Data from: Locally-weighted meta-regression and benefit transfer

These files contain the data and data dictionary used to produce the results presented in: Moeltner, K., Puri, R., Johnston, R. J., Besedin, E., Balukas, J. A., & Le, A. (2023). Locally-weighted meta-regression and benefit transfer. Journal of Environmental Economics and Management, 121, 102871. 

Meta-regression models (MRMs) are commonly used within benefit transfer to estimate broadly applicable, “umbrella” benefit functions that may be used to predict willingness to pay for environmental quality improvements at sites for which primary valuation studies have not been conducted. In virtually all benefit transfers of this type, a single regression model is fit to all source points in the metadata, and used to produce out-of-sample predictions for all possible policy-site applications. Despite the advantages of this approach over other types of benefit transfer, the predictive accuracy of these MRMs generally leaves room for improvement. This dataset enables reproduction of the presented locally-weighted regression approach to MRM estimation, for an empirical application on willingness-to-pay for water quality improvements. The metadata are drawn from primary stated preference studies that estimate per household (use and nonuse) WTP for water quality changes in specific U.S. water bodies. Changes in water quality, in turn, affect ecosystem services including aquatic life support, recreational uses (such as fishing, boating, and swimming), and nonuse values. Studies were limited to those for which WTP estimates could be readily mapped to water quality changes measured on a standard 100-point Water Quality Index (WQI). All monetary values were adjusted to 2019 U.S. dollars. The data includes 188 observations from 58 prior stated preference studies, with the earliest of these published in 1980. Variable definitions are provided in the attached data dictionary file. Additional details of the metadata are described in Moeltner et al. (2023).