Demand for insurance can be driven by high risk aversion or high risk.We show how to separately identify risk preferences and risk types using only choices from menus of insurance plans.Our revealed preference approach does not rely on rational expectations, nor does it require access to claims data.We show what can be learned non-parametrically from variation in insurance plans, offered separately to random cross-sections or offered as part of the same menu to one cross-section.We prove that our approach allows for full identification in the textbook model with binary risks and extend our results to continuous risks.We illustrate our approach using the Massachusetts Health Insurance Exchange, where choices provide informative bounds on the type distributions, especially for risks, but do not allow us to reject homogeneity in preferences.
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