Joint Hedonic Travel Cost Method: Combining Revealed and Stated Preference Data to Estimate Demand for Attribute Quality of Sport Fishing in Illinois
Araujo, Rogerio C. Pereira
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/82953
Description
Title
Joint Hedonic Travel Cost Method: Combining Revealed and Stated Preference Data to Estimate Demand for Attribute Quality of Sport Fishing in Illinois
Author(s)
Araujo, Rogerio C. Pereira
Issue Date
2002
Doctoral Committee Chair(s)
Braden, John B.
Department of Study
Agricultural Economics
Discipline
Agricultural Economics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Recreation
Language
eng
Abstract
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.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.