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. JEL Codes: D03, D83, O13, O14, O30, Q16. Downloaded from2. Many other dimensions might be important. For example, the strength of the tide, the time of day, the temperature, the tightness with which pods are attached, the strain of pods used, and so on could matter. In our analysis, we largely focus on two or three dimensions for parsimony, but actual demands on attention are much greater. LEARNING THROUGH NOTICING 1313at Harvard Library on August 24, 2015 http://qje.oxfordjournals.org/ Downloaded from 4. Note that l and t appear symmetrically in equation (1). For the purpose of forming beliefs about the underlying technology (y), it does not matter whether the farmer learns through many observations across plots at a fixed time, or through many observations over time on a fixed plot. Equation (1) also reflects a simplifying assumption that the payoff function is separable across input dimensions, allowing us to separately analyze the farmer's decisions across dimensions. LEARNING THROUGH NOTICING
We present a model of uninformative persuasion in which individuals "think coarsely": they group situations into categories, and apply the same model of inference to all situations within a category. Coarse thinking exhibits two features that persuaders take advantage of: (i) transference, whereby individuals transfer the informational content of a given message from situations in a category where it is useful to those where it is not, and (ii) framing, whereby objectively useless information influences individuals' choice of category. The model sheds light on uninformative advertising and product branding, as well as on some otherwise anomalous evidence on mutual fund advertising.
Research in behavioral public finance has blossomed in recent years, producing diverse empirical and theoretical insights. This article develops a single framework with which to understand these advances. Rather than drawing out the consequences of specific psychological assumptions, the framework takes a reducedform approach to behavioral modeling. It emphasizes the difference between decision and experienced utility that underlies most behavioral models. We use this framework to examine the behavioral implications for canonical public finance problems involving the provision of social insurance, commodity taxation, and correcting externalities. We show how deeper principles undergird much work in this area and that many insights are not specific to a single psychological assumption.
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.
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.
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