Payment for environmental service contracts commonly require actions beyond adoption of a practice, such as undergoing specified enrollment procedures, granting consent to being monitored, and paying penalties for violations. These provisions are a bundle of attributes a landholder must accept with contract enrollment, leading to transaction costs in the contracting process. This article develops a principal-agent framework to study the links between these transaction costs and the well-known information asymmetries between the landholders and the government agency offering contracts. Using stated choice data collected from a sample of farmers, we estimate a mixed logit model to quantify the contribution of different contract attributes on contract willingness-to-accept (WTA). More stringent provisions in contracts were found to raise individual WTA by widely differing amounts across farmers, but the average effects imply that overall contract supply is sensitive to stringency. From a series of microsimulations based on the estimated model, we find that transaction costs create a significant drain on the cost-effectiveness of contracting from the agency's point of view, similar in magnitude to the inefficiency created by hidden information. While stringent contractual terms raise program expenditures, they may be justified if they raise compliance rates enough to offset the added cost. We also simulate an implicit frontier to trace out the change in compliance needed to justify a given increase in stringency. For environmental benefits in the range of previous estimates, this analysis suggests that stringent terms would need to substantially raise compliance rates to be cost effective.
Erosion of agricultural croplands is a significant contributor of sedimentation to reservoirs. Here, physiographic and economic models for a large agricultural watershed (2377 square miles with 27 subwatersheds) are integrated for the reduction of sedimentation of one Midwestern reservoir. Sediment reduction and the cost-effectiveness of three agricultural best management practices (no-till, filter strip, and permanent vegetation) implementation were considered under three modeling scenarios: random assignment; the globally most cost-effective approach; and a cost-effective targeting approach. This study demonstrates how physiographic and economic data can be harnessed to yield readily comprehendible cost-effective targeting maps. Cost-effective targeting may be preferable to watershed managers for its “user-friendliness” without too great a sacrifice of the globally most cost-efficient solution.
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