Key methodologies used for managing weather risks have relied on the assumption that climate is not changing and that the historic weather record is therefore representative of current risks. Anthropogenic climate change upends this assumption, effectively reducing the information available to actors and increasing ambiguity in the estimated climate distribution, with associated costs for weather risk management and risk-averse decision-makers. These costs result purely from the knowledge that the climate could be changing, may arise abruptly, are additional to any direct costs or benefits from actual climate change, and are, to date, entirely unquantified. Using a case study of extreme rainfall-related flood damages in New York City, this paper illustrates how these ambiguity-related costs arise. Greater uncertainty over the current climate distribution interacts with a steeply non-linear damage function to greatly increase the mean and variance of the posterior loss distribution. This is a systemic information shock that cannot be diversified within the insurance sector, producing higher and more volatile premiums and higher reinsurance costs. These effects are consistent with recent developments in US property insurance markets, where premium increases, bankruptcies, and insurer withdrawals have been linked to the growing costs of natural disasters.
Authors
- Acknowledgements & Disclosure
- No relevant sources of funding or financial relationships to disclose. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.
- DOI
- https://doi.org/10.3386/w32684
- Pages
- 29
- Published in
- United States of America
Table of Contents
- NBER WORKING PAPER SERIES 1
- LEARNING CATASTROPHIC RISK AND AMBIGUITY IN THE CLIMATE CHANGE ERA 1
- Frances C. Moore 1
- Working Paper 32684 httpwww.nber.orgpapersw32684 1
- NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge MA 02138 July 2024 1
- Learning Catastrophic Risk and Ambiguity in the Climate Change Era Frances C. Moore NBER Working Paper No. 32684 July 2024 JEL No. G52Q54 2
- Frances C. Moore Department of Environmental Science and Policy 1023 Wickson Hall University of California Davis Davis One Shields Avenue Davis CA 95616 fmooreucdavis.edu 2
- A Code Repository is available at httpsgithub.comfmoore125LearningCatRisk.git 2
- 1 Introduction 3
- 2 Background 5
- 3 Case Study Illustration 7
- 4 Results 12
- Posterior Scale Parameter 14
- Probability Density 14
- Climatology 14
- Learning Model 14
- Damage Function 14
- Annual Max Daily Rainfall inches 14
- Probability Density 14
- Learning Model 14
- Assumed Stationarity Potential NonStationarity 20
- 5 Discussion and Conclusions 21
- A Appendix 24
- Acknowledgements 25
- References 25
