The Geneva Risk and Insurance Review最新文献

There is widespread concern in developing countries with the expansion of formal insurance products to help manage significant risks. These concerns arise primarily from a lack of understanding of insurance products, general failures of financial literacy and the need to use relatively exotic products in order to keep costs down for poor households. We investigate the importance of incentivized measures for general understanding, as well as domain-specific knowledge of the decision context on the purchase and the quality of index insurance decisions. We evaluate the quality of financial decisions by comparing the individual expected welfare outcomes of a number of decisions each individual makes to purchase index insurance or not. We find that excess purchase is an important driver of welfare losses, and that our incentivized measure of domain-specific literacy plays a critical role in bringing about better quality index insurance decisions.

Supplementary information: The online version contains supplementary material available at 10.1057/s10713-020-00060-1.

We conducted in-the-field choice experiments in China to investigate farmers' willingness to pay for crop insurance and to determine how objective and subjective beliefs affect Willingness to Pay (WTP). We deploy three variants of the choice experiment using a priming mechanism on objective and subjective beliefs plus a control. We find that the cuing frame matters in that there are differences in WTP within five attributes and across variants. In terms of practical policy, our results suggest that farmers' frame of reference toward objective and subjective risks can affect insurance demand.

Promoting prevention is an important goal of public policy. Fifty years ago, Ehrlich and Becker (J Polit Econ 80:623-648, 1972) proposed a simple model of prevention (or self-protection as they called it). Surprisingly enough, subsequent research, mainly within the expected utility paradigm, showed that it is hard to derive clear predictions within this simple model that can help to guide policy. This is what I refer to as the prevention puzzle: why is it so hard for economic theory to guide prevention decisions? In this article I try to shed light on this question. I review the existing literature and add some tentative new results under nonexpected utility. While the impact of risk aversion on prevention is complex, three factors seem to contribute unambiguously to underprevention: prudence, likelihood insensitivity, and loss aversion. I conclude by giving some ideas how empirical research may contribute to the understanding of prevention decisions and help to solve the prevention puzzle.

Since the mid-1980s, the share of household net worth intermediated by US financial institutions has shifted from defined benefit plans to life insurers and defined contribution plans. Life insurers have primarily grown through variable annuities, which are mutual funds with longevity insurance, a potential tax advantage, and minimum return guarantees. The minimum return guarantees change the primary function of life insurers from traditional insurance to financial engineering. Variable annuity insurers are exposed to interest and equity risk mismatch and their stock returns were especially low during the COVID-19 crisis. We consider regulatory changes, such as more detailed financial disclosure and standardized stress tests, to monitor potential risk mismatch and to ensure stability of the insurance sector.

Governments sometimes encourage or impose individual self-protection measures, such as wearing a protective mask in public during an epidemic. However, by reducing the risk of being infected by others, more self-protection may lead each individual to go outside the house more often. In the absence of lockdown, this creates a "collective offsetting effect", since more people outside means that the risk of infection is increased for all. However, wearing masks also creates a positive externality on others, by reducing the risk of infecting them. We show how to integrate these different effects in a simple model, and we discuss when self-protection efforts should be encouraged (or deterred) by a social planner.

Integrated epidemiological-economics models have recently appeared to study optimal government policy, especially stay-at-home orders (mass "quarantines"). But these models are challenging to interpret due to the lack of closed-form solutions. This note provides an intuitive and graphical explanation of optimal quarantine policy. To be optimal, a quarantine requires "the cavalry" (e.g., mass testing, strong therapeutics, or a vaccine) to arrive just in time, not too early or too late. The graphical explanation accommodates numerous extensions, including hospital constraints, sick worker, age differentiation, and learning. The effect of uncertainty about the arrival time of "the cavalry" is also discussed.

This paper analyzes the insurability of pandemic risk and outlines how underwriting policies and scenario analysis are used to build resilience upfront and plan contingency actions for crisis scenarios. It then summarizes the unique "lessons learned" from the Covid-19 crisis by baselining actual developments against a reasonable, pre-Covid-19 pandemic scenario based on the 2002 SARS epidemic and 1918 Spanish influenza pandemic. Actual developments support the pre-Covid-19 hypothesis that financial market developments dominate claims losses due to the demographics of pandemics and other factors. However, Covid-19 "surprised" relative to the pre-Covid-19 scenario in terms of its impact on the real economy as well as on the property and casualty segment as business interruption property triggers and exclusions are challenged, something that may adversely impact the insurability of pandemics as well as the perception of the industry for some time to come. The unique lessons of Covid-19 reinforce the need for resilience upfront in solvency and liquidity, the need to improve business interruption wordings and re-underwrite the book, and the recognition that business interruption caused by pandemics may not be an insurable risk due to its large accumulation potential and the threat of external moral hazard. These insurability limitations lead to a discussion about the structure and financing of protection against the impact of future pandemics.

Private insurance coverage for economic losses caused by pandemics is limited. While many factors contribute to reduced demand and supply, we attribute the low amount of coverage to the high levels of capital that would be required to credibly insure pandemic economic losses with cross-sectional pooling mechanisms. Pooling over time significantly reduces the required capital and therefore the cost of insurance, but as a practical matter likely requires a government with the ability to borrow and tax. We also argue that insurance for economic losses due to pandemics likely generates positive externalities for the macroeconomy. We therefore analyze the general tradeoffs associated with different ways that a government can promote such insurance.