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«Toward More Effective Endangered Species Regulation By Jacob P. Byl Dissertation Submitted to the Faculty of the Graduate School of Vanderbilt ...»

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The federal government’s center of expertise in economic analysis of regulations is the Office of Information and Regulatory Affairs (OIRA) within the Office of Management and Budget (OMB) in the White House. The current guidance from OIRA on economic analysis is Circular A-4, which explains that the goal when estimating impacts of regulation is to measure the entire range of costs and benefits that accrue to people in the United States because of proposed regulations (OMB 2003). The preferred method for measuring benefits of regulation is to use measures of what people are willing to pay for improvements in quality of life. Costs are estimated by adding the expected administrative costs for the agency with the costs of additional burdens on regulated parties.

Cost-benefit analysis can help lead to win-win situations because resources can be focused on places where they are most effective, leading to more of the desired regulatory outcome with lower costs. Circular A-4 describes the goals of economic analysis as to “(1) learn if the benefits of an action are likely to justify the costs or (2) discover which of various possible alternatives would be the most cost-effective” (p. 2).

By choosing the most cost-effective regulations, agencies are able to achieve better regulatory results.

To see how this can lead to a win-win outcomes, consider a hypothetical with an agency that is charged with protecting the national tree, the oak. This agency has the daunting task of figuring out how to protect a national symbol that is important to people and ecosystems, but also forms the basis of livelihoods from forestry to cooperage of wine barrels. Suppose that the agency is interested in pursuing a proposed policy A, where A could stand for improvement of oak savannah habitat or some other agency action. To assess whether proposed policy A to protect oaks is a net benefit to society, the agency can use economic analysis. Circular A-4 calls for the agency to clearly lay out alternatives to the proposed regulation, for example policy B that targets improvement of oak savannah habitat on federal land and policy C that is a no-action alternative. For each of the alternatives, the agency calculates the expected costs and benefits of the action.

Once the expected costs are subtracted from the expected benefits, the agency has an estimate for the net benefit of each alternative. If the expected net benefit of policy A is

-$50 million, then it is a signal that the rule may not be in the best interests of society. If alternative B has an expected net benefit of $50 million and alternative C has an expected net benefit of $0, then the economic analysis suggests that regulatory policy B is the preferred action. Compared with the original proposal of A, policy B can offer more effective protection of the oak that results in more benefits to society at lower costs. By moving forward with regulations that focus resources where they are most effective, the agency can do a better job fulfilling its mandate to protect our national tree.

B. Cost-Benefit Analysis of Other Environmental Regulations The call for the FWS and NMFS to consider economic factors in critical habitat designation is similar to the calls for economic analysis in the Clean Air Act and Clean Water Act, both written around the same time as the ESA. Agencies and courts have interpreted this language for the past thirty years to mean agencies should use costbenefit analysis when possible (U.S. EPA 2003).

To measure benefits of proposed regulations, the EPA typically relies on studies that look at how much a relevant population would be willing to pay to achieve a change in regulatory outcomes. To measure costs of proposed regulations, the EPA often relies on data provided by regulated industries to estimate the value of the burdens created by the new regulations.

For example, economic analysis of a proposed change in the standard for particulate matter in the air showed that the expected benefits of cleaner air and fewer premature fatalities caused by pollutants outweigh the expected costs by about ten to one (U.S. EPA 2012). The EPA who administers the program on air pollution considered an alternative standard that would have resulted in higher benefits, but also higher costs. By using economic analysis, the EPA had valuable information to help choose between the alternatives. Even when an agency chooses an alternative that does not have the highest net benefit, laying out the alternatives and considering the costs and benefits of them can be a valuable exercise in making thoughtful decisions that add transparency because the economic analyses are publicly available (U.S. EPA 2003).

This is not to say that cost-benefit analysis is without challenge or controversy.

Measuring benefits of health and safety regulations often involves estimating the value of saving human lives, which can be a difficult exercise because, fortunately, people are not directly traded on markets. This means that economists have to estimate values of saving lives by looking at things like wage premiums for risky jobs and willingness-to-pay for safety features in consumer products. Many, such as Zelizer (2001), see estimating values of saving lives as a disrespectful practice because it appears to put a price on the value of people.

Like the EPA, the agencies that implement the ESA are likely to have some challenges and controversy when it comes to quantifying costs and benefits of proposed regulations. Despite these downsides, the FWS and NMFS can benefit from the valuable information that can be provided by accurate cost-benefit analysis of proposed critical habitat designations.





IV. Measuring Benefits of ESA Regulations The FWS and NMFS only consider narrow categories of costs and benefits of critical habitat. This is a reasonable reading of the ESA, but it also risks missing the forest for the trees. This section discusses how broader measures of costs and benefits will lead to more accurate cost-benefit analysis that follows the guidance provided by OIRA and parallels the practices of other agencies that use economic analysis for environmental regulations.

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burdens on the agency to administer the areas. In the economics literature, people have argued that there are real costs to the ESA, including critical habitat designation. Shogren (1998) uses economic theory to show why there can be real costs to critical habitat designation for private landowners. Zabel and Paterson (2006) try to measure the empirical effect of critical habitat designation by looking at building permits issued in California before and after proposal and designation of critical habitat. They find evidence that builders expect development to be more expensive after land becomes critical habitat with a 37% decrease in the long-run supply of housing permits. This indicates that, at least on the cost side, the designation of critical habitat does matter in the market. List et al. (2006) look at the effect of critical habitat designation on property values and find a 22% decrease for properties that are within a critical habitat area in Arizona. Estimates of the magnitude of the costs of critical habitat will be used in Part V to help calibrate the scope of benefit estimates.

As pointed out in Viscusi (1992), measuring costs of regulations tends to be more of an accounting exercise than a discussion of

Abstract

economic and policy considerations. Regulated industries and their trade associations tend to have strong incentives to quantify the estimated costs of proposed regulations and publicize those as part of their efforts to avoid or weaken regulatory restrictions. So when it comes to the ESA and critical habitat designation, groups like the American Forest Products Association, the American Builders Association, and the Oil and Gas Production Alliance are likely to be vocal with their (perhaps exaggerated) estimates of the costs. Because measuring costs is usually more straightforward than measuring benefits and there are already well-informed parties that have incentives to provide estimates of expected costs, I focus on the more vexing issue of how to measure benefits of ESA critical habitat.

B. Measuring Benefit Values of Listed Species Following Circular A-4, the starting point for measuring benefits of ESA regulation is to use estimates of what people are willing to pay for the survival and recovery of the listed species. The benefit of the existence and revival of species can be measured through willingness-to-pay studies. Willingness-to-pay studies use various techniques to elicit from members of a relevant population how much they value a change in regulatory outcomes. When species are commercially valuable, such as salmon, estimates of benefits can be based on market prices (Loomis & Richardson 2008). More often, species are not traded on markets and benefit estimates are derived using other methods. Travel-cost studies look at how much people are willing to pay to travel to a particular place to have an experience interacting with a natural feature, such as how much a family is willing to pay to experience a whale sightseeing tour. Willingness-topay for travel can be used to back out how much people value the experience of seeing the whales. This can give researchers a sense for how much people value the existence and success of the species itself.

Stated-preference studies use surveys to ask people from relevant populations how much they are willing to pay for changes in regulatory outcomes. Stated-preference studies have the benefit of being flexible and allowing researchers to capture values for a range of species and scenarios, but the studies require careful attention to details like wording of questions (Arrow et al. 1993). Otherwise, estimates can vary greatly with small changes in methodology. Despite this drawback, stated-preference surveys are the most common way to measure benefits of endangered species because they are the only way to capture values for some species. For example, there are no market prices to signal the value of a commercially worthless species that people are never going to cross paths with. Yet, those same people may care about the existence of a bird in the Arctic National Wildlife Refuge that fits that description, even if the people never plan to travel there (U.S. NMFS 2002). If people care enough about that bird to pay money for its protection, then those values should count as benefits for regulatory protections for the bird.

Economists have estimated values of benefits for over forty different species (U.S. NMFS 2002). The average respondent in the studies was willing to pay an amount ranging from $12 (in 2014$) to save the Atlantic salmon in Maine to over $200 to prevent the extinction of the humpback whale. These studies can be used to calculate benefit values of protecting the species by extrapolating the survey responses over the relevant populations (Jakobsson & Dragun 1996).

Existing studies that measure willingness-to-pay for species provide starting points for estimates of benefits of protecting critical habitat of species. Although a new study for each species is the ideal way to estimate benefits of protecting species, this can prove to be cost and time prohibitive (U.S. EPA 2003). Fortunately, it is not necessary to do a new study for each species in each specific location. Benefits transfer measures can lead to reasonable estimates of benefits of saving species that have not been directly studied. OIRA’s Circular A-4 recommends estimating benefits by using transfer calculations, which provide systematic ways to gather estimates from different contexts and use them to estimate benefits in a new context.

C. Measuring Benefits Values of Habitats with Ecosystem Services This section describes why it is important to implement economic analysis with a broad sense of benefits. Benefits of endangered species are not limited to the values people place on the listed species themselves. The ESA is intended to protect “endangered species... and the habitats upon which depend” (16 U.S.C. § 1531(b)). So the benefits of these protections should not be limited to the benefits of the species that are listed under the ESA; when ecosystems are conserved because of the ESA, the benefits that flow from those ecosystems to people should all be counted as benefits of the regulation. For critical habitat designation, this can be done by using measures of ecosystem services like water filtration and carbon sequestration.

One way to interpret the language of the ESA is to think of the economic benefits that flow from the listed species and the ecosystems that are conserved because of the listed species. The conflict between loggers and environmentalists in the Pacific Northwest is not just about the listed Northern Spotted Owl. The conflict is about how we choose to balance economic values of harvesting old-growth timber versus the values of preserving these ecosystems that are unlikely to reappear if destroyed. There is a paradox in the current situation where the full economic value of harvesting timber is measured but the value of protecting the old-growth forest is limited to the benefits that accrue to a small handful of imperiled species. A reasonable way to measure the benefits of endangered species and the ecosystems they inhabit is to use the economic valuation tools that are often used in other fields. Ecologists think of the benefits that flow from ecosystems to people as ecosystem services (Nagle et al. 2013). Economists use various techniques to put values on these streams of services (Richardson & Loomis 2008). Using existing estimates of ecosystem services, the FWS and NMFS can start to measure some of the values of benefits that flow from the ecosystems upon which endangered species depend.

When the EPA measures benefits of air or water regulations, they measure the benefits of the reductions in the pollutant at issue (U.S. EPA 2012b). They also measure benefits of reductions in co-pollutants, meaning other pollutants that are not the direct subject of this regulation, but that are predicted to fall because of the regulation. For example, in air regulations to limit emissions of NOx, we also see drops in ground-level ozone. So the EPA estimates the benefits of lives that are saved because of reduced NOx and the benefits of lives that are saved because of reduced ozone.



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