Bias and efficiency of single versus double bound models for contingent valuation studies: a Monte Carlo analysis

Abstract

The dichotomous choice contingent valuation method can be used either in the single or double bound formulation. The former is easier to implement, while the latter is known to be more efficient. We analyse 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. The greater efficiency of the double bound is confirmed, although differences tend to reduce by increasing the sample size. Provided that a reliable pretest is conducted, and the sample size is large, use of the single rather than the double bound model is warranted.