The Social Value of Predicting Hurricanes

Renato Molina and Ivan Rudik
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Ivan Rudik: Cornell University

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Abstract: Hurricanes are among the costliest natural disasters in the world, with a significant portion of their impact linked to whether forecasts can accurately predict hurricanes’ intensity and path. In this paper, we estimate the economic impacts of the official hurricane forecasts in the US and the value of improving them. We reconstruct county-level forecasts of storm track, wind speed, and precipitation for all major hurricanes in the US from 2005 to 2021, and we link them with data on hurricane damages and federal emergency expenditures to either protect or recover from hurricanes. We find that protective expenditures exponentially increase with the forecast wind speed and with the degree of uncertainty about the forecast. Correspondingly, we find that forecast errors are costly: underestimating wind speed can result in damages up to an order of magnitude larger than if the forecast had been accurate. Finally, we estimate the marginal social value of improving forecasts and find that for the average county, a reduction in forecast uncertainty by one standard deviation would reduce total protective expenditures and subsequent damages by over half a million dollars. This value is larger for higher-intensity storms or when conditions make forecasts more uncertain. Our results suggest that forecast improvements since 2009 have generated benefits that are orders of magnitude greater than the cumulative budget for operating and improving the hurricane forecast system.

Date: 2022-07-29
New Economics Papers: this item is included in nep-env and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:sqtjr

DOI: 10.31219/osf.io/sqtjr

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