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).

本数据集包含了用于生成发表于Moeltner, K., Puri, R., Johnston, R. J., Besedin, E., Balukas, J. A., & Le, A. (2023)的《局部加权元回归与效益转移》一文中结果的原始数据及其数据字典。元回归模型（MRMs）在效益转移中广泛应用，以估算具有广泛适用性的、被称为‘总括’效益函数的模型，该模型可用于预测未进行原始估值研究的环境质量改善的支付意愿。在几乎所有此类效益转移中，单一的回归模型被拟合至元数据中的所有源点，并用于对所有可能的政策-地点应用进行样本外预测。尽管与其它类型的效益转移方法相比，该方法的优点显而易见，但这些MRMs的预测准确性普遍存在提升空间。本数据集使得再现所展示的局部加权回归方法在MRM估计中的实证应用成为可能，特别是在水质量改善的支付意愿方面。元数据源自对特定美国水域水质变化每户（使用与非使用）支付意愿进行估算的原始陈述偏好研究。水质的变化反过来又影响生态系统服务，包括水生生物支持、娱乐用途（如捕鱼、划船和游泳）以及非使用价值。研究仅限于那些支付意愿估算能够轻易映射至标准100点水质指数（WQI）测量的水质变化的案例。所有货币价值均已调整为2019年美国美元。数据包括来自58项先前陈述偏好研究的188个观测值，其中最早的研究发表于1980年。变量定义提供在所附的数据字典文件中。元数据的详细信息在Moeltner等人（2023）中有所描述。