Bayesian Estimation of Dichotomous Choice Contingent Valuation with Follow-Up

Abstract

Dichotomous choice contingent valuation involves a binary yes/no question that can be followed by a subsequent question in order to obtain more information from the respondent, and leading to more efficient welfare estimates. Estimation methods for these data have mainly focused on classical maximum likelihood. In this paper we study possible improvements utilising a Bayesian MCMC approach to model this type of data. The classical and Bayesian approaches are compared with a Monte Carlo simulation experiment. The results show that the Bayesian approach improves the performance of the model, particularly with relatively small samples.

We are grateful to two anonymous referees and the editors of this book for helpful comments. This work has been done during the stay of the first author in the Department of Agricultural and Resource Economics (ARE) of the University of California, Berkeley.