Cross‐Context Benefit Transfer: A Bayesian Search for Information Pools
提出贝叶斯模型搜索算法，识别不同商品和人群组合中共享价值分布的信息池，以提升资源估值中效益转移的效率，并以美国户外活动支付意愿数据验证。
Abstract 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.