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Cross-Context Benefit Transfer: A Bayesian Search for Information Pools Public Deposited

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https://ir.library.oregonstate.edu/concern/articles/7m01br30m

This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the author(s) and published by Oxford University Press on behalf of the Agricultural and Applied Economics Association. The published article can be found at:  http://ajae.oxfordjournals.org/.

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  • Commodity equivalence and population similarity are two widely accepted paradigms for the valid transfer of welfare estimates across resource valuation contexts. We argue that strict adherence to these rules may leave relevant information untapped. We propose a Bayesian model search algorithm that examines the probabilities with which two or more sub-sets of meta-data, each corresponding to a different combination of commodity and population, share common value distributions. Using as an example a large meta-data set of willingness-to-pay for diverse outdoor activities across different regions of the U.S., we find strong potential for contexts that would not traditionally be considered as transfer candidates to form information pools. Exploiting these commonalities leads to substantial efficiency gains for benefit estimates.
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  • Moeltner, K., & Rosenberger, R. S. (2014). Cross-context benefit transfer: a Bayesian search for information pools. American Journal of Agricultural Economics, 96(2), 469-488. doi:10.1093/ajae/aat115
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  • 96
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  • 2
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  • Data collection for this research was supported by funds from the U.S. EPA STAR Grant #RD-832-421-01 to Oregon State University.
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