Eliciting risk preferences: When is simple better?

We study the estimation of risk preferences with experimental data and focus on the trade-offs when choosing between two different elicitation methods that have different degrees of difficulty for subjects. We analyze how and when a simpler, but coarser, elicitation method may be preferred to the more complex, but finer, one. Results indicate that the more complex measure has overall superior predictive accuracy, but its downside is that subjects exhibit noisier behavior. Our main result is that subjects' numerical skills can help better assess this tradeoff: the simpler task may be preferred for subjects who exhibit low numeracy, as it generates less noisy behavior but similar predictive accuracy. For subjects with higher numerical skills, the greater predictive accuracy of the more complex task more than outweighs the larger noise. We also explore preference heterogeneity and provide methodological suggestions for future work.