A Bayesian approach to double bounded contingent valuation

Abstract

In this paper we propose a Bayesian approach to model double bounded contingent valuation data. The double bounded elicitation method is interpreted as a two tier iterated process in which the subject is allowed to have a second thought about his/her valuation for the environmental good. Prior information is modelled from the answers to the first dichotomous choice question. The model is Quasi-Bayesian (Q-B) in that the prior distribution refers to mean willingness to pay while the likelihood function refers to the proportions of a multinomial distribution. This model is applied to empirical data from a contingent valuation survey involving the valuation expressed by European tourists for access to natural areas in the Canary Islands. Results show that point estimate of consumer surplus computed with the Q-B model does not differ substantially from single bounded model estimation. In addition, double bounded seems to be quite robust to the choice of the prior model of willingness to pay responses. Comparison with open ended suggests that the Q-B model might be useful to control for strategic response and starting point biases.