Revealed Preferences from Voluntary Contributions
提出一种基于捐款数据恢复公共品补偿变差的方法，利用预期效用模型和双障碍模型估计结构参数，并通过蒙特卡洛模拟和草地鸟类栖息地保护案例验证其有效性。
We propose a method for recovering the compensating variation for a public good based on donation data. People face a take-it-or-leave-it actual donation decision, and the public good is provided if a threshold contribution level is reached. Compensating variation is derived using an expected utility model that accounts for nonparticipation, free riding, and warm glow, and a double hurdle model is used to recover estimates of structural parameters. Monte Carlo simulations suggest that the approach reliably recovers willingness to pay measures with moderate bias, and this bias is reduced when donations are combined with data on the donors’ subjective probabilities that the threshold will be reached. We illustrate the methodology through an empirical application involving the protection of grassland bird habitats.