Browsing by Author "Suter, Jordan F."
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Item Linking Eligibility for Agricultural Subsidies to Water Quality(Department of Applied Economics and Statistics, University of Delaware, Newark, DE., 2017-03) Palm-Forster, Leah H.; Suter, Jordan F.; Messer, Kent D.Item Machine Learning Based Policy to Ease Information Asymmetry in Non-Point Pollution Management(Department of Applied Economics and Statistics, University of Delaware, Newark, DE., 2017-03) Fooks, Jacob R.; Messer, Kent D.; Suter, Jordan F.This research examines how an artificial neural network incorporating high-frequency monitoring data and natural system dynamics can inform policies that regulate an environmental externality with inherent information asymmetry. Using an experiment with both students and agricultural producers we study strategic behavior under various policies and measure participants’ relative values for different levels of information accuracy under such policies. First, we show that a neural-network-based recursive filter can be applied to monitoring data to estimate an individual polluter’s contribution to the ambient level of pollution, in essence, turning nonpoint sources into estimated point sources. We then test the implications of this result using an economic experiment that explores the effects of spatial relationships and the information structure of policies on behavior and preferences. The results of the experiments show that participants change their emissions in response to both policy and information treatments and that there are no significant differences in behavior between professional and student participants. However, we find that the agricultural producers are more willing than student participants to pay for policies that more accurately target the individual sources of pollution. This latter result suggests a strong preference for polluter-pay policies instead of ambient-based policies amongst producers, even if they do not necessarily lead to higher total profits.Item Spatial Attribution in Nonpoint Source Pollution Policy(Department of Applied Economics and Statistics, University of Delaware, Newark, DE., 2018-01) Fooks, Jacob R.; Messer, Kent D.; Suter, Jordan F.This research uses experiments involving student and farmer participants to explore the informational structure of nonpoint source pollution policies in a spatial, physically realistic stream setting with high frequency end of stream sensing. The data allows for an approximate solution to the “attribution” problem using an artificial neural network based recursive filter. This provides estimates of individual parcel contributions to the ambient pollution level. These estimates are noisy, and prove to have an interesting endogenous error structure. This is incorporated into a policy which imposes differentiated taxes on polluters. Laboratory experiments compare this estimated pollution source policy, ambient exogenous targeted tax policy, and a perfect information policy interacted with corresponding information sets. After taking part in all treatments, participants’ values for these different policies were measured using a voting mechanism. Additional information on the part of the regulator leads to increased production over the efficient level, with additional participant information exacerbating the problem. Even though the ambient policies performed better in aggregate, the voting data indicated positive willingness-to-pay for the estimated policy by some students who were located to strategically benefit from this information, while farmer subjects had a large positive willingness-to-pay for exact information, irrespective of parcel location.