I investigate the impact of assessment design on student decision making in the context of a second year economics course. I develop a dynamic structural model of student test taking where students are uncertain about their ability, and have biased beliefs about the returns to studying. In the model, students exert costly effort to earn ordinal test grades centered around a target average. Between tests, students update their beliefs about their ability between tests. Using novel high frequency data collected from a competitive large course setting, I estimate the true returns to studying, students' beliefs about returns to studying, and students' capacity to update under a Biased Bayesian framework. Then I combine the three components to simulate counterfactuals using the dynamic model. I find that returns to study effort are relatively concave, that students overestimate their returns to studying, and that students vastly under-react to test grades when updating. Counterfactuals suggest that biased model perceptions contribute to large increases in study effort, implying that the phenomenon exerts positive influence regarding knowledge acquisition. In addition, assessment design counterfactuals suggest that more frequent, and more objective tests are of benefit to students, as they contribute to a swifter reduction in students' biased beliefs about their own skills.

We study purchase deferral and pricing in digital goods markets using data from PC video game sales on Valve's Steam platform. We document that demand systematically declines prior to predictable sales, consistent with forward-looking consumer behavior. To rationalize this phenomenon, we develop and estimate a structural model of demand with forward-looking consumers and differentiated products, allowing substitution across both time and product. The model shows that firms can use temporary discounts to implement intertemporal price discrimination, segmenting consumers by patience. Quantitatively, we find that approximately 40–50% of consumers are forward-looking, and that discounting increases both consumer surplus and profits for most games. Counterfactuals demonstrate that discounting can outperform optimal uniform pricing. Market structure also plays a key role: competition reduces profits, while centralized pricing increases surplus extraction by internalizing substitution. These results provide an  explanation for high–low pricing in digital goods markets without stockpiling. 

Misspecification is theoretically linked to failures in belief updating, but empirical evidenceremains scarce. Using a field experiment in a large university course, we show that misspecified beliefs are a major barrier to accurate updating. Students remain overconfident despite receiving informative test scores, and they substantially overestimate the noisiness in test scores. A randomized controlled trial providing impersonal information about test score noisiness significantly improves students’ predictions, closing up to onethird of the gap with a Bayesian benchmark. These results show that misspecification is an important constraint on belief updating but can be mitigated through information interventions.