Joint hedonic travel cost method: Combining revealed and stated preference data to estimate demand for attribute quality of sport fishing in Illinois.

摘要

This study evaluates the performance of the hedonic travel cost method by applying recent methodological advances in recreation valuation: conjoint analysis and combined revealed and stated preference data. The combination of these techniques creates a joint hedonic travel cost method that consists of using better quality data to overcome the problem of multicollinearity and appearance of negative implicit prices. The hedonic travel cost models are specified by combining hedonic price functional forms (linear, semilog) and data types (revealed, stated, pooled data). Prior to the estimation of the joint models, the revealed and stated preference models are statistically tested to verify similarities in the underlying preference structure. The selection of the best hedonic travel cost models are based on the overall performance of the hedonic price and inverse demand models regarding the goodness-of-fit and model parameters. The results showed that the semilog hedonic price models coupled with the pooled data provided the best basis for estimation of inverse demands. Even though the joint hedonic travel cost method had reduced the collinearity between attributes and produced some gains in efficiency, it did not improve considerably the significance of the implicit prices and estimation of theoretically consistent inverse demands.