A fundamental implication of standard moral hazard models is overuse of low-value medical care because copays are lower than costs. In these models, the demand curve alone can be used to make welfare statements, a fact relied on by much empirical work. There is ample evidence, though, that people misuse care for a different reason: mistakes, or "behavioral hazard." Much high-value care is underused even when patient costs are low, and some useless care is bought even when patients face the full cost. In the presence of behavioral hazard, welfare calculations using only the demand curve can be off by orders of magnitude or even be the wrong sign. We derive optimal copay formulas that incorporate both moral and behavioral hazard, providing a theoretical foundation for value-based insurance design and a way to interpret behavioral "nudges." Once behavioral hazard is taken into account, health insurance can do more than just provide financial protection - it can also improve health care efficiency.

Self-control problems change the logic of agency theory by partly aligning the interests of the firm and worker: both now value contracts that elicit future effort. Findings from a year-long field experiment with full-time data entry workers support this idea. First, workers increase output by voluntarily choosing dominated contracts (which penalize low output but give no additional rewards for high output). Second, effort increases closer to (randomly assigned) paydays. Third, the contract and payday effects are strongly correlated within workers, and this correlation grows with experience. We suggest that workplace features such as high-powered incentives or effort monitoring may provide self-control benefits.

Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these “prediction policy problems” requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of “machine learning” are particularly useful for addressing these prediction problems. We use an example from health policy to illustrate the large potential social welfare gains from improved prediction.