Testing for Asymmetric Information in Insurance Markets: A Multivariate Ordered Regression Approach
提出一种基于多元有序Logit模型的扩展正相关检验方法，用于检测保险市场中的非对称信息，并以美国Medigap健康保险市场为例，发现风险与保险覆盖的关联因覆盖类型和风险类别而异，且受个人社会经济特征和风险偏好的影响。
Abstract The positive correlation (PC) test is the standard procedure used in the empirical literature to detect the existence of asymmetric information in insurance markets. This article describes a new tool to implement an extension of the PC test based on a new family of regression models, the multivariate ordered logit, designed to study how the joint distribution of two or more ordered response variables depends on exogenous covariates. We present an application of our proposed extension of the PC test to the Medigap health insurance market in the United States. Results reveal that the risk–coverage association is not homogeneous across coverage and risk categories, and depends on individual socioeconomic and risk preference characteristics.