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