Amer. J. Agr. Econ. 84(1) (Febuary 2002): 161–170 Copyright 2002 American Agricultural Economics Association Choice Modeling and Tests of Benefit Transfer Mark Morrison, Jeff Bennett, Russell Blamey, and Jordan Louviere Benefit transfer is increasingly being used by decision makers as a way of estimating environmen- tal values suitable for use in benefit cost analysis. However, recent studies examining the validity of benefit transfer of passive use values estimated using contingent valuation have rejected the hypothesis of convergent validity. In this article, we demonstrate the usage of a form of conjoint analysis known as choice modeling for benefit transfer. Choice modeling has been touted as being particularlysuitableforbenefittransferbecauseitispossibletoallowfordifferencesinenvironmen- tal quality and socioeconomic characteristics when transferring benefit estimates. We demonstrate that choice modeling is suitable for benefit transfer, particularly when the transfers involve implicit prices. Second, we examine the circumstances in which benefit transfer of choice modeling derived value estimates is likely to be most valid. Two split sample tests were undertaken to achieve this objective. The evidence from these tests indicates that transfers across different case study sites are likely to be subject to less error than those across different populations. Key words: benefit transfer, choice modeling, passive use values, stated preference techniques. In many situations, because of time and bud- get constraints, those tasked with making decisions regarding the allocation of natural resources are required to extrapolate from existing data that were collected for a dif- ferent purpose. The use of existing studies in project evaluations and policy analyses is known in the resource economics literature as ‘benefit transfer.’ The use of existing data is not something new to economics, or indeed many other disciplines. The novelty of ‘benefit trans- fer’ is that data believed to be sensitive to changes in the context in which they were collected, and subject to various uncer- tainties, are used. For instance, differences Mark Morrison is lecturer in the School of Marketing and Man- agement, Charles Sturt University, Jeff Bennett is professor of Environmental Management, in the National Centre for Devel- opment Studies, Australian National University. Russell Blamey isavisitingfellowattheResearchSchoolofSocialSciences,Aus- tralian National University, Jordan Louviere is professor, Centre for Health Economics, Research and Evaluation, Sydney Uni- versity. An earlier version of this article was presented at the World Congress of Environmental and Resource Economists, Venice, Italy, June 25–27 1998. Funding for this research was provided by the Land and WaterResourcesResearchandDevelopmentCorporation,Envi- ronment Australia, the New South Wales National Parks and Wildlife Service, and the New South Wales Environment Protec- tion Authority. between the case study sites, or in the prefer- ences of respondents from different regions, could lead to errors when transferring esti- mates. It is therefore important to determine whether benefit transfer is statistically valid, what biases might be expected, their extent, and whether they can be corrected (Boyle and Bergstrom). Several studies have already been con- ducted to determine the convergent validity 1 of benefit transfer of passive use values. These studies have involved tests of the transferability of results across different sites, across different populations, and across time. Theearlyevidencefromthesestudieshasnot been supportive of the hypothesis of conver- gent validity for transfers across site or pop- ulations (Bergland, Magnussen, and Navrud; Kirchoff, Colby, and LaFrance). However, these studies have, to date, been undertaken solely using the contingent valu- ation method. A limitation of this technique in the context of benefit transfer is that it only values discrete changes in environmen- tal quality which may be different across sites. Bergland, Magnussen, and Navrud rec- ommend that that convergent validity should 1 Convergent validity occurs when two measures of the same construct are statistically equivalent.