Bayesian Approaches to Modeling Stated Preference Data

Abstract

Bayesian econometric approaches to modeling non-market valuation data have not often been applied, but they offer a number of potential advantages. Bayesian models incorporate prior information often available in the form of past studies or pre-tests in Stated Preference (SP) based valuation studies; model computations are easily and efficiently performed within an intuitively constructed Markov chain Monte Carlo framework; and asymptotic approximations, unreasonable for the relatively small sample sizes seen in some SP data sets, need not be invoked to draw (posterior) inferences. With these issues in mind, we illustrate computationally feasible approaches for fitting a series of surveys in a sequential manner, and for comparing a variety of models within the Bayesian paradigm. We apply these approaches to a series of SP surveys that examined policies to conserve old growth forests, northern spotted owls, and salmon in the U.S. Pacific Northwest.