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 debate an exciting new approach to non-market valuation was developed in the 1990s: a combination and joint estimation of revealed preference and stated preference data.
There are two primary objectives for Preference Data for Environmental Valuation. One objective is to fill a gap in the nonmarket valuation "primer" literature. A number of books have appeared over the past decade that develop the theory and methods of nonmarket valuation but each takes an individual nonmarket valuation method approach. Preference Data for Environmental Valuation considers each of these valuation methods in combination with another method. These relationships can be exploited econometrically to obtain more valid and reliable estimates of willingness-to-pay relative to the individual methods. The second objective is to showcase recent and novel applications 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. Preference Data for Environmental Valuation will be accessible to economists and consultants working in business or government, as well as an invaluable resource for researchers and students alike.
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. Joint estimation of stated and revealed welfare measures: The conceptual basis Kenneth E. McConnell
3. The basics of estimating preference functions for combining stated and revealed preference Timothy C. Haab
4. Dichotomous and frequency data joint estimation John C. Whitehead
5. Combining dichotomous choice willingness to pay response and recreation demand data Ju-Chin Huang
6. Multiple choice discrete data joint estimation John C. Whitehead
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, Bin Sun and John C. Whitehead
Part 3: Mixed Data
12. Joint estimation and consumers' responses to pesticide risk Young Sook Eom and V. Kerry Smith
13. Local impacts of tropical forest logging: Joint estimation of revealed and stated preference data from Ruteng, Indonesia David T. Butry and Subhrendu K. Pattanayak
14. Combining revealed preference and stated preference data without invoking the weak complementarity condition Kevin J. Egan
15. Joint estimation of revealed and stated preference trip and willingness-to-pay data to estimate the benefits and impacts of an Atlantic Intracoastal Waterway dredging and maintenance program Christopher F. Dumas, Jim Herstine and John C. Whitehead
Part 4: Discrete Data
16. 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
17. 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
18. Estimating the nonmarket value of green technologies using
Part 5: Benefit Transfer
19. 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
20. Benefits transfer of a third kind: An examination of structural benefits transfer George Van Houtven, Subhrendu Pattanayak, Sumeet Patil and Brooks Depro
21. Conclusions and future research John C. Whitehead, Timothy C. Haab, and Ju-Chin Huang
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John Whitehead is a Professor in the Department of Economics at Appalachian State University, USA. Tim Haab is a Professor of Environmental Economics at the Ohio State University, USA. Ju-Chin Huang is Associate Professor in the Department of Economics at the University of New Hampshire, USA.