Preference Data for Environmental Valuation

Description

The monetary valuation of environmental goods and services has evolved from a fringe field of study in the late 1970s and early 1980s to a primary focus of environmental economists over the past decade. Despite its rapid growth, practitioners of valuation techniques often find themselves defending their practices to both users of the results of applied studies and, perhaps more troubling, to other practitioners.

One of the more heated threads of this internal debate over valuation techniques revolves around the types of data to use in performing a valuation study. In the infant years of the development of valuation techniques, two schools of thought emerged: the revealed preference school and the stated preference school, the latter of which is perhaps most associated with the contingent valuation method.

In the midst of this heated debate an exciting new approach to non-market valuation was developed in the 1990s: a combination and joint estimation of revealed preference (RP) and stated preference (SP) data.

This book provides a systematic, cohesive and in-depth discussion of the theory and methods of joint estimation, as well as showcasing recent developments in theory and methods of data combination and joint estimation via a set of original, state-of-the-art studies that are contributed by leading researchers in the field.

Contents

Foreword Trudy Cameron Preface Timothy C. Haab, Ju-Chin Huang and John C. Whitehead 1. Introduction John C. Whitehead, Timothy C. Haab and Ju-Chin Huang Part 1: Theory and Methods 2. Theory Ted McConnell 3. The basics of estimating preference functions for combining stated and revealed preferences Timothy C. Haab 4. Participation and frequency data joint estimation John C. Whitehead 5. Multiple choice discrete data joint estimation John C. Whitehead 6. Combining dichotomous choice willingness-to-pay response and recreation demand data Ju-Chin Huang Part 2: Frequency Data 7. Econometric models for joint estimation of revealed and stated preference site-frequency recreation demand models Craig E. Landry and Haiyong Liu 8. Combining multiple revealed and stated preference data sources: A recreation demand application Daniel J. Phaneuf and Dietrich Earnhart 9. Combined conjoint-travel cost demand model for measuring the impact of erosion and erosion control programs on beach recreation Ju-Chin Huang, George R. Parsons, P. Joan Poor and Min Qiang Zhao 10. Using revealed and stated preference methods to value large ship artificial reefs: The Key West Vandenberg sinking O. Ashton Morgan and William L. Huth 11. Combining revealed and stated preferences to hypothetical bias in repeated question surveys: A feedback model of seafood demand Timothy C. Haab, John C. Whitehead and Bin Sun Part 3:Discrete Data 12. Gauging the value of short-term site closures in a travel-cost random utility model of recreation demand with a little help from stated preference data George R. Parsons and Stela Stefanova 13. Modeling behavioral response to changes in reservoir operations in the Tennessee Valley region Paul M. Jakus, John C. Bergstrom, Marty Phillips and Kelly O’Brien 14. Estimating the nonmarket value of green technologies using partial data enrichment techniques Brett R. Gelso 15. Joint estimation of recreation site selection and dichotomous choice willingness-to-pay in a random utility model framework John C. Whitehead Part 4: Mixed Data 16. Joint estimation and consumer responses to pesticide risk Young Sook Eom and V. Kerry Smith 17. Local impacts of tropical forest logging: Joint estimation of revealed and stated preference data from Ruteng, Indonesia David T. Butry and Subhrendu K. Pattanayak 18. Combining revealed preference and stated preference data without invoking the weak complementarity condition Kevin J. Egan 19. Joint estimation of stated preference trip and willingness-to-pay data to estimate the benefits and impacts of an Atlantic Intracoastal Waterway dredging and maintenance program John C. Whitehead, Christopher F. Dumas and Jim Herstine Part 5: Joint Estimation and Benefit Transfer 20. Are benefit transfers using a joint revealed and stated preference model more accurate than revealed and stated preference data alone? Juan Marcos Gonzalez-Sepulveda and John B. Loomis 21. Benefits transfer of a third kind: An examination of structural benefits transfer George Van Houtven, Subhrendu Pattanayak, Sumeet Patil and Brooks Depro 22. Conclusions Timothy C. Haab, Ju-Chin Huang and John C. Whitehead

Related Subjects

1. Environmental Economics
2. Environmental Studies
3. Environmental Politics