I study how an organization should manage a project of uncertain scope, when it is advised by a privately informed expert who prefers to prolong his employment. The optimal long-term contract combines a deadline for project completion and incentive payments which decline as the deadline approaches. When the firm can additionally learn about the project's state from output, the optimal deadline exhibits variable sensitivity to output, with a hard deadline at the outset of the project and increasingly soft deadlines as the project's performance declines.

We study how regulating data usage impacts innovation in digital markets. Platforms commonly use proprietary data about third-party sellers to inform their own competing offerings, dampening incentives for innovation. We model this interaction and characterize how data usage restrictions reshape these incentives. An outright ban on data usage may boost or curtail innovation, depending on the thickness of the right tail of demand for new products. More flexible rules controlling when and what data are made available can always improve the effectiveness of regulation. Our results contribute to an ongoing policy discussion regarding competition in digital markets.

We study how an organization should dynamically screen an agent of uncertain loyalty whom it suspects of committing damaging acts of undermining. The organization controls the stakes of the relationship, while the agent strategically times undermining, which can occur repeatedly and is detected only stochastically. The optimal commitment stakes policy exhibits both discreteness and gradualism, with distinct “untrusted” and “trusted” phases featuring gradually rising stakes during the untrusted phase and a discrete gap in stakes between phases. This policy is also the equilibrium outcome when the organization cannot commit, and the agent’s equilibrium undermining policy exhibits variable, non-monotonic intensity.