Research
Command-Control versus Market Incentive Policies for Non-point Source Pollution
This paper aims to compare the cost-effectiveness between command-control and market instruments in addressing non-point source pollution. By definition, non-point source pollution (NPSP) is extremely difficult to observe individual level discharge and thus, very hard to implement market incentive policies. Few observational studies examine the cost effectiveness of NPSP policies because it is difficult to study how individual polluters respond to pecuniary incentives to abate. I exploit a policy setting where agricultural runoff is in fact, a point source pollution but is regulated as if it were NPSP which allows the study of abatement behavior in what is typically a NPSP setting. I study a program called the Florida Everglades Forever Act intended to reduce phosphorus runoffs from entering the sensitive Everglades ecosystem. The program consists of both a command-control component as well as a market incentive component which I am able to disentangle using a new dataset I developed on annual farm level discharge and subsidies for pollution reduction. The dataset allows the use of the two-step Arellano-Bond estimator to estimate a marginal abatement cost (MAC) curve for the average farm. Using the estimated MAC curve, I simulate the costs under the market mechanism and compare that with both data-estimated and engineer-estimated costs under command-control. I find that to achieve the same benchmark pollution outcome, the market mechanism would reduce compliance cost by 20%.
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Regulating Non-point Pollution with Ambient Tax: Are more monitors better?
Non-point source pollution occurs when pollution is not observable at the individual level by the regulator. Traditional tools like Pigouvian taxes are not applicable here but a tax can be applied at the group level based on joint pollution production. Such policies are referred to as ambient taxes and they are a promising alternative to ameliorate the problem of non-point source pollution. Ambient taxes can induce polluters to collectively meet the pollution standard at least cost under certain settings. But in general, it may achieve the regulator’s pollution target at higher than least cost if not at highest cost. This result is due to the presence of free-riding incentives. I show in this paper the conditions in which uncertainty about firm types may lead to incorrectly setting the uniform ambient tax rate which then creates the potential for free-riding. I also compare the Nash and Sub-game Perfect Nash equilibria and analyze the potential welfare gains of adding more water quality monitoring points. I find that expanding the network of water monitors in such a setting does not always reduce free riding potential and, in some cases, may increase it.
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Indemnity Payments and Payments for Ecosystem Services: Policy Implications (joint with Chris Costello)
Payments for Ecosystem Services (PES) are voluntary programs where private or public beneficiaries of ecosystem services (a public good) agree to pay private producers of ecosystem service (ES) inputs. However, when there is private risk to the private provisioning of ES inputs, then there may be gains to offering loss protection (indemnity). This paper characterizes conditions in which it is optimal for a budget constrained regulator to (i) offer indemnity in conjunction with a linear pricing contract and (ii) to pursue both poverty alleviation and the social benefits of the ES inputs. We find that it is optimal for the regulator to completely take on the risk of producing ES inputs (or outputs), i.e., offer full indemnity, if agents are risk neutral. Furthermore, the value from optimally choosing the indemnity, compared to the no-indemnity case, is higher whenever agents are more risk averse with simulated increases in ES supply between 5\% and 40\% for the same budget. We also provide a guide to practitioners and empirical researchers on how to evaluate the appeal of indemnity in any particular setting for which PES exists and provision of inputs is risky. Lastly, we identify a estimatable threshold for the business-as-usual ES supply curve slope above which it is optimal to pursue the dual objective.
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Highway infrastructure and safety implications of AV technology in the motor carrier industry
This study explores the infrastructure investments needed to support the adoption of autonomous vehicles and provides potential safety benefits from AV technologies as a possible motivation for such investments. We assume that the motor carrier industry will be (one of) the first adopter(s) of such technologies, and therefore is the focus of this study. Using large truck crash data from 2013 through 2015 obtained from the Missouri State Highway Patrol, Chi-square Automatic Interaction Detection decision trees are estimated to examine the effect of AV technologies on motor carrier crash severity. Results suggest that the greatest contributory predictors of crash severity outcomes are driving too fast for conditions, distracted/inattentive driving, overcorrecting and driving under the influence of alcohol. If these circumstances are altered by AV technologies, it is suggested that between 117 and 193 severe crashes involving large trucks could be prevented annually in Missouri alone. To render such safety benefits, key vehicle needs include autonomously controlling acceleration and steering, monitoring of the environment, and responding to dynamic driving environments without the need for human intervention. Importantly, the safe operations of a system that can perform such AV tasks require readable lane markings to capitalize on potential safety benefits.