In this article, we investigate the effect of several commonly used experimental designs on willingness-to-pay in a Monte Carlo environment where true utility parameters are known. All experimental designs considered in this study generated unbiased valuation estimates. However, random designs or designs that explicitly incorporated attribute interactions generated more precise valuation estimates than main effects only designs. A key result of our analysis is that a large sample size can substitute for a poor experimental design. Overall, our results indicate that certain steps can be taken to achieve a manageably sized experimental design without sacrificing the credibility of welfare estimates. Copyright 2005 American Agricultural Economics Association.
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