A Benefit Function Transfer obtains estimates of
Willingness-to-Pay (WTP) for the evaluation of a given policy at a site by
combining existing information from different study sites. This has the
advantage that more efficient estimates are obtained, but it relies on the
assumption that the heterogeneity between sites is appropriately captured in the
Benefit Transfer model. A more expensive alternative to estimate WTP is to
analyse only data from the policy site in question while ignoring information
from other sites. We make use of the fact that these two choices can be viewed
as a model selection problem and extend the set of models to allow for the
hypothesis that the benefit function is only applicable to a subset of sites. We
show how Bayesian Model Averaging (BMA) techniques can be used to optimally
combine information from all models.
The Bayesian algorithm searches for the
set of sites that can form the basis for estimating a Benefit function and
reveals whether such information can be transferred to new sites for which only
a small dataset is available. We illustrate the method with a sample of 42
forests from U.K. and Ireland. We find that BMA benefit function transfer
produces reliable estimates and can increase about 8 times the information
content of a small sample when the forest is 'poolable'.