Preference data for environmental valuation : combining revealed and stated approaches
Author(s)
Bibliographic Information
Preference data for environmental valuation : combining revealed and stated approaches
（Routledge explorations in environmental economics, 31）
Routledge, 2011
- : hbk
Available at 5 libraries
-
National Graduate Institute for Policy Studies Library (GRIPS Library)
: hbk519||H29||3101260678
Note
Includes bibliographical references and index
Description and Table of Contents
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 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 this book. 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. This book 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. This book will be accessible to economists and consultants working in business or government, as well as an invaluable resource for researchers and students alike.
Table of 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. 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 partial data enrichment techniques Brett R. Gelso 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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