ADVERSE SELECTION AND MORAL HAZARD IN INSURANCE: CAN DYNAMIC DATA HELP TO DISTINGUISH?

• 成果类型: Article
• 署名作者: Abbring, Jaap H.; Heckman, James J.; Chiappori, Pierre-Andre; Pinquet, Jean
• 署名单位: Free University of Berlin; University of Chicago; Universite Paris Nanterre
• 刊物名称: JOURNAL OF THE EUROPEAN ECONOMIC ASSOCIATION
• ISSN/ISSBN: 1542-4766
• DOI: 10.1162/154247603322391152
• 发表日期: 2003
• 页码: 512-521
• 关键词: models

摘要

A standard problem of applied contracts theory is to empirically distinguish between adverse selection and moral hazard. We show that dynamic insurance data allow to distinguish moral hazard from dynamic selection on unobservables. In the presence of moral hazard, experience rating implies negative occurrence dependence: individual claim intensities decrease with the number of past claims. We discuss econometric tests for the various types of data that are typically available. Finally, we argue that dynamic data also allow to test for adverse selection, even if it is based on asymmetric learning. (JEL: D82, G22, C41, C14)