Monitoring with Rich Data

We offer a new perspective on the issue that commonly used contracts tend to be simple, even though standard models predict more complicated optimal contracts. We consider moral hazard problems where a principal has access to rich monitoring data about an agent's action. Rather than focusing on optimal contracts, we measure the performance of contracts by analyzing the rate at which the principal's payoffs converge to the first-best as the amount of data grows large. Our main result shows that the optimal convergence rate to the first-best is achieved by binary wage schemes, suggesting a novel rationale for this simple and widely observed class of contracts. Notably, in order to attain the optimal convergence rate, the principal must set a lenient cutoff for when the agent receives a high vs. low wage. In contrast, we find that other common contracts where wages vary more finely with observed data (e.g., linear contracts) approximate the first-best at a highly suboptimal rate. Finally, we show that the optimal convergence rate depends only on a simple summary statistic of the monitoring technology. This yields a detail-free ranking over monitoring technologies that quantifies their value for incentive provision in data-rich settings and applies regardless of the agent's specific utility or cost functions.