We study the estimation of risk aversion preferences with experimental data. The focus is on the trade-offs that arise when choosing between two different elicitation methods that have different degrees of difficulty for subjects. We analyze how and when the simpler, but coarser, elicitation method may be preferred to the more complex, but finer, one. Results indicate that the more complex measure has an overall superior predictive accuracy, but its downside is that subjects exhibit noisier

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... vior; conversely, the simpler measure has the opposite costs and benefits. Our main result is that subjects' numerical skills can help better assess this tradeoff: the simpler task may be preferred when subjects exhibit low numeracy as it generates less noisy behavior but similar predictive accuracy than the more complex task; conversely, for subjects with higher numerical skills, the greater predictive accuracy of the more complex task more than outweighs the larger noise. We explore timeconsistency and preference heterogeneity under the two methods and provide methodological suggestions for future work.