Research

Published Papers

Paying for Expertise: The Effect of Experience on Reinsurance Demand with J. Tyler Leverty and Kenny Wunder, Journal of Risk and Insurance, Vol 88, Issue 3: 727-756, 2021.

Optimizing Forecast-based Actions for Extreme Rainfall Events with Jonathan Lala (lead author), Juan Bazo, and Paul Block. Climate Risk Management, Vol 34 (2021) 100374

Working Papers

The Value of Forecast Improvements: Evidence from Advisory Lead Times and Vehicle Crashes (2022)

Upcoming presentations: TBA

Past presentations: Colorado University Environmental & Resource Economics Workshop 2022, Vail, CO; Western Economic Association (WEAI) Annual Meeting 2022, Portland, OR (Virtual); American Risk and Insurance Association (ARIA) Annual Meeting 2022, Long Beach, CA.

Abstract: Scientific and technological advances are resulting in improved forecasts of risk, but do better forecasts result in better risk management? I investigate to what extent the improvements in lead time of winter weather advisories affect the frequency of motor vehicle crashes. I construct a data set of winter weather advisories, weather monitor readings, and vehicle crashes at the county-date level in 11 states in the US during 2006-2018. Using within county variation in lead time, I show that receiving winter advisories earlier reduces crash risk significantly. I also examine two potential mechanisms that might lead to these effects. First, using the mobile phone location data from SafeGraph, I show that longer lead times result in fewer visits by people to places outside their homes. Second, using snow plow truck location data, I show that road crews perform a greater level of winter maintenance activities when advisories arrive with longer lead time. Overall, this study provides evidence that improvements in forecast lead times result in meaningful economic benefits to society, and these benefits come from both the individual and institutional response to longer lead times.

Forecast-based Financing for Risk Mitigation (2022)

Abstract: Advances in forecasting technologies provide opportunities to develop forecast-contingent mechanisms to finance risk-mitigating early actions. In this paper, I examine one such mechanism-the Forecast Based Financing (FbF) program that provides trigger-based financing to take loss-reducing early actions before a natural disaster occurs. I build a model to incorporate the existing structure of the FbF mechanism and provide comparative statics results for the effects of exogenous economic factors on the optimal financing amount and forecast trigger. My analyses provide three key insights. First, when forecast skill is low, more risk aversion leads to higher financing and lower forecast trigger levels. However, as forecast skill improves, the effect of risk aversion on the optimal financing becomes small and can even flip direction. Second, when forecast skill, likelihood of risky event, or the marginal benefit of actions increases, or the absolute benefit of actions decreases, it is optimal to choose higher levels of financing as well as higher levels of trigger, and vice versa. Third, using numerical analysis, I show that the benefits of a trigger-based FbF plan may be comparable to those of a fully-contingent scenario.

Research in Progress

Weather Forecasts and Local Economy (with Honglin Li)

Upcoming presentation: Eastern Economic Association (EEA) Annual Meetings, February 2023, New York, NY; Midwest Economic Association (MEA) Annual Meeting, March-April 2023, Cleveland, Ohio