Due Diligence in Meta-analyses to Support Benefit Transfers

Meta-analyses are becoming a popular tool for supporting benefit transfers, but the availability of studies is a direct consequence of policy issues, research funding, and investigator interest. We investigate fragility versus robustness of the meta-equation by considering sample selection, removing one observation or study at a time with replacement, and removing/adding regressors. Several key variables are found to be robust, strengthening the argument for their use in policy prescriptions. The key insights are that these methods can be used to parse meta-data to identify the most appropriate set(s) of observations and regressors to support literature evaluations, benefit transfers and other practical applications using statical summaries of empirical data.

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