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Understanding Error Structures and Exploiting Panel Data in Meta-analytic Benefit Transfers

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Abstract

A regression meta-analysis is a statistical summary of results from a set of empirical studies. While, a meta-analysis is typically used to drawn inferences regarding the collective insights from an empirical literature, a regression meta-analysis can also be used to predict outcomes as a substitute for the conduct of a new study. Within the nonmarket-valuation literature benefit transfers are a special case of prediction where value estimates collected for one purpose are used as a basis for predicting value for unstudied applications. Balancing against the prediction opportunities provided by a regression meta-analysis is the potential prediction error. This paper considers some of these issues in the estimation of a regression meta-analysis to support prediction of nonmarket values for applications where an original study does not exist. We do not purport to address all elements of the error structure and prediction issues, but to present a more coherent focus to enhance future research on the validity and reliability of benefit-function transfers, and ultimately assist in enhancing the credibility of benefit transfers to support policy analyses.

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Notes

  1. Everything said here applies to estimates of willingness to accept, but we use willingness to pay here as an example because of the focus on willingness to pay in empirical studies and challenges when estimating willingness to accept (Kim et al. 2015).

  2. We frame this definition in terms of willingness to pay and the discussion follows using willingness to pay, but the discussion is equally applicable to willingness to accept estimates of value, marginal prices from hedonic models, or any other welfare measure.

  3. For an original study, these would generally be the actual characteristics of individuals in the samples used to estimate models to predict \(y_{k}\), while in a meta-equation these would be summary statistics such as percent female, median income, etc. for people affected in region j.

  4. The details and Stata code for implementing the simulations are available on request from the authors.

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Authors and Affiliations

  1. Virginia Tech, Blacksburg, VA, USA

    Kevin J. Boyle

  2. Michigan State University, East Lansing, MI, USA

    Jeffrey M. Wooldridge

Authors
  1. Kevin J. Boyle
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  2. Jeffrey M. Wooldridge
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Correspondence to Kevin J. Boyle or Jeffrey M. Wooldridge.

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Guest Editor: V. Kerry Smith.

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Boyle, K.J., Wooldridge, J.M. Understanding Error Structures and Exploiting Panel Data in Meta-analytic Benefit Transfers. Environ Resource Econ 69, 609–635 (2018). https://doi.org/10.1007/s10640-017-0211-y

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