Treatment for depression is complex, requiring decisions that may involve trade-offs between exploiting treatments with the highest expected value and experimenting with treatments with higher possible payoffs. Using patient claims data, we show that among skilled doctors, using a broader portfolio of drugs predicts better patient outcomes, except in cases where doctors' decisions violate loose professional guidelines. We introduce a behavioral model of decision making guided by our empirical observations. The model's novel feature is that the trade-off between exploitation and experimentation depends on the doctor's diagnostic skill. The model predicts that higher diagnostic skill leads to greater diversity in drug choice and better matching of drugs to patients even among doctors with the same initial beliefs regarding drug effectiveness. Consistent with the finding that guideline violations predict poorer patient outcomes, simulations of the model suggest that increasing the number of possible drug choices can lower performance.

Stratification is a distinctive feature of competitive education markets that can be explained by a preference for good peers. Learning externalities can lead students to care about the ability of their peers, resulting in across-school sorting by ability. This paper shows that a preference for good peers, and therefore stratification, can also emerge endogenously from reputational concerns that arise when graduates use their college of origin to signal their ability. Reputational concerns can also explain puzzling observed trends including the increase in student investment into admissions exam preparation, and the decline in study time at college.