Economics
Valuing non-market valuation studies using meta-analysis: A demonstration using estimates of willingness-to-pay for water quality improvements
Document Type
Article
Abstract
A common refrain in the environmental economics literature is the need for additional estimates of environmental and natural resource values. Yet economists have paid little formal attention to the value of information (VOI) provided by non-market valuation studies, and whether this value justifies the cost. We develop a novel VOI model to quantify the benefit of non-market valuation studies conducted to expand the body of value estimates in the literature, and hence available for use in policy analyses. The approach is designed to capitalize on information available through preexisting valuation meta-data, and can be applied to any valuation meta-regression that is suitable to inform policy decisions. We illustrate the approach for a prototype nationwide water-quality improvement policy in the United States, assuming that policy benefits are evaluated using benefit transfer based on a meta-regression model of willingness-to-pay. Our results suggest that, when evaluating nationwide water quality regulations with uncertain net benefits, the VOI from a typical water quality valuation study will almost certainly exceed the cost. We also examine how VOI varies with study design features and information gaps in the literature, thereby illustrating how the approach can inform research priorities.
Publication Title
Journal of Environmental Economics and Management
Publication Date
11-2020
Volume
104
ISSN
0095-0696
DOI
10.1016/j.jeem.2020.102379
Keywords
benefit transfer, meta-analysis, non-market valuation, value of information, water quality, willingness-to-pay
Repository Citation
Newbold, Stephen C. and Johnston, Robert J., "Valuing non-market valuation studies using meta-analysis: A demonstration using estimates of willingness-to-pay for water quality improvements" (2020). Economics. 146.
https://commons.clarku.edu/faculty_economics/146

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