Testing in Models of Asymmetric Information
研究在非对称信息模型中，测试潜在特征如何克服逆向选择问题并实现完全信息竞争均衡，扩展了Mirrlees关于无限罚款最优使用的结论，并指出测试无法完全解决信息问题时的困难来源。
This paper explores the role of testing in models of asymmetric information. We demonstrate conditions under which testing for underlying characteristics can overcome adverse selection problems and lead to a full-information competitive equilibrium. This paper provides a more general statement of Mirrlees result on the optimal use of infinite fines. Where testing cannot fully resolve the problems associated with asymmetric information, we outline the source of the difficulties. Our results, developed in the context of a labour market, can be directly extended to other environments. In problems with asymmetric information, testing to discover an agent's chosen action or underlying characteristics may significantly reduce the cost of moral hazard and adverse selection.