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Relating environmental attitudes and contingent values: how robust are methods for identifying preference heterogeneity?

Listed author(s):
  • Gwendolyn Aldrich
  • Kristine Grimsrud
  • Jennifer Thacher

    ()

  • Matthew Kotchen

We assess the importance and robustness of cluster analysis and latent class analysis as methods to account for unobserved heterogeneity. We provide a critique and comparison of both methods in the context of measuring environmental attitudes and a contingent valuation study involving endangered species. We find strong evidence of robustness for these methods: group characterization and assignment of individuals to groups are similar between methods, and willingness-to-pay estimates are consistent. In addition, there are significant differences in willingness-to-pay across environmental attitudinal groups, and we find that accounting for unobservable heterogeneity provides a significantly better fitting model. Copyright Springer Science+Business Media, Inc. 2007

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File URL: http://hdl.handle.net/10.1007/s10640-006-9054-7
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Article provided by Springer & European Association of Environmental and Resource Economists in its journal Environmental and Resource Economics.

Volume (Year): 37 (2007)
Issue (Month): 4 (August)
Pages: 757-775

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Handle: RePEc:kap:enreec:v:37:y:2007:i:4:p:757-775
DOI: 10.1007/s10640-006-9054-7
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  1. Nunes, Paulo A. L. D., 2002. "Using factor analysis to identify consumer preferences for the protection of a natural area in Portugal," European Journal of Operational Research, Elsevier, vol. 140(2), pages 499-516, July.
  2. Cooper, Philip & Poe, Gregory L. & Bateman, Ian J., 2004. "The structure of motivation for contingent values: a case study of lake water quality improvement," Ecological Economics, Elsevier, vol. 50(1-2), pages 69-82, September.
  3. Alain Le Kama & Katheline Schubert, 2004. "Growth, Environment and Uncertain Future Preferences," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 28(1), pages 31-53, May.
  4. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
  5. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
  6. Riccardo Scarpa & Mara Thiene, 2005. "Destination Choice Models for Rock Climbing in the Northeastern Alps: A Latent-Class Approach Based on Intensity of Preferences," Land Economics, University of Wisconsin Press, vol. 81(3).
  7. Peter Boxall & Wiktor Adamowicz, 2002. "Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(4), pages 421-446, December.
  8. Bart Hobijn & Philip Hans Franses, 2000. "Asymptotically perfect and relative convergence of productivity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 59-81.
  9. Kotchen, Matthew J. & Reiling, Stephen D., 2000. "Environmental attitudes, motivations, and contingent valuation of nonuse values: a case study involving endangered species," Ecological Economics, Elsevier, vol. 32(1), pages 93-107, January.
  10. Edward Morey & Jennifer Thacher & William Breffle, 2006. "Using Angler Characteristics and Attitudinal Data to Identify Environmental Preference Classes: A Latent-Class Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 34(1), pages 91-115, May.
  11. Nunes, Paulo A. L. D. & Schokkaert, Erik, 2003. "Identifying the warm glow effect in contingent valuation," Journal of Environmental Economics and Management, Elsevier, vol. 45(2), pages 231-245, March.
  12. Joost M.E. Pennings & Raymond M. Leuthold, 2000. "The Role of Farmers' Behavioral Attitudes and Heterogeneity in Futures Contracts Usage," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(4), pages 908-919.
  13. Gregory L. Poe & Kelly L. Giraud & John B. Loomis, 2005. "Computational Methods for Measuring the Difference of Empirical Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 353-365.
  14. Baker, Gregory A. & Burnham, Thomas A., 2001. "Consumer Response To Genetically Modified Foods: Market Segment Analysis And Implications For Producers And Policy Makers," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 26(02), December.
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