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This paper develops a choice model for environmental public goods which allows for consumers to learn about their preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. Our main findings are that in a Random Utility Model (RUM) discrete choice model, a subject's scale should increase and the variability of scale should decrease with experience if subjects are Bayesians. We then estimate the model using field data regarding preferences for one particular public good, water quality. We find strong evidence that additional experience increases scale, thereby makes consumer preferences more predictable from the econometrician's perspective. We find supportive but less convincing evidence that experience decreases the variability of scale across subjects. . We thank the Northern Ireland Environment Agency for funding data collection for the water quality study. Dugald Tinch designed and managed the choice experiment survey used here as a source of data. We also thank MASTS (www.masts.ac.uk) for funding part of this work. Finally, we thank three referees for their very helpful comments on the original version of this paper.

This paper develops a reduced form method of controlling for differences in information sets of subjects in public good discrete choice models, using stated preference data. The main contribution of our method comes from accounting for the effect of information provided during a survey on the mean and the variance of individual-specific scale parameters. In this way we incorporate both scale heterogeneity as well as observed and unobserved preference heterogeneity to investigate differences across and within information treatments. Our approach will also be useful to researchers who want to combine stated preference data sets while controlling for scale differences. We illustrate our approach using the data from a discrete choice experiment study of a biodiversity conservation program and find that the mean of individual-specific scale parameters and its variance in the sample is sensitive to the information set provided to the respondents.