Consideration set models generalize discrete-choice models by relaxing the assumption that consumers consider all available options. Determining which options were considered has previously required either survey data or restrictions on how attributes affect consideration or utility. We provide an alternative route. In full-consideration models, choice probabilities satisfy a symmetry property analogous to Slutsky symmetry in continuous-choice models. This symmetry breaks down in consideration set models when changes in characteristics perturb consideration. We show that consideration probabilities are constructively identified from the resulting asymmetries. We validate our approach in a lab experiment where consideration sets are known and then apply our framework to study a “smart default” policy in Medicare Part D, wherein consumers are automatically reassigned to lower-cost prescription drug plans with the option of opting out. Full-consideration models imply that such a policy will be ineffective because consumers will opt out to avoid switching costs. Allowing for inattention, we find that defaulting all consumers to lower-cost options produces negligible welfare benefits on average, but defaulting only consumers who would save at least $300 produces large benefits.

We study the impact of changing choice set size on the quality of choices in health insurance markets. Using novel data on enrolment and medical claims for school district employees in the state of Oregon, we document that the average employee could save $600 by switching to a lower cost plan. Structural modelling reveals large “choice inconsistencies” such as non-equalization of the dollar spent on premiums and out of pocket, and a novel form of “approximate inertia” where enrolees are excessively likely to switch to other plans that are close to the current plan on the plan design spreadsheet. Variation in the number of plan choices across districts and over time shows that enrolees make lower-cost choices when the choice set is smaller. We show that a curated restriction of choice set size improves choices more than the best available information intervention, partly because approximate inertia lowers gains from new information. We explicitly test and reject the assumption that this is because individuals choose worse from larger choice sets, or “choice overload”. Rather, we show that this feature arises from the fact that larger choice sets feature worse choices on average that are not offset by individual re-optimization.

Competition in health insurance markets may fail to improve health outcomes if consumers are not able to identify high quality plans. We develop and apply a novel instrumental variables framework to quantify the variation in causal mortality effects across plans and how much consumers attend to this variation. We first document large differences in the observed mortality rates of Medicare Advantage plans within local markets. We then show that when plans with high (low) mortality rates exit these markets, enrollees tend to switch to more typical plans and subsequently experience lower (higher) mortality. We derive and validate a novel "fallback condition" governing the subsequent choices of those affected by plan exits. When the fallback condition is satisfied, plan terminations can be used to estimate the relationship between observed plan mortality rates and causal mortality effects. Applying the framework, we find that mortality rates unbiasedly predict causal mortality effects. We then extend our framework to study other predictors of plan mortality effects and estimate consumer willingness to pay. Higher spending plans tend to reduce enrollee mortality, but existing quality ratings are uncorrelated with plan mortality effects. Consumers place little weight on mortality effects when choosing plans. Good insurance plans dramatically reduce mortality, and redirecting consumers to such plans could improve beneficiary health.

We study choice over prescription insurance plans by the elderly using government administrative data to evaluate how these choices evolve over time. We find large "foregone savings" from not choosing the lowest cost plan that has grown over time. We develop a structural framework to decompose the changes in "foregone welfare" from inconsistent choices into choice set changes and choice function changes from a fixed choice set. We find that foregone welfare increases over time due primarily to changes in plan characteristics such as premiums and out-of-pocket costs; we estimate little learning at either the individual or cohort level.

We explore the in- and out- of sample robustness of tests for choice inconsistencies based on parameter restrictions in parametric models, focusing on tests proposed by Ketcham, Kuminoff and Powers (KKP). We argue that their non-parametric alternatives are inherently conservative with respect to detecting mistakes. We then show that our parametric model is robust to KKP's suggested specification checks, and that comprehensive goodness of fit measures perform better with our model than the expected utility model. Finally, we explore the robustness of our 2011 results to alternative normative assumptions highlighting the role of brand fixed effects and unobservable characteristics.

This paper investigates the degree to which choice inconsistencies documented in the context of Medicare Part D plan choice vary across consumers and geographic regions. Our main finding is that there is surprisingly little variation: regardless of age, gender, predicted drug expenditures or the predictability of drug demand consumers underweight out of pocket costs relative to premiums and fail to consider the individualized consequences of plan characteristics; as a result, they frequently choose plans which are dominated in the sense that an alternative plan provides better risk protection at a lower cost. We find limited evidence that the sickest individuals had more difficulty with plan choice, and we document that much of the variation in potential cost savings across states comes from variation in choice sets, not variation in consumers' ability to choose.

We evaluate the choices of elders across their insurance options under the Medicare Part D Prescription Drug plan, using a unique data set of prescription drug claims matched to information on the characteristics of choice sets. We document that elders place much more weight on plan premiums than on expected out of pocket costs; value plan financial characteristics beyond any impacts on their own financial expenses or risk; and place almost no value on variance reducing aspects of plans. Partial equilibrium welfare analysis implies that welfare would have been 27% higher if patients had all chosen rationally.

We state a sufficient condition under which choice data alone suffices to identify consumer preferences when choices are not fully informed. Suppose that: (i) the data generating process is a search model in which the attribute hidden to consumers is observed by the econometrician; (ii) if a consumer searches good j, she also searches goods which are better than j in terms of the non-hidden component of utility; and (iii) consumers choose the good that maximizes overall utility among searched goods. Canonical models will be biased: the value of the hidden attribute will be understated because consumers will be unresponsive to variation in the attribute for goods that they do not search. Under the conditions above and additional mild restrictions, an alternative method of recovering preferences using cross derivatives of choice probabilities succeeds regardless of the search protocol and is thus robust to whether consumers are informed. The approach nests several standard models, including full information. Our methods suggest natural tests for full information and can be used to forecast how consumers will respond to additional information. We verify in a lab experiment that our approach succeeds in recovering preferences when consumers engage in costly search.

A large body of research has investigated whether physicians overuse care. There is less evidence on whether, for a fixed level of spending, doctors allocate resources to patients with the highest expected returns. We assess both sources of inefficiency, exploiting variation in rates of negative imaging tests for pulmonary embolism. We document enormous across-doctor heterogeneity in testing conditional on patient population, which explains the negative relationship between physicians' testing rates and test yields. Furthermore, doctors do not target testing to the highest risk patients, reducing test yields by one-third. Our calibration suggests misallocation is more costly than overuse.