Predicting Resource Policy Outcomes via Meta-Regression: Data Space, Model Space, and the Quest for ‘Optimal Scope’

Resource-managing agencies are increasingly relying on secondary data to predict economic benefits for planned policy interventions. This ‘transfer of benefits’ is often based on the quantitative synthesis of aggregate results for similar past interventions via Meta-Regression Models. However, this approach is generally plagued by the paucity of available studies and related small sample problems. A broadening of scope of the Meta-Regression Model by adding data from “related, yet different” contexts or activities may circumvent these issues, but may not necessarily enhance the efficiency of transfer functions if the different contexts do not share policy-relevant parameters. We illustrate how different combinations of contexts can be interpreted as ‘data spaces’ which can then be explored for the most promising transfer function using Bayesian Model Search techniques. Our results indicate that model-averaged benefit predictions for scope-augmented data spaces can be more efficient than those flowing from the baseline context and data.