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

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, and 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 a large meta-data set of willingness-to-pay for diverse outdoor activities across various regions of the United States as an example, 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.