Risk management in the use of published statistical results for policy decisions

Statistical inferential results generally come with a measure of reliability for decision-making purposes. For a policy implementer, the value of implementing published policy research depends critically upon this reliability. For a policy researcher, the value of policy implementation may depend weakly or not at all upon the policy's outcome. Some researchers might benefit from overstating the reliability of statistical results. Implementers may find it difficult or impossible to determine whether researchers are overstating reliability. This information asymmetry between researchers and implementers can lead to an adverse selection problem where, at best, the full benefits of a policy are not realized or, at worst, a policy is deemed too risky to implement at any scale. Researchers can remedy this by guaranteeing the policy outcome. Researchers can overcome their own risk aversion and wealth constraints by exchanging risks with other researchers or offering only partial insurance. The problem and remedy are illustrated using a confidence interval for the success probability of a binomial policy outcome.