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Testing for Hypothetical Bias in Contingent Valuation Using a Latent Choice Multinomial Logit Model


  • Steven B. Caudill
  • Peter A. Groothuis
  • John C. Whitehead


The most persistently troubling empirical result in the contingent valuation method literature is the tendency for hypothetical willingness to pay to overestimate real willingness to pay. We suggest a new approach to test and correct for hypothetical bias using a latent choice multinomial logit (LCMNL) model. To develop this model, we extend Dempster, Laird, and Rubin’s (1977) work on the EM algorithm to the estimation of a multinomial logit model with missing information on categorical membership. Using data on both the quality of water in the Catawba River in North Carolina and the preservation of Saginaw wetlands in Michigan, we find two types of “yes” responders in both data sets. We suggest that one set of yes responses are yea-sayers who suffer from hypothetical bias and answer yes to the hypothetical question but would not pay the bid amount if it were real. The second group does not suffer from hypothetical bias and would pay the bid amount if it were real.

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  • Steven B. Caudill & Peter A. Groothuis & John C. Whitehead, 2006. "Testing for Hypothetical Bias in Contingent Valuation Using a Latent Choice Multinomial Logit Model," Working Papers 06-09, Department of Economics, Appalachian State University.
  • Handle: RePEc:apl:wpaper:06-09

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    References listed on IDEAS

    1. Christian A. Vossler & Robert G. Ethier & Gregory L. Poe & Michael P. Welsh, 2003. "Payment Certainty in Discrete Choice Contingent Valuation Responses: Results from a Field Validity Test," Southern Economic Journal, Southern Economic Association, vol. 69(4), pages 886-902, April.
    2. Greene, William H. & Hensher, David A., 2003. "A latent class model for discrete choice analysis: contrasts with mixed logit," Transportation Research Part B: Methodological, Elsevier, vol. 37(8), pages 681-698, September.
    3. Steven B. Caudill & Peter A. Groothuis, 2005. "Modeling Hidden Alternatives in Random Utility Models: An Application to "Don’t Know" Responses in Contingent Valuation," Land Economics, University of Wisconsin Press, vol. 81(3).
    4. Gregory Poe & Jeremy Clark & Daniel Rondeau & William Schulze, 2002. "Provision Point Mechanisms and Field Validity Tests of Contingent Valuation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(1), pages 105-131, September.
    5. John C. Whitehead & Peter A. Groothuis & Rob Southwick & Pat Foster-Turley, 2006. "Economic Values of Saginaw Bay Coastal Marshes," Working Papers 06-10, Department of Economics, Appalachian State University.
    6. Brown, Thomas C. & Ajzen, Icek & Hrubes, Daniel, 2003. "Further tests of entreaties to avoid hypothetical bias in referendum contingent valuation," Journal of Environmental Economics and Management, Elsevier, vol. 46(2), pages 353-361, September.
    7. David Aadland & Arthur J. Caplan, 2003. "Willingness to Pay for Curbside Recycling with Detection and Mitigation of Hypothetical Bias," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 492-502.
    8. Karen Blumenschein & GlennC. Blomquist & Magnus Johannesson & Nancy Horn & Patricia Freeman, 2008. "Eliciting Willingness to Pay Without Bias: Evidence from a Field Experiment," Economic Journal, Royal Economic Society, vol. 118(525), pages 114-137, January.
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    C25; P230; Q51;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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