On the effect of the prior of Bayes estimators of the willingness to pay for electric-vehicle driving range

We use Bayes’ estimator of a consumer-surplus probit model to study the relevance of the prior in a discrete choice model. We take random subsamples of varying sizes of stated preference data regarding ultra-low emission vehicle purchases in California and focus on the willingness-to-pay for improvements in driving range. Prior information is obtained from a meta-analysis of consumer valuation of driving range. We find the posterior distribution of the willingness-to-pay using a tight and a weakly informative prior, and also analyze the nonparametric estimates of the posterior compare these with the likelihood function of the problem. It is found that the weight of the prior is relevant for very small samples, but for standard sample sizes the prior vanishes. Thus, the Bayes estimator of a static discrete choice model is in general equivalent to the maximum likelihood estimator, although for some intermediate sample sizes the prior provides more realistic values.

► Willingness-to-pay for greater electric vehicle driving range is quantified.
► Using Bayes estimator of a consumer-surplus probit model the problem of defining WTP priors is solved.
► Using priors from an electric vehicle meta-analysis the effect of sample size is examined.