Monopolistic screening under learning by doing
研究在动态逆向选择框架下，代理人学习速率是私人信息时，委托人如何设计激励合同；发现学习效应被过度或不足利用取决于高效者还是低效者学习更快。
This article investigates the design of incentives in a dynamic adverse selection framework where agents' production technologies display learning effects and agents' learning rates are private knowledge. In a simple two-period model with full commitment available to the principal, we show that whether learning effects are over- or underexploited crucially depends on whether more efficient agents also learn faster (so costs diverge through learning effects) or whether it is the less efficient agents who learn faster (so costs converge). We further show that an overexploitation of learning effects can occur also if the full-commitment assumption is relaxed.