Risk preferences and risk perceptions in insurance experiments: some methodological challenges

The ability to run experiments, or to see natural data as a quasi-experiment, does not free one from the need for theory when evaluating insurance behavior. Theory can be used to motivate the experimental design, evaluate latent effects from the experiment, or test hypotheses about latent effects or about observable effects that could be confounded by latent effects. The risk, evident in the broader behavioral literature in general, is the attention given to “behavioral story-telling” in lieu of rigorous scholarship. Such story-telling certainly has a role in fueling speculation about possible casual forces at work generating the data we see, but should not be mistaken for the final word. There is also a severe cost in terms of the heroic assumptions needed for identification. Again, such identifying assumptions can have a valuable role, but many general claims rely critically on those assumptions. Controlled laboratory experiments and Bayesian econometric methods should play a complementary role to field experiments and quasi-experiments. One clear lesson from the evaluation of methodological challenges is to use theory more, to explore the ability of “standard economics” to explain behavior. The time has long passed where straw men theories are set up to fail when confronted with behavior. Just as we want to consider flexible parametric functional forms when appropriate, we should be open to conventional economics applied more flexibly.