October 1, 2019, 15:30–16:50
Room MS 001
Econometrics and Empirical Economics Seminar
Consideration set models generalize discrete choice models by relaxing the assumption that consumers are aware of all available options. Thus far, identification arguments for these models have relied either on auxiliary data on what options were considered or on instruments excluded from consideration or utility. In a general discrete choice framework, we show that consideration probabilities can be identified without these data intensive methods using insights from behavioral decision theory. 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, and we show that consideration probabilities are constructively identified from the resulting asymmetries. In a lab experiment, we recover preferences and consideration probabilities using only data on which items were ultimately chosen, and we apply the model to study a \smart default" policy in Medicare Part D.