Data Management

Experts In Experiments: How Selection Matters For Estimated Distributions Of Risk Preferences

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Executive Summary

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. The authors 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 of participants into the experiment. They 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.

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