:eee:jeeman:v:82:y:2017:i:c:p:239-255
    DOI: 10.1016/j.jeem.2016.11.002
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    Cited by:

    1. Aziz, Sonia & Boyle, Kevin & Akanda, Ali S. & Hanifi, M.A. & Pakhtigian, Emily L., 2022. "Early Warning Systems, Mobile Technology, and Cholera Aversion: Evidence from Rural Bangladesh," RFF Working Paper Series 22-24, Resources for the Future.
    2. Anand, Vaibhav, 2001. "The Value of Forecast Improvements: Evidence from Advisory Lead Times and Vehicle Crashes," SocArXiv hdpga, Center for Open Science.
    3. Stefan Borsky & Hannah Hennighausen, 2022. "Public Flood Risk Mitigation and the Homeowner’s Insurance Demand Response," Land Economics, University of Wisconsin Press, vol. 98(4), pages 537-559.
    4. Ashley R. Coles & Kyle E. Walker, 2021. "Assessing motorist behavior during flash floods in Tucson, Arizona," Transportation, Springer, vol. 48(6), pages 3037-3057, December.
    5. Anand, Vaibhav, 2022. "The Value of Forecast Improvements: Evidence from Advisory Lead Times and Vehicle Crashes," MPRA Paper 114491, University Library of Munich, Germany.
    6. Lusher, Lester & Ruberg, Tim, 2023. "Killer Alerts? Public Health Warnings and Heat Stroke in Japan," IZA Discussion Papers 16562, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Q54; Q58; D81; C23; C14; Keywords:; Wireless alerts; Extreme weather; Hazard mitigation;
    All these keywords.

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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