American Economic Review 111, no.1 (January 2021): 276-323.
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model persuaders face a tradeoff: better-fitting models induce less movement in receivers’ beliefs. Consequently, a receiver exposed to the true model can be most misled by persuasion when that model fits poorly, competition between persuaders tends to neutralize the data by pushing towards better-fitting models, and a persuader facing multiple receivers is more effective when he can send tailored, private messages.
Quarterly Journal of Economics 129, no.3 (August 2014): 1311-1315.
We consider a model of technological learning under which people "learn through noticing": they choose which input dimensions to attend to and subsequently learn about from available data. Using this model, we show how people with a great deal of experience may persistently be off the production frontier because they fail to notice important features of the data they possess. We also develop predictions on when these learning failures are likely to occur, as well as on the types of interventions that can help people learn. We test the model's predictions in a field experiment with seaweed farmers. The survey data reveal that these farmers do not attend to pod size, a particular input dimension. Experimental trials suggest that farmers are particularly far from optimizing this dimension. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, behavioral changes occur only after the farmers are presented with summaries that highlight previously unattended-to relationships in the data.
Quarterly Journal of Economics 130, no.4 (November 2015): 1623-1667.
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.
Journal of the European Economic Association 12, no. 6 (December 2014): 1423-1452)
What do we notice and how does this affect what we learn and come to believe? I present a model of an agent who learns to make forecasts on the basis of readily available information, but is selective as to which information he attends to: he chooses whether to attend as a function of current beliefs about whether such information is predictive. If the agent does not attend to some piece of information, it cannot be recalled at a later date. He uses Bayes’ rule to update his beliefs given attended-to information, but does not attempt to fill in missing information. The model demonstrates how selective attention may lead the agent to persistently fail to recognize important empirical regularities, make systematically biased forecasts, and hold incorrect beliefs about the statistical relationship between variables. In addition, it identifies factors that make such errors more likely or persistent. The model is applied to shed light on stereotyping and discrimination, persistent learning failures and disagreement, and the process of discovery.
