Experts in experiments: How selection matters for estimated distributions of risk preferences

An ever increasing number of experiments attempts to elicit risk preferences of a population of interest with the aim of calibrating parameters used in economic models. We are concerned with two types of selection effects, which may affect the external validity of standard experiments: Sampling from a narrowly defined population of students ("experimenter-induced selection") and self-selection due to non-response or incomplete response of participants in a random sample from a broad population. We find that both types of selection lead to a sample of experts: Participants perform significantly better than the general population, in the sense of fewer violations of revealed preference conditions. Self-selection within a broad population does not seem to matter for average preferences. In contrast, sampling from a student population leads to lower estimates of average risk aversion and loss aversion parameters. Furthermore, it dramatically reduces the amount of heterogeneity in all parameters.