A False Sense of Security

Andrew Martinez (Doctoral Student, Research Assistant, Department of Economics, University of Oxford Institute for New Economic Thinking at the Martin School, University of Oxford) 

Abstract:

I analyze damages from hurricane strikes on the United States since 1955. Using machine learning methods to select the most important drivers for damages, I show that large errors in a hurricane’s predicted landfall location result in higher damages. This relationship holds across a wide range of model specifications and when controlling for ex-ante uncertainty and potential endogeneity. Using a counterfactual exercise I find that the cumulative reduction in damages from forecast improvements since 1970 is about $82 billion, which exceeds the U.S. government’s spending on the forecasts and private willingness to pay for them. 

Keywords: Adaptation, Model Selection, Natural Disasters, Uncertainty JEL classifications: C51, C52, Q51, Q54