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Fatal Errors: The Mortality Value of Accurate Weather Forecasts

Author

Listed:
  • Jeffrey G. Shrader
  • Laura Bakkensen
  • Derek Lemoine

Abstract

We provide the first revealed preference estimates of the benefits of routine weather forecasts. The benefits come from how people use advance information to reduce mor tality from heat and cold. Theoretically, more accurate forecasts reduce mortality if and only if mortality risk is convex in forecast errors. We test for such convexity using data on the universe of mortality events and weather forecasts for a twelve-year period in the U.S. Results show that erroneously mild forecasts increase mortality whereas erro neously extreme forecasts do not reduce mortality. Making forecasts 50% more accurate would save 2,200 lives per year. The public would be willing to pay $112 billion to make forecasts 50% more accurate over the remainder of the century, of which $22 billion reflects how forecasts facilitate adaptation to climate change.

Suggested Citation

  • Jeffrey G. Shrader & Laura Bakkensen & Derek Lemoine, 2023. "Fatal Errors: The Mortality Value of Accurate Weather Forecasts," Working Papers 23-30, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:23-30
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    File URL: https://www2.census.gov/library/working-papers/2023/adrm/ces/CES-WP-23-30.pdf
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    References listed on IDEAS

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    1. Garth Heutel & Nolan H. Miller & David Molitor, 2021. "Adaptation and the Mortality Effects of Temperature across U.S. Climate Regions," The Review of Economics and Statistics, MIT Press, vol. 103(4), pages 740-753, October.
    2. Olivier Deschênes & Enrico Moretti, 2009. "Extreme Weather Events, Mortality, and Migration," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 659-681, November.
    3. Matthew Gibson & Jeffrey Shrader, 2018. "Time Use and Labor Productivity: The Returns to Sleep," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 783-798, December.
    4. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    5. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.
    6. Alan Barreca & Karen Clay & Olivier Deschenes & Michael Greenstone & Joseph S. Shapiro, 2016. "Adapting to Climate Change: The Remarkable Decline in the US Temperature-Mortality Relationship over the Twentieth Century," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 105-159.
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    Cited by:

    1. Linsenmeier, Manuel & Shrader, Jeffrey G., 2023. "Global inequalities in weather forecasts," SocArXiv 7e2jf, Center for Open Science.
    2. Lusher, Lester & Ruberg, Tim, 2023. "Killer Alerts? Public Health Warnings and Heat Stroke in Japan," IZA Discussion Papers 16562, Institute of Labor Economics (IZA).
    3. Molina, Renato & Rudik, Ivan, 2022. "The Social Value of Predicting Hurricanes," SocArXiv sqtjr, Center for Open Science.

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    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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