1. Introduction Markets do exist to provide the information necessary for conducting benefit-cost analyses in many public policy decision-making situations. When desired estimates of benefits or costs manifest in the market (Arrow 1999, p. vi), economists have increasingly turned to contingent valuation surveys to elicit the values that individuals would place on public goods and externalities (Mitchell and Carson 1989; Cropper and Oates 1992; Deacon et al. 1998). Although discrete choice, take-it-or-leave-it methods of eliciting preferences have gained favor on theoretical grounds (Arrow et al. 1993; Carson, Groves, and Machina 1999) and realism (Hanemann 1994), accumulated evidence from a number of laboratory and field contingent valuation validity studies suggests that these methods overstate actual willingness to pay (WTP) for private and public goods (e.g., Cummings, Harrison, and Rustrom 1995; Brown et al. 1996; Cummings et al. 1997; Balistreri et al. 2001; Champ and Bishop 2001). That is, respondents are more likely to say to commitments than actual commitments, reflecting hypothetical bias and the need for contingent valuation responses (Harrison 2002). Two recent papers offer possible methods for calibrating discrete-choice responses by considering payment certainty levels reported by respondents. In what we term the follow-up certainty question (FCQ) method, Champ et al. (1997) ask dichotomous respondents to indicate how certain they are, on a scale from 1 (very uncertain) to (very certain), that they would pay the stated dollar amount if the program were actually offered. Separate WTP functions are estimated for each certainty level. Welsh and Poe (1998) instead adopt a discrete choice (MBDC) approach that directly incorporates certainty levels through a two-dimensional decision matrix: One dimension specifies dollar amounts that individuals would be required to pay on implementation of the policy, and the second dimension allows individuals to express their level of voting certainty through definitely no, probably no, not sure, probably yes, and definitely yes response options. A multiple-bounded logit model is used to estimate separate WTP functions for each certainty level. In this paper, we use a field validity test of contributions to a green electricity pricing program to further explore these methods and address several validity issues. First, using actual sign-up data as a criterion, we derive optimal correction strategies for the two methods. Previous laboratory research on private goods suggests that dichotomous responses from those who are definitely sure (Blumenschein et al. 1998) or at least probably sure (Johannesson et al. 1999) closely predict actual purchase decisions. Johannesson, Liljas, and Johansson (1998) find that respondents who are absolutely sure of their decision provide a conservative estimate of real purchases. These laboratory results are replicated in public goods contingent valuation field validity research using FCQ methods, suggesting that models that only use responses with certainty values on a 1-to-10 scale of 7 and higher (Ethier et al. 2000), 8 and higher (Champ and Bishop 2001), or 10 (Champ et al. 1997) best predict actual contributions. We are the first to provide correction strategies for the MDC approach. Second, we examine if the experimental classroom results reported in Welsh and Poe can be replicated in the field. In that paper, the authors compare estimated logistic response distributions from dichotomous questions and MBDC not sure responses and find that they are statistically different. This suggests that respondents who are uncertain of their values will tend to yea-say when asked a single dichotomous question, a result that has been replicated elsewhere (e. …
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Jan 1, 2006
David Ungemah + 2
Open Access