Dynamic Learning and Context-Dependence in Sequential, Attribute-Based, Stated-Preference Valuation Questions

A hybrid stated-preference model is presented that combines the referendum contingent valuation response format with an experimentally designed set of attributes. A sequence of valuation questions is asked to a random sample in a mailout mail-back format. Econometric analysis shows greater discrimination between alternatives in the final choice in the sequence, and the vector of preference parameters shifts. Lead and lag choice sets have a structural influence on current choices and unobserved factors induce positive correlation across the responses. These results indicate that people learn about their preferences for attributebased environmental goods by comparing attribute levels across choice sets.