The results from a comprehensive sample of published and unpublished studies of the preference reversal phenomenon are pooled and analysed. The principal objective is to estimate structural models in order to investigate the differences in behaviour between choice and valuation. In particular, we aim to estimate the attitude to risk implied by the pooled data on choice and valuations separately.
An outline of the approach is as follows. It is widely accepted that the vast majority of subjects are risk averse, and this risk aversion is revealed clearly when the subject is asked to choose between two lotteries. In contrast, when the certainty equivalent of a single lottery is elicited, subjects have a tendency to report a valuation that is close to the expected value of the lottery; in other words, they tend towards risk-neutrality in valuation tasks. It is our view that since choice tasks are more natural than valuation tasks, the former are more likely to reveal true preferences. The tendency towards risk neutrality observed in valuation tasks is in contrast interpreted as a deviation from true preferences resulting from the abnormal nature of the task. The meta analysis pools all the available choice data in order to estimate the degree of risk aversion of a representative experimental subject. It also pools the available valuation data in order to estimate the degree of risk aversion implied by these tasks. The latter estimate is, as expected, much lower than the “true” risk aversion inferred from the choice data, and this difference is, in itself, a powerful explanation of the preference reversal phenomenon.
However, what is most interesting in the present context is the manner in which the method of eliciting certainty-equivalents affects the implied degree of risk aversion. More precisely, are some methods of valuation elicitation better at extracting “true” valuations than others? This question is addressed by allowing the risk aversion estimate to depend on the elicitation method in the econometric model. We find that increasing the number of tasks brings valuations closer to choices, as does the use of the Random Lottery Incentive scheme, but that the use of the Becker-DeGroot-Marschak elicitation scheme increases the discrepancy between valuations and choices.
Using the estimates from the econometric model, an algorithm is developed which can be used to predict the outcome of a preference reversal experiment.
Presented by:
Peter Moffatt (University of East Anglia) Co-author: Nicholas Bardsley
Date & time:
January 12, 2009 4:00 pm - January 12, 2009 12:00 am
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