Incentivizing catastrophe risk sharing

Abstract

Government plays a vital role in improving community resilience against natural disasters. Due to the limited relief capacity of a government, it is desirable to develop a risk-sharing mechanism involving both private sector providers (e.g., insurers, for-profit disaster agencies, and firms that provide resources for risk mitigation and recovery) and the public. In this article, we take catastrophe insurance as an example to examine ways of providing incentives for multilateral risk sharing, especially when it involves socially connected communities. We consider a sequential game with three sets of players, the government, a private insurer, and a community of households. The government determines an optimal subsidy portfolio (including ex ante insurance premium subsidy and ex post relief subsidy) for a community with particular levels of social network influence and risk perception. We characterize the equilibrium purchase rate within the community by positive and negative herding behaviors and identify the government’s optimal subsidy strategy dependent on the available budget and the emphasis on ex post social responsibility. We also extend the game to account for multi-community coverage and multi-year insurance contracts to demonstrate the benefits of spatial and inter-temporal risk pooling.

Keywords:

• Risk sharing
• catastrophe insurance
• game theory
• social network equilibrium

Additional information

Funding

This research was partially supported by the National Natural Science Foundation of China (NSFC) [Grants 71401088, 71871128 and 71361130017] and Tsinghua University Initiative Scientific Research Program [Grant 20151080401].

Notes on contributors

Shenming Song

Shenming Song is currently pursuing a PhD degree in the Department of Industrial Engineering at Tsinghua University. Her research focuses on decision analysis, social network analysis and data-driven modeling of human behavior.

Chen Wang

Chen Wang is an associate professor in the Department of Industrial Engineering at Tsinghua University. She obtained her doctoral degree from the Department of Industrial and Systems Engineering at the University of Wisconsin–Madison. Her research interests include decision analysis, risk analysis, and data-driven modeling.