Bias and efficiency of single vs. double bound models for contingent valuation studies: a Monte Carlo Analysis

The Dichotomous Choice Contingent Valuation Method (DC-CVM), both in the single and the double bound formulation, has been in the last years the most popular technique among practitioners of contingent valuation, due to its simplicity of use in data collection. The single bound procedure is easier to implement than the double bound, especially in data collection and estimation. On the other hand, it is well known that the double bound is more efficient than the single bound estimator. It remains to analyze the bias of the ML estimates produced by either model, and the gain in efficiency associated to the double bound model, in different experimental settings. We find that there are no relevant differences in point estimates given by the two models, even for small sample size, so that neither estimator can be said to be less biased than the other. The greater efficiency of the double bound is confirmed, although it can be seen that the differences tend to reduce by increasing the sample size, and are often negligible for medium size samples. Provided that a reliable pre-test is conducted, and the sample size is large, our results warrant the use of the single rather than the double bound model.