Internal Consistency in Double Bounded Dichotomous Choice Contingent Valuation: Introducing Advanced Disclosure and Explicit Decision Rules in Sequential Learning Designs

One of the most commonly observed anomalies in Double Bounded Dichotomous Choice is that of internal inconsistency between the estimated values derived by single bounded and double bounded methods from the same data set. This paper introduces a simple bootstrap method to test the significance of such differences. We add to the idea that the willingness to pay (WTP) differences between Single Bounded Dichotomous Choice (SBDC) and Double Bounded Dichotomous Choice (DBDC) can be attenuated by Sequential Learning following Bateman et al. (2008). To this end, we study the effects in the field of introducing into the DBDC mechanism: i) Advance Disclosure (See Bateman et al. (2004)) and ii) Explicit DBDC Decision Rules as examined in an experimental context by Carson et al. (2009). Results show that introduction of these features into the design of CV surveys significantly reduces variance and anomalies presented in a standard DBDC treatment, thus generating internally consistent and more efficient willingness to pay estimates.