This paper describes three approaches to estimating confidence intervals for willingness to pay
measures, the delta, Krinsky and Robb and bootstrap methods. The accuracy of the various
methods is compared using a number of simulated datasets. In the majority of the scenarios
considered all three methods are found to be reasonably accurate as well as yielding similar
results. The delta method is the most accurate when the data is well-conditioned, while the
bootstrap is more robust to noisy data and misspecification of the model. These conclusions are
illustrated by empirical data from a study of willingness to pay for a reduction in waiting time for a
general practitioner appointment in which all the methods produce fairly similar confidence
intervals
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