Brucellosis in the Greater Yellowstone Area (GYA) is not only a disease problem but also a complex social and economic problem. The disease is costly as it diminishes economic values associated with ranching, tourism, and related outdoor activities such as wildlife viewing and hunting, in addition to broader concerns about conservation. These costs may dramatically increase if brucellosis spreads beyond the GYA, particularly if infected cattle were moved to new, high-risk areas. There is a collective desire to address brucellosis, but managing it has been challenging as it involves allocating money among various costly management options that produce uncertain benefits accruing over a long time frame. Further complicating matters is the number of individuals, stakeholders, and agencies with authorities over various aspects of the problem and the fact that the benefits of management may not accrue to those incurring the cost (e.g., cattle producers in Kansas may benefit from reduced risks of infected GYA cattle exports). Moreover, costs and benefits can vary considerably and spatially across the various stakeholder groups (e.g., costs and benefits may differ in Montana and Wyoming). These and other social concerns around brucellosis can be addressed by using economics to examine the issues, as economics is a decision science that can be used to assess costs and benefits, help determine socially and politically desirable strategies, and assist in designing policies that incentivize individuals for taking part in the desired strategy. However, economic analysis for the GYA requires a coupled-systems approach in which values are derived from models of ecological-socioeconomic interactions. This is because disease-control activities are investments that alter ecological and disease dynamics to produce benefits, perhaps in conjunction with some costs, that accrue over time. Accordingly, disease ecology plays a key role in determining economic outcomes. Human behavior also matters, as the actions of individuals and resource managers related to managing risks will affect economic outcomes and may further impact the disease ecology of the system.
The appropriate tool for assessing the short- and long-term economic and ecological impacts of managing brucellosis in the GYA is bioeconomic analysis.1 Bioeconomic analysis uses cost-benefit analysis and predictive modeling for coupled ecological and socioeconomic systems (Clark, 2005). A simple bioeconomic cost-benefit analysis would assess the economic impact of particular disease management strategies on public and/or private lands. A more sophisticated approach, however, is using the bioeconomic framework as a decision model to identify strategies––and policies that can effectively implement those strategies––that put society’s scarce economic resources to their most valued (e.g., socially or politically) uses across the GYA. Bioeconomic analysis has not yet been applied for brucellosis in the GYA, and so it is not yet possible to comment on the cost-effectiveness of various management options. Even conducting a simple cost-benefit analysis for one or more management options would be a major research undertaking
1 Bioeconomic analysis has been used to examine coupled systems management since the 1950s (Gordon, 1954; Clark, 2005), focusing initially on fisheries problems where it was used to develop individual tradeable quota (ITQ) and other rights-based markets that have seen increasing use since the 1970s (Costello et al., 2008). Bioeconomic analysis has since expanded to the management and valuation of other natural systems (Fenichel et al., 2016). The approach has recently been applied to address wildlife and livestock disease problems (e.g., Bicknell et al., 1999; Mahul and Gohin, 1999; Horan and Wolf, 2005; Fenichel and Horan, 2007a,b; Horan et al., 2008, 2010; MacLachlan et al., 2017) as well as the more general problem of invasive species management (e.g., Leung et al., 2002).
at the present time due to the need to model a complex, coupled system for which much socioeconomic data are currently lacking; thus, it was beyond the committee’s task to conduct such an analysis.
This chapter describes how bioeconomic analysis can serve as a critical decision-making framework for the adaptive management of brucellosis in the GYA, provides relevant insights from related work, and identifies gaps in knowledge that need to be filled to perform an analysis. The remainder of the chapter is divided into three major sections: Section 2 presents the framework for bioeconomic analysis, with a discussion of both economic costs and benefits (subsection 2.1) and criteria for making decisions (subsection 2.2); Section 3 examines economic efficiency in a complex system like the GYA, with discussion of the economic considerations associated with various risk mitigation and adaptation strategies; and Section 4 discusses economic values in developing appropriate brucellosis control policies in the GYA.
A bioeconomic framework, illustrated in Figure 8-1, involves integrating two types of models. First is a disease ecology model of population growth and disease transmission for a wildlife-livestock system that incorporates the impacts of human actions. This model is commonly based on an S-I-R-type model (susceptible, infected, recovered) that divides the relevant animal populations into interacting sub-populations according to disease status (Anderson and May, 1979) and is modified to account for the impact of human choices on population and infection dynamics (e.g., Fenichel et al., 2011). Recent approaches employ Bayesian state-space models to address disease ecology uncertainties (Springborn and Sanchirico, 2013; Hobbs et al., 2015; MacLachlan et al., 2017), including unobservable states and uncertainty about the effectiveness of human efforts to interrupt disease transmission or to affect other variables such as mortality and reproduction.
Second is an economic model that incorporates human responses to ecological changes for predicting economic impacts over time. This model consists of two components: economic cost and benefit functions, and an economic model of decision making. The first component is a set of economic cost and benefit functions that indicates how various costs and benefits depend on public and private actions, as well as on the likely current and future values of various ecological state variables (i.e., ecological variables that change over time, such as elk populations and prevalence rates). Cost and benefit functions, rather than observed past cost and benefit values, are necessary to predict future outcomes due to the investment nature of disease management. Costs and benefits may be affected by numerous uncertainties such as the effectiveness of prevention activities as well as the magnitudes of ecological variables (broadly defined to include wildlife and livestock variables) and their dynamics. These uncertainties can change over time as learning occurs. The second component is an economic model of decision making (e.g., by ranchers, hunters, and resource managers)that indicates how actions are chosen in response to current and predicted future ecological states, knowledge states, and the choices of other decision makers (e.g., regulators). The modeling of behavioral responses is important because economic outcomes ultimately depend on public and private choices in conjunction with ecological and knowledge outcomes; analyses that ignore behavioral responses can yield inaccurate results (Finnoff et al., 2005). The two economic model components––cost and benefit functions and decision-making processes—are discussed separately within the context of the GYA.
2.1 Economic Cost and Benefit Functions
Brucellosis and managing it in the GYA are likely to affect many socioeconomic cost and benefit values. The two main classes of values are market values that are generated via market transactions (e.g., ranching production costs and sales; hunting-related expenditures; tourism expenditures; elk feed costs), and nonmarket values, which arise outside of traditional markets (e.g., values for species conservation, and bequest and other cultural values; Ready and Navrud, 2002; Freeman, 2003; Mazzanti, 2003).
Nonmarket values may seem less tangible, but they nonetheless represent real, quantifiable economic values that are regularly used to inform policy (Freeman, 1993, 2003; NRC, 2004).2 These values may reflect various social issues bearing on decision making, to the extent that these social issues impact positively or negatively on the well-being of individuals with an interest in the GYA.
Both market and nonmarket values stem from how the provision of some good or service affects well-being. Impacts to well-being can be measured in different ways, but having a common metric facilitates comparisons of different impacts. There are several advantages to using a monetary metric. One is that individuals and policy makers are accustomed to evaluating decisions based on monetary values. Another is that many impacts to well-being are already monetized through individuals’ choices in market transactions. Nonmarket impacts are implicitly monetized in situations where individuals evaluate monetary and nonmonetary trade-offs to determine whether a noneconomic impact is worth the economic impact (Freeman, 1993). For instance, when deciding whether to sell the family ranch, a rancher will need to determine whether there will be a direct economic gain (i.e., the offer price exceeds the present value of future profits) and then whether this gain exceeds the loss of nonmarket values (e.g., bequest or heritage values) associated with giving up the ranching lifestyle and the opportunity to pass the ranch to descendants.
There are key market and nonmarket costs and benefits that accrue annually to determine the economic welfare of several important GYA stakeholder groups.3 The investment nature of disease management means the appropriate economic welfare measure for each group accounts for the expected stream of net benefits accruing over time (e.g., an expected net present value). A cost-benefit analysis requires predicting future economic impacts by predicting changes in ecological states and behavioral responses to these changes (Clark, 2005; Finnoff et al., 2005). This means the coupled nature of the system––the manner in which behavior affects and is affected by ecological variables––plays a key role in determining economic impacts. Uncertainty also plays a key role for predicting future impacts. Additional costs arise when individuals are risk-averse, as uncertainty is costly.4 Note that the probability of disease transmission from elk, bison, or cattle herds likely depends on the distribution of these herds across the landscape, so that welfare measures depend on ecological variables.
The GYA ranching community, as well as non-GYA ranchers at risk of infection via GYA cattle exports or wildlife migration, benefits from the business of cattle production: from running outfitting businesses (which includes selling access to their land for hunting and wildlife viewing), and from nonmarket benefits such as the overall enjoyment of their land under various uses (amenity values), wildlife conservation, and the ability to continue ranching over multiple generations (bequest or heritage values) (Schumaker et al., 2012). Ranchers’ economic welfare for any given time period is calculated as producers’ profits from market activities (revenues less costs associated with production and disease management; Just et al., 2005) plus the ranchers’ nonmarket values. These nonmarket values may have ecological linkages, as ranchers may consider infection risks from wildlife when making risk-management decisions and because wildlife abundance affects the demand for hunting and wildlife viewing on their lands.
2 Examples of how nonmarket valuation is used in practice include the following: by the U.S. Environmental Protection Agency to analyze the benefits of numerous environmental programs (particularly to perform cost-benefit analysis under the Clean Air Act); by various statefisheries and wildlife departments to determine recreational fishing and hunting demand for use in improving management; and for natural damage assessment and associated litigation (e.g., the Exxon Valdez and BP oil spills). See McCollum (2003) for a number of specific examples.
3 This discussion is not exhaustive, as research on economic cost and benefit functions for the GYA is limited.
4 Further analysis is necessary in such cases to estimate ranchers’ risk preferences over uncertain outcomes to accurately measure both costs as well as any cost-mitigating behaviors.
The economic cost of brucellosis that is incurred in a given period by GYA ranchers, as well as by non-GYA ranchers with at-risk herds, is the reduction in economic welfare to these ranchers relative to the risk-free outcome. In other words, the costs are reduced profits and nonmarket values. Reductions in profits may stem from reduced productivity on infected ranches reduced market values of goods and services produced on ranches within brucellosis-infected regions due to consumers’ concerns about infection and increased costs due to risk mitigation (actions to reduce the likelihood of cattle infections) and adaptation (actions to reduce economic impacts after infection occurs) (Perrings, 2005).
Brucellosis management policies can reduce ranchers’ disease-related costs, with the policies implemented in one period reducing these costs over many time periods due to the investment nature of brucellosis management. Accordingly, the net benefits of brucellosis management policies are the expected present value of increased economic welfare relative to the case of no intervention.5
Producers’ profit functions are needed to predict disease-related costs or management-related benefits. For various GYA localities, these functions can be estimated econometrically as functions of cattle inventories, herd and disease management activities, and infection risks that depend on wildlife densities.6 These estimates would be based on reported activity levels, farm inventories, revenue and cost data, and data on disease risks from wildlife. The U.S. Department of Agriculture’s National Agricultural Statistics Service (USDA-NASS) collects some data and reports values that are aggregated over farms (to maintain rancher confidentiality) and by certain classes of expenditures (e.g., labor, machinery). But these data do not parse out specific risk-management activities, nor do they or any other data source report on outfitting operations. Ranchers would have to be surveyed to obtain this information. Ranchers were surveyed to estimate the costs of implementing various risk-management practices on an average farm in the southern GYA (Roberts et al., 2012). These risk-management practices include hazing of elk, fencing haystacks, spaying heifer calves, adult cattle booster vaccination, modified winter feeding schedules for cattle, riding through cattle herds to prevent cattle-elk contacts, and delaying grazing on public lands; however, the effectiveness of these practices—along with the associated benefits to ranchers and society—remains highly uncertain (Roberts et al., 2012).
The USDA-NASS data may not provide enough information about price variability in response to spatial infection risks, although this information would be important to perform a cost-benefit analysis. Surveys of ranchers, combined with data on spatial transmission risks from wildlife, might facilitate estimation of these consumer-driven price responses.
Benefit functions for nonmarket goods and services valued by ranchers (e.g., amenities and bequests) are also needed to predict disease-related costs or management-related benefits. These functions can be estimated using stated preference methods that either survey individuals about their values (contingent valuation methods) (Boyle, 2003) or estimate values based on hypothetical choices made by individuals in experimental conditions (attribute-based methods involving choice experiments) (Adamowicz et al., 1998; Holmes and Adamowicz, 2003). Ecological linkages may be important. For instance, amenity and conservation benefits at any point in time likely depend on the current distribution of wildlife herds across the landscape; thus, the surveys or experiments used to elicit the benefit functions will need to account for this ecological linkage. The committee is not aware of such studies for the GYA.
5 The term “net benefits” is used because brucellosis management policies may impose some regulatory costs on ranchers. Depending on the specific policy, thesecosts may include increased rancher expenditures, reduced productivity, or impacts to market values. Regulatory costs, which are generated by specific policy choices rather than the disease itself, are distinct from brucellosis costs.
6 When estimation is based on observed or reported production and risk management behaviors, estimation generally involves making assumptions about the decision-making processes (e.g., maximizing expected profits or utility) that led to these behaviors (Greene, 2017). Hence, economic behavior and also ecological variables, to the extent that behavior depends on these, will play a role in estimation. Also, profits associated with jointly produced activities will need to be jointly estimated. For instance, land uses and risk mitigation practices such as wildlife hazing jointly affect the profitability of both ranching and outfitting activities; see Chambers (1988) for more on the economics of joint production systems.
Market impacts associated with ranching and outfitting go beyond ranchers. As brucellosis risks alter ranchers’ demands for various production inputs (e.g., feed, grazing on public lands, machinery, veterinary services), there will be additional short- and long-run economic effects in related sectors as well as in local economies where ranchers spendtheir income. Furthermore, brucellosis and its management may adversely impact the provision of outfitting services and hence consumers of these services (e.g., hunters or wildlife viewers).
Hunting may occur in outfitting markets involving access payments to landowners or outside of markets where such fees do not arise (e.g., on public lands). In either case, economic net benefits to hunters are defined as the willingness to pay for hunting less hunting-related expenditures (e.g., travel, access payments, permits, and equipment) and the opportunity cost of leisure. Hunter benefits can be calculated based on the demand for hunting trips. This demand can be estimated using random utility models that are based on actual trip behavior (a revealed preference approach) (Freeman, 1993; Parsons, 2003). Estimates are produced spatially and can indicate how behavior is responsive to changes in ecological states. The demand for hunting trips may also be estimated from surveys that ask hunters where they might visit, rather than where they did visit, under different scenarios (a stated preference approach) (NRC, 2004).
Instead of estimating the demand for hunter visits, which does not link to harvesting directly, Kauffman and colleagues (2012) estimate hunter demand for elk permits in northwest Wyoming. This framework is more applicable to a bioeconomic analysis involving harvesting behavior (Fenichel et al., 2016). Kauffman and colleagues (2012) model this demand as a function of elk and wolf populations, and hunter success––which in turn is estimated as a function of elk numbers—and found a link between elk population and demand that could have significant economic impacts. For instance, they found that a 50% reduction in seven elk herds with access to feedgrounds may reduce regional expenditures (not economic welfare) associated with elk hunting by more than $500,000 per year (Kauffman et al., 2012). This finding indicates a potential economic trade-off between the benefits of disease management and hunting.
Wildlife Viewers’ and Conservationists’ Values
Economic benefit functions for wildlife viewing can also be modeled based on the demand for trips. Loomis and Caughlan (2004a,b) used a stated preference approach to examine the relation between elk and bison numbers in the National Elk Refuge (NER) and the Grand Teton National Park (GTNP) and tourism to these locations and the associated economic outcomes (with no consideration of disease impacts). Their approach did not facilitate estimation of a tourism benefit function that depends on elk and bison numbers, which is required for a bioeconomic analysis. However, their results do indicate a potentially large economic trade-off between the benefits of disease management and wildlife viewing.7
Conservationists (including landowners, hunters, and tourists) may have nonmarket values that depend on wildlife stocks: for instance, values for wildlife populations that are at healthy abundance levels and safe from disease, or values associated with using predators to control populations. Wolves serve as a predator control for elk and positively influence visitation to Yellowstone National Park (YNP), thereby
7Loomis and Caughlan (2004a,b) estimate how several discrete management scenarios might alter visitation choices by GTNP tourists and how this would impact income (not the social net benefits of tourism) to the local economy. For instance, under management conditions at the time of that study, (nonlocal) tourism-related income was estimated at $306.5 million. Under a scenario involving reduced elk feeding and hunting regulations that may reduce elk populations by nearly 28% and bison populations by nearly 42%, tourism-related income may be reduced by $23.3 million due to less tourism. Under a scenario of no active management (no feeding, no limit on hunters) that would reduce elk populations by nearly 71% and reducebison populations by nearly 67%, tourism-related income would fall by $62.2 million.
generating economic value (Duffield et al., 2006).8 Conservationists may also have nonmarket values that depend on certain activities, such as negative values (costs) associated with the number of animals vaccinated using biobullets (which are no longer used), the number of animals culled inside national parks, the number of animals whose migration is limited by human intervention, and the amount of supplemental feeding that occurs. Many of these latter values may reflect a preference that wildlife populations remain “wild” in the NER and GTNP (Loomis and Caughlin, 2004a). Also, other things being equal, marginal values of these costs and benefits might be larger for bison, which are considered a symbol of the American west. The sorts of “non-use” values described are stated preference methods.
The Public Sector’s Values
Local, state, and federal agencies are stakeholders that incur costs from program expenditures and also benefit from generating revenues (e.g., hunting fees) to fund other programs. The social cost for the public sector to manage elk (test and removal, low-density feeding, and strain 19 vaccination) may exceed public expenditures on risk management due to transaction costs and the opportunity cost of reallocating resources from other valued programs (Alston and Hurd, 1990; Boroff et al., 2016).
Values of Ecosystem Services and of Learning
The nonmarket values previously described do not include any values associated with dynamic ecosystem processes and learning. Pertinent ecosystem processes would include valuable services such as the reproduction of healthy wildlife, as well as costly disservices such as disease transmission. These services are generally not valued directly, but instead have value because they affect the longer-run production of other goods and services that have value. For instance, hunters and conservationists generally do not directly value wildlife reproduction that produces healthy offspring; they value the future use and enjoyment of these offspring. People also do not place a negative value directly on disease transmission, but rather a negative value is placed on the adverse future outcomes from having more infected animals.
Ecosystem values are calculated as part of bioeconomic analysis, and these values play an important role in determining the values associated with various management activities (Barbier, 2000).9 Activities that enhance reproduction of healthy animals are an investment in a valued natural asset (healthy wildlife) that produces returns accruing in multiple future periods (benefits of future conservation, wildlife viewing, and harvesting). Activities that manipulate the natural system to produce valuable information for future management represent a valuable investment in knowledge assets. Activities that enhance disease transmission are an investment in a costly natural liability (infected animals) that produces losses accruing in multiple future periods (losses from infected cattle and welfare losses to conservationists). Alternatively, actions to reduce transmission are beneficial investments in reducing disease liabilities. The perspective that actions affecting ecological and information (knowledge) dynamics represent investments in future economic impacts facilitates the calculation of the longer-term costs and benefits of an action and also makes it possible to attribute these values to the relevant stakeholder groups.
Many disease-control actions do not only affect transmission; they may also adversely affect valuable resource assets in addition to reducing disease liabilities, generating costs in addition to disease-control benefits. For instance, harvesting wildlife non-selectively (i.e., irrespective of disease status) removes healthy animals so that they are unavailable for future use or enjoyment by humans or for reproduction, and it removes diseased animals so that they are unavailable for generating future disease costs. Supplemental feeding is also non-selective, as it is not possible to only reduce feeding of infected animals. Therefore,
8 The economic impacts reported were based on spending rather than net benefits. Economic impacts to cattle and hunters due to wolf predation are also discussed, but Duffield and colleagues (2006) acknowledgea number of uncertainties, and no formal analysis was provided.
9 The bioeconomic approach to the valuation of ecosystem services is also referred to as the dynamic production function approach to economic valuation, which is an improvement over static approaches (Barbier, 2000).
reduced feeding is beneficial in terms of reducing infectious contacts and increasing mortality rates of infected animals, but costly in terms of increasing mortality rates of healthy animals.
The returns or losses to ecological and knowledge investments, which vary over space and time, ultimately depend on how the populations are managed spatially over time (Barbier, 2011). This means that GYA wildlife and cattle management strategies at different points in time and space and for different purposes are not independent.
2.2 Decision-Making Criteria
The expected economic outcomes associated with disease management depend on the specific management choices. These are not merely public policy choices but the private responses to those choices. It is therefore necessary to model private and public decision-making processes to predict and analyze behavioral responses to disease risks and other economic considerations.
Economic models generally assume decisions are made to maximize some private or public measure of economic welfare. For instance, private individuals such as ranchers may be concerned about profits as well as nonmarket benefits associated with their family’s quality of life and altruistic concerns about ecological and societal outcomes. But even with altruistic concerns, ranchers and other private individuals will generally be unwilling to incur significant costs for activities that benefit others. This means private individuals will have insufficient incentives to invest in activities that largely benefit others (referred to as generating positive externalities) (Baumol and Oates, 1988; Hanley et al., 1997). For instance, ranchers are unlikely to adopt many biosecurity activities because the private costs outweigh the private benefits (Roberts et al., 2012), even though the public benefits may outweigh the costs.
Given a model of how individuals make decisions in response to economic factors (including policy variables) and ecological factors, it is possible to perform a simple cost-benefit analysis that analyzes the expected welfare impacts of particular public policies. Specifically, such an analysis would involve predicting the dynamic economic and ecological feedback responses to a particular, pre-defined policy scenario in order to predict the expected short- and long-term economic impacts to the various stakeholder groups. Simple cost-benefit analyses that have been performed for the GYA generally focus on a subset of the system, such as impacts to one or few stakeholder groups, and may not fully incorporate dynamic feedback responses. Examples include analyses of discrete scenarios (in static settings) of the expected net benefits of elk management (Boroff et al., 2016) and the unintended consequences on elk hunting (Kauffman et al., 2012).10
A more sophisticated cost-benefit analysis evaluates economic-ecological trade-offs to determine the most desirable management strategies according to some socially relevant criterion. Specifically, public agencies’ objectives may reflect a variety of considerations based on what the public wants, as reflected by some public preferences over the distribution of costs and benefits among various stakeholder groups (Rausser, 1982; Gardner, 1987; Mueller, 1989; Rausser and Foster, 1991).
10 A break-even analysis was conducted for various discrete scenarios involving various on-farmrisk-management practices. This includes hazing of elk, fencing all haystacks, spaying 100 heifer calves each year, administering adult booster vaccinations, feeding cattle only 1 day’s worth of hay each day (rather than leaving 2 days’ worth on the ground) in the winter, hiring a rider to prevent cattle-elk contacts by riding through cattle herds for 4 hours per day for 6 months, and delaying grazing on public lands by 1 month (Roberts et al., 2012). That study specifically determined the minimum level of practice effectiveness that makes each scenario profitable. Theanalysis provides insights into relative costs of various practices. However, as the break-even point yields no net benefits, theanalysis does not indicate the cost-effectiveness of each scenario. The cost-effective allocation of practices would minimize the costs of attaining a particular level of risk reduction, given current densities of infected elk. Such an analysis would require abandoning the focus on discrete scenarios limits and would instead determine the optimal level of effort applied to each practice(e.g., determining the percentage of young heifers to spay, the number of days to delay grazing, the time allocated to hazing and riding through herds, etc.).
Economic efficiency provides a benchmark in guiding public policy, as an efficient strategy is expected to allocate economic resources to the most socially valued uses based on the costs and benefits. Specifically, an efficient strategy is determined by asking whether each dollar to be invested in a particular disease control action is expected to provide net social gains. If so, then the investment would be made and the same question would be asked about the next dollar as well as for all other potential management options. Efficiency is attained when no more gains are expected to arise from investments in any action, including planned future investments. The result is that the set of actions planned to be taken now and in the future will maximize the expected present value of current and future economic net benefits to society. Computationally, the efficient strategy can be identified using the optimization approach of stochastic dynamic programming to address dynamic considerations as well as uncertainty about the dynamic processes that generate future states (e.g., Leung et al., 2002). The approach will involve partially observable Markov process models of disease ecology when there is also uncertainty about current states (e.g., Springborn and Sanchirico, 2013; MacLachlan et al., 2017). Optimal strategies are updated as new information emerges to better quantify uncertainties. The process ideally takes learning into consideration; the expected benefits of actively perturbing the natural system to yield valuable information for improving future management are an explicit consideration when choosing among alternative actions and the levels of these actions (Grafton and Kompas, 2005; Springborn and Sanchirico, 2013). The bioeconomic approach therefore brings economics to bear on adaptive management in ways that differ from recent analyses of adaptive management for the GYA (e.g., Hobbs et al., 2015) but are still consistent with Walters’ (1986) definition of active adaptive management. As efficient strategies are identified, the bio-economic approach simultaneously estimates the economic values of these actions as well as the ecological impacts, with these predictions also being updated annually.
The efficiency criterion does not directly deal with how to equitably distribute benefits across various stakeholder groups, but it does determine the level of benefits that can be distributed. Importantly, by maximizing expected net benefits to society, the efficient strategy prevents wasteful use of resources so that, in principle, more benefits can be had by all. The concept of equity is dependent on value judgments, which are often political decisions left in the realm for decision makers to decide (Just et al., 1982). However, cost-benefit analysis based on efficiency considerations can be used to inform decision makers on the equity implications of various policy approaches.11
Decision criteria defined solely in terms of expected economic outcomes (e.g., efficiency or maximizing an objective that differentially weights the welfare of different stakeholder groups) give policy makers considerable flexibility in selecting a management strategy. But sometimes agencies also have ecological objectives in addition to economic objectives. For instance, brucellosis disease eradication has been a focus of the Cooperative State-Federal Brucellosis Eradication Program (USDA-APHIS, 2012) and the former Greater Yellowstone Interagency Brucellosis Committee, and it is addressed in the disease ecology literature (Roberts and Heesterbeek, 2003). While eradication is not the stated objective of the IBMP (2015), the Interagency Bison Management Plan (IBMP) does state “these management actions demonstrate a long-term commitment … towards the eventual elimination of brucellosis in free ranging bison in Yellowstone National Park” (USDOI and USDA, 2000).
11 More than one set of policy tools (e.g., taxes, subsidies, regulations) are capable of promoting efficiency, with each approach generating different distributions of economic welfare among thevarious stakeholder groups. In theory, these distributions can be fine-tuned using non-distortionary policies (i.e., lump sum taxes or payments) that do not cause individuals to deviate from the efficient choices. However, welfare economics suggests that non-distortionary inter- and intra-generational transfers to achieve distributional objectives will not always be feasible, and it also suggests considering both efficiency and distributional effects in optimal policy design (e.g., Gardner, 1987; Stiglitz, 1987). Even so, efficiency remains a desirable benchmark from which to evaluate theseother objectives, as all stakeholders can benefit when the same distributional objectives are achieved more efficiently. Because of this, the committee focuses on efficiency but notes that in some instances the economic objective may include distributional considerations.
Efforts can be made to cost-effectively achieve eradication (i.e., achieving the ecological objective at minimum cost to society), so that both economic and ecological objectives are addressed. However, if eradication is not efficient, then pursuing this objective will waste resources relative to how it could have been spent to more efficiently manage the problem (Fenichel et al., 2010).12 Imposing a specific ecological objective (such as eradication) that would not be satisfied under the efficient strategy implies that policy makers––and by extension, taxpayers––are willing to spend whatever will be required to achieve the ecological objective, regardless of the cost or benefit. This is akin to committing to purchase a product prior to knowing its price or its value.
A brucellosis management strategy is efficient when the expected marginal benefits and expected marginal costs of each action are equated, where costs and benefits reflect the dynamic impacts to the coupled system (Clark, 2005).13 It is insufficient to simply consider the disease control impacts of the alternative disease control actions; the marginal costs and benefits associated with other impacts also need to be considered. For example, elk hunting can be implemented at a cost (e.g., travel, outfitting and time costs) to reduce transmission via reduced elk densities, thereby reducing disease liabilities (a benefit); however, hunting also involves several longer-run (intertemporal) costs associated with removing an animal: the animal is no longer available for future use and enjoyment or for reproduction (an ecosystem service). As epidemiological risks differ over time and across the GYA, so do the costs and benefits of certain actions. This means the various management actions will need to be targeted across space and time. Other things being equal, it is efficient to implement more disease-control efforts in areas or across time periods with low expected marginal costs and large expected marginal benefits.
Undertaking multiple actions generally promotes an efficient reduction in risks, in part due to the principle of diminishing marginal returns. This principle indicates that as more effort is applied to a particular activity, the marginal net benefits generally diminish (Varian, 1993). For instance, hunting to reduce elk densities may initially be an efficient approach for reducing elk transmission risk due to the significant reduction in risk for each extra unit of hunting. However, as hunting efforts are expanded to further reduce risks, the additional net gains become smaller and this approach most likely becomes less attractive relative to alternative efforts such as reduced feeding. At some point, it becomes more economicalto expand disease control investments to include additional activities, even if those other activities were initially less preferable. Thus, efficiency is enhanced when each additional dollar spent on disease control is applied to the activity that yields the greatest additional or marginal expected net benefits.
The epidemiological benefits from undertaking multiple actions to reduce disease transmission can be characterized by impacts to disease management thresholds, commonly used by disease ecologists to inform management (Roberts and Heesterbeek, 2003). A disease management threshold is a population threshold associated with a single management action to reduce disease prevalence. For instance, a vaccination threshold represents the minimum percentage of the herd that needs to be vaccinated to reduce prevalence (Roberts and Heesterbeek, 2003). Alternatively, a host-density threshold defines the threshold animal density that will need to be attained via harvesting before prevalence starts to decline (Heesterbeek and Roberts, 1995). It may be expensive to alter populations to satisfy a fixed threshold condition. However, disease
12 Eradication is only efficient if the additional benefits of eliminating thelast infected animal exceed the additional costs (e.g., McInerney et al., 1992). This may not be possible if there is inter-species transmission (Bicknell et al., 1999) or there are reintroduction risks (Horan and Fenichel, 2007). Perhaps recognizing this, the National Bovine Brucellosis Surveillance Plan “reflects the shift in goals from disease eradication to detection of re-emergence and demonstrating disease-freestatus of U.S. domestic cattle and bison herd” (USDA-APHIS, 2012, p. 21).
13 Efficiency simply assumes thattheregulatory agency places equalweights on thewelfare accruing to thevarious stakeholder groups. Suppose instead that the agency places unequalweights on these welfare measures. In that case, marginalcosts and benefits associated with each stakeholder group are simply weighted to become marginalpolitical costs and benefits. The qualitative results described are largely unaffected by this difference, but the quantitative results would differ.
management thresholds may not be fixed. Undertaking additional actions may make it possible to shift a threshold in a manner such that the threshold condition becomes less stringent and hence less costly to satisfy. That is, one action may be seen as an investment in reducing the threshold associated with other actions. For instance, altering supplemental feeding practices to reduce transmission risks will generally shift the vaccination and host-density thresholds so that fewer vaccination and harvesting efforts are required to reduce prevalence (Fenichel and Horan, 2007b). Thresholds associated with one ecological variable can also change over time in response to changes in other ecological variables, particularly in spatial, multispecies systems such as the GYA. For instance, current efforts to reduce brucellosis transmission within one elk herd may improve future brucellosis thresholds in neighboring elk and bison herds. This means managers can take steps now to effectively invest in reducing future threshold values (Fenichel et al., 2010). The bioeconomic approach therefore involves managing both populations and thresholds through combinations of investments based on an evaluation of the associated costs and benefits.
Just as it is efficient to target multiple actions, it is also efficient to target economic risks at multiple levels (Baumol and Oates, 1988). Economic risk, which involves both infection probabilities and the economic outcomes of infection, can be managed by targeting efforts toward risk mitigation and adaptation (Perrings, 2005). Considering wildlife to be the source of brucellosis risk to domestic cattle and bison, risk mitigation involves reducing disease transmission among wildlife and potential exposure of healthy cattle to infection. Adaptation in this setting involves reducing expected economic impacts arising once domestic animals in the disease surveillance area (DSA) become infected, which could include reducing both DSA losses and the likelihood that infected cattle are moved outside the DSA (thereby reducing expected cattle losses outside the DSA). Efficiency generally requires a combination of mitigation and adaptation efforts that are chosen based on the marginal benefits and marginal costs of each activity (Perrings, 2005).
3.1 Risk Mitigation for Wildlife Populations
Mitigating brucellosis in wildlife involves choosing which species to target and which controls to use, and these choices may vary by location and over time. The quantitative analysis required to make these choices has not yet been developed; the committee therefore proceeds with only a qualitative discussion. Elk are currently recognized as the primary source of recent brucellosis infections in cattle (USFWS and NPS, 2007). This suggests that the largest expected marginal gains might initially come from targeting elk, although this does not mean bison populations can be ignored; it would likely be efficient to direct some efforts at bison populations that generate risks.
Disease-control measures that are better targeted at reducing disease transmission tend to be more effective, yielding greater marginal benefits while also generating fewer adverse and costly ecological impacts. Examples of actions that selectively target brucellosis transmission in elk include vaccination, fetus removal, and test and removal (others are discussed elsewhere in Chapters 7 and 10). Vaccination has long been viewed as the holy grail of elk brucellosis management, but an effective vaccine for elk does not yet exist and may not exist for some time (USFW and NPS, 2007; Maichak et al., 2017). Other actions that selectively target transmission show promise, but the associated direct costs might be excessive. Removing fetuses quickly after an abortion event is likely to be costly except at areas of high animal concentration (such as feedgrounds, where this activity may represent an effective and low-cost option). Test and removal of wildlife is also potentially costly, although it would be less so in areas of high animal densities (e.g., elk feedgrounds and where bison exit YNP for the winter). The Wyoming Brucellosis Coordination Team (WBCT) (2005) recommended a pilot project targeting test-and-removal efforts to high-risk female elk, but this strategy was not adopted (USFWS and NPS, 2007; NER and USFWS, 2014). Test and removal of elk on feedgrounds was estimated to be extremely costly relative to elk vaccination and low-density feeding (Boroff et al., 2016).
Alternative supplemental feeding practices can be adopted to reduce elk densities (low-density feeding that spreads out feed over a larger area) or to reduce the likelihood of contacting an aborted fetus (elevated feeding). These practices are targeted in the sense that they directly affect transmission without adversely affecting heathy animals. Low-density feeding costs may be of similar or slightly greater magnitude as vaccination costs (Boroff et al., 2016). However, as low-density feeding is more effective than vaccination, it is likely that low-density feeding costs less although no cost information was available for elevated feeding (Boroff et al., 2016).14
Highly targeted approaches tend to be costly. The greater the overall marginal costs of highly targeted approaches relative to the expected marginal benefits, the more desirable it may become to adopt an imperfectly targeted approach, such as reduced supplemental feeding and increased harvesting and hunting activities. These activities are imperfectly targeted because they are nonselective as they impact both infected and non-infected animals without regard to an animal’s health status. Since nonselective actions adversely impact healthy animals more when prevalence is low, these measures are more likely to generate ancillary costs that go beyond the direct expenditures for these actions. These measures are also imperfectly targeted in a spatial sense, as control efforts in one area may spur herd movements that adversely affect infection risks.
Reducing Supplemental Feeding
Feedgrounds probably support a larger elk population by reducing winter mortality for elk, but they also increase disease transmission among elk by encouraging elk to congregate. Reducing supplemental feeding is therefore likely to reduce elk seroprevalence over time, thereby generating disease control benefits. However, as supplemental feeding (as well as other changes in land use) affect infected and healthy animals nonselectively, reduced feeding may adversely impact those who value larger elk populations, such as hunters and wildlife viewers. In contrast, conservationists might value reduced feeding because it implies more natural herds, even though they are of smaller magnitude.
A reduction in supplemental feeding at a particular location also has nontargeted spatial implications, as herd movement in response to reduced feeding may generate new risks. Specifically, supplemental feeding benefits ranchers by encouraging elk to stay away from ranches and susceptible cattle. This latter effect reduces disease exposure to cattle, as elk substitute one habitat (feedgrounds) for another (private ranches). Thus, a reduction in supplemental feeding poses significant short-term risk to cattle due to the potential increase in exposure as more elk move onto private ranches. But reducing supplemental feeding may create longer-term benefits of reduced transmission, stemming from fewer and less dense elk populations. The efficiency of reducing the magnitude of feeding operations depends on whether the long-term benefits outweigh the short-term costs.
Current strategies recommend continuing supplemental feeding in the near term to reduce exposure to cattle while also investing in native forage so that supplemental feeding can eventually be reduced (USFWS and NPS, 2007; NER and USFWS, 2014). Prior bioeconomic analysis on supplemental feeding in nonspatial settings has found optimal feeding levels to vary inversely to local disease risks, with periodic (not permanent) cessation of feeding for sufficiently high-risk periods (Fenichel and Horan, 2007b; Horan et al., 2008). These results imply that permanently closing all GYA feedgrounds, as some have suggested, may be inefficient.
14Boroff and colleagues (2016) conducted an analysis that compares some costs and benefits of various elk management practices on feedgrounds. However, care should betaken not to attributeany efficiency consequences to their results. That analysis is not based on a model of a dynamic coupled system, and only some costs (direct costs of the management practice) and benefits (reduced impacts to cattle producers) are considered. Moreover, the preferred strategy is not determined from a marginal analysis, but rather stems from comparing thetotal net benefits of several predetermined scenarios.
Reducing population levels within high-prevalence herds could reduce the potential for infectious contacts with cattle simply because there would be fewer wildlife present. A sufficient reduction in density can also lower disease prevalence by reducing infectious contacts among wildlife, but only if disease transmission is density dependent––as is the case for elk but not bison. As previously discussed, non-selective population controls imply economic impacts beyond disease control.
The efficiency of population controls is increased via spatial targeting to address brucellosis transmission within high-risk populations and from these populations to cattle. For instance, spatial control may influence elk densities on public grazing areas or the number of elk seeking refuge from hunting on private lands where hunting is not allowed. The latter is a growing concern given the growth in privately owned land in the GYA (Schumaker et al., 2012). Spatial controls may also reduce the risks of disease spread. For instance, northern movement of bison out of YNP is density dependent; reducing herd density can reduce the amount of migration and the distance traveled. Elk and bison populations in YNP are managed spatially, but for reasons other than disease control (USFWS and NPR, 2007; NER and USFWS, 2014). This is despite the WBCT’s recommendation that bison population controls be based on disease prevalence (WBCT, 2005).
Managing Risk Outside the Designated Surveillance Areas
An important spatial consideration is the efficient allocation of risk mitigation efforts toward managing risks within the current DSA versus preventing expansion of the DSA (e.g., by reducing elk densities along the DSA border and in neighboring areas). Prior work on invasive species management suggests that, at the margin, it may be efficient to invest more in preventing expansion than in reducing current risks (Leung et al., 2002). While quantitative analysis would be required to confirm this for the GYA, it would be useful to ask whether one more infected ranch within the current DSA is likely to be more or less costly than an infected ranch outside of the DSA. Intuition suggests that once mitigation and adaptation efforts are already in place to address risks inside the DSA, an additional infected ranch is unlikely to be as costly as a similar ranch outside the current DSA where no adaptation mechanisms are in place. This could be particularly true if a larger infected area becomes increasingly difficult to manage and significantly increases the likelihood of broader-scale trade sanctions, which is a major concern (USDA-APHIS, 2012).
Benefits Beyond Cattle Protection
Although cattle are the major concern, there are benefits to protecting other species from brucellosis. Protecting elk and bison from brucellosis could generate significant benefits to the extent that this protection enhances ecological productivity or would be valued by hunters, wildlife viewers, and conservationists (e.g., Horan and Melstrom, 2011). Reducing prevalence among bison might generate larger marginal values simply because bison are less abundant than elk and are considered an important symbol of the American west. Measures taken to protect wildlife from brucellosis may also generate benefits that go beyond the current brucellosis problem. For instance, reducing animal contact rates to reduce the spread of brucellosis may also protect against other potential diseases, such as chronic wasting disease (CWD) in elk. Efforts to improve the overall health of elk and bison herds may also make these animals more resilient to other current and future impacts (e.g., climate change and other diseases).
3.2 Risk Mitigation by Ranchers
Risk mitigation efforts are not confined to wildlife populations; efforts can also be undertaken on ranches to mitigate risks that wildlife pose to cattle. These efforts involve preventing infectious contacts from wildlife to cattle, either by reducing exposure of cattle to potentially infectious wildlife or by reducing susceptibility of cattle to infection. Efficient brucellosis mitigation on GYA ranches involves choosing
which controls to use, recognizing that these choices may vary by location and over time. As with mitigation efforts targeted at wildlife, it will likely be efficient to adopt a suite of controls, including efforts to reduce both exposure and susceptibility (Roberts et al., 2012), and to vary these controls spatially and temporally based on risks. The quantitative analysis required to make these choices has not yet been developed; therefore, the committee provides only a qualitative discussion.
Vaccination is the only method for reducing susceptibility. Vaccines are significantly more effective in cattle than in wildlife, although they are still imperfect and a relatively costly business expense for ranchers who operate in highly competitive cattle markets. Producers will not provide booster vaccination of adult cattle unless this is subsidized (Roberts et al., 2012). But even then, existing vaccines may not be an economical first line of defense, due to the relatively high cost and limited effectiveness as compared to other approaches (Roberts et al., 2012).
Exposure of cattle to wildlife risks may be reduced by a variety of biosecurity actions, including hazing of elk, fencing haystacks, spaying heifer calves, modified winter feeding schedules for cattle, riding through cattle herds to prevent cattle-elk contacts, and delaying grazing on public lands. A break-even analysis of these practices was conducted for GYA ranches to provide insight into practices that are likely to be more expensive (Roberts et al., 2012), although care should be taken not to attribute any efficiency consequences to these results.15 Hazing of elk and fencing haystacks are likely to be the cheapest approaches, followed by vaccination and a modified winter feeding schedule (Roberts et al., 2012). In contrast, delayed grazing appears to be the most expensive approach (Roberts et al., 2012). This ranking of alternatives only includes direct expenditures. However, as some ranchers have outfitting businesses that benefit from the presence of elk, there may be additional costs associated with biosecurity practices, such as hazing and fencing haystacks, that limit elk densities on ranches.
It is inappropriate to compare one study on the costs of elk risk mitigation efforts (Boroff et al., 2016) with another study on the costs of cattle risk mitigation efforts (Roberts et al., 2012) because the actions are implemented at different scales and produce very different types of benefits. There may be reasons why actions under either approach may be desirable. For instance, mitigation efforts targeted at wildlife may protect more cattle herds than on-farm biosecurity efforts, which may only protect a single herd. On-farm biosecurity efforts have the benefit of being a more direct, and therefore potentially more effective, means of protecting cattle, and they likely result in fewer trade-offs with hunting, wildlife viewing, and conservation than measures targeting wildlife disease transmission. However, the large geographic sizes of ranches in the GYA and the large herd sizes on these ranches could reduce the efficiency of biosecurity practices (Hennessy, 2007a,b).
Risk mitigation efforts can also be undertaken on GYA ranches to reduce the risk of brucellosis spreading across GYA cattle herds, in the case that one or more herds become infected (e.g., keeping cattle separated in grazing areas and along fence lines). The committee is not aware of any economic analysis of this issue for the GYA.
It is worth emphasizing that spatially differentiated on-farm mitigation efforts are likely to be efficient because the GYA is ecologically and economically heterogeneous. For instance, ranch location may affect the efficiency of biosecurity, as epidemiological risks differ across the GYA. Also, ranch location and herd size play a role in determining grazing decisions. Larger herds will be more expensive to move long distances and are likely to be at greater risk on any given allotment than smaller herds due to more opportunities for infectious contacts with aborted fetal material. Herd size and ranch location thus factor into tradeoffs involving the travel costs and risks of different grazing opportunities.
15Roberts and colleagues (2012) investigate a number of scenarios involving fixed, predetermined effort levels applied to various practices. Their break-even analysis illustrates when a producer might find a particular scenario to be worthwhile (i.e., to yield positive net benefits to the producer), which is quite different from a marginal analysis that aims to identify an efficient mix of efforts (i.e., to yield maximum net benefits to society). Their analysis is also not based on a model of a dynamic coupled systeminvolving spatially heterogeneous risks, for which a one-size-fits-all approach is unlikely to apply.
3.3 Risk Adaptation by Ranchers
Economic risks to the cattle sector can also be reduced via adaptation––that is, by reducing any economic losses that are likely to arise if a herd becomes infected. There can be no risk to cattle if infection does not generate economic losses. Reducing brucellosis-related costs may not only benefit the cattle industry: with fewer economic damages from brucellosis, there will be less of a need to manage brucellosis in the wild. This means the stringency of brucellosis controls for wildlife could be reduced relative to the case of no adaptation, at least for the interior of the GYA, thereby generating additional cost savings. More specifically, with fewer risks to GYA cattle, it would be relatively more efficient to target wildlife to prevent the spatial spread of the disease (i.e., prevent the expansion of the DSA) rather than protect producers in the current DSA.
Compensation schemes that reimburse ranchers for losses are not adaptation mechanisms. Compensation simply involves shifting the risk from one stakeholder group (ranchers) to another (the public sector, or taxpayers), while not actually reducing the level of risk facing society. Compensation programs can actually increase risks in some instances (see Chapter 7 and section 4.2 of this chapter).
One adaptation approach for reducing brucellosis-related losses is to alter the market(s) in which producers participate. Currently, the predominant cattle operation in the GYA is the cow/calf-yearling operation (Ruff et al., 2016), which involves maintaining a breeding herd that faces continual brucellosis risks. An alternative to this is a stocker operation that purchases weaned calves and grazes them before selling them to a feedlot. Stocker operations do not involve a breeding herd, and so they would be free of brucellosis risks once they have fully transitioned to the new operation. However, stocker operations are more costly to run in the DSA relative to the Midwest, putting DSA stocker operations at a competitive disadvantage. Increased costs stem from the harsh GYA climate and a comparative lack of corn, water, and other key resources more readily available in the Midwest, along with excess feedlot and slaughter capacity in the Midwest (Allen, 2014; Meatingplace, 2014). The lack of packing and processing sector infrastructure (e.g., slaughterhouses) necessary to produce finished beef products in the GYA means that large finished animals would have to be shipped far from the GYA for processing, which is more expensive than the current practice of shipping younger and lighter cattle. The construction of a local slaughterhouse facility would reduce transportation costs, but not finishing costs.
GYA ranchers would face greater financial risks under a stocker operation, but these could be offset by reduced brucellosis risks (Ruff et al., 2014, 2016). Ranchers with a sufficiently large rate of time preference, or discount rate, might find it privately optimal to switch to a stocker operation (Ruff et al., 2014, 2016), but this only takes private market costs and benefits into account. Private nonmarket values are also relevant and could impact the decision in either direction.16 If external social benefits of reduced brucellosis risk are also taken into account, then it might be efficient for many producers to switch to a stocker operation. In this case, such a transition might have to be incentivized via subsidy.
For some locations, it also makes sense to consider an alternative land use that does not involve cattle. The land use with the greatest expected social net benefits (taking into consideration any nonmarket values for ranchers to remain in the cattle industry) ought to be encouraged, perhaps through an incentive program. In any case, it would not be surprising for a quantitative analysis to indicate that having fewer cow/calf operations is likely to be efficient. This type of finding is broadly applicable to environmental risk-management problems (Baumol and Oates, 1988).
Another proposed way to alter the market(s) in which producers participate would be to develop local markets that would pay a premium for GYA beef. Such an approach is seen as a way of increasing profitability and enabling traditional cattle operations to stay within the GYA. Research on locational branding (Tonsor et al., 2013) finds that U.S. consumers may be unwilling to pay extra for beef produced in the United States relative to other parts of North America, but it is unknown whether visitors to the GYA might be willing to pay more for GYA beef. An alternative branding option might be to promote local beef as
16 For instance, altruistic/conservationist values could lead ranchers to switch to a stocker operation in order to help reduce brucellosis risks, whereas heritage values might have theopposite effect.
improving GYA sustainability. Regardless of how branding might be developed, it is unclear whether local demand would be sufficient to support GYA producers.
A second approach to reducing brucellosis-related losses is to ensure that cattle markets do not overly penalize non-infected GYA herds when one or a few GYA herds become infected. For instance, there have been recent calls elsewhere for relaxing costly trade restrictions in response to infectious livestock disease while adopting scientifically sound risk-management approaches (ECCFMD, 2011; MDARD, 2011; DEFRA, 2014). One potentially efficient approach is to improve signaling about herd health—beyond individual testing—to facilitate trade while reducing movement risks (Hennessy et al., 2005; Horan et al., 2015). Specifically, the concept of “risk-based trading” alerts importers of the exporting herd’s health history and enables livestock trades within endemic areas, which would spur private risk-management actions by buyers and sellers (DEFRA, 2014). The European Union’s Progressive Control Pathway suggests a similar approach, including targeted movement controls that depend on how risks differ (probabilities of transmission; economic consequences of transmission) between and within infected areas where movement might occur (ECCFMD, 2011).
3.4 Mitigating Risks to Importers of GYA Cattle
Risk mitigation and adaptation efforts can also be implemented by importers of GYA cattle. For instance, risks to importers may be reduced by informing importers of herds’ health histories, testing animals prior to movement, requiring post-movement quarantines of animals from high-risk areas, and restricting animal movements from certain high-risk areas. Current DSA regulations require a number of these types of restrictions, although the economic efficiency of these requirements has not been examined.
The bioeconomic decision framework can be used to identify desirable actions on both public and private lands (where desirability is defined according to the agencies’ preferences over those of various stakeholder groups). Agencies can implement many actions on public lands directly, such as supplemental feeding for elk. However, policy mechanisms are generally required to promote an agency’s preferred disease management strategies when private individuals are involved. This includes hunters on public and private lands and ranchers on private lands and public grazing areas––at least when these individuals choose risk mitigation and adaptation strategies that are inconsistent with an agency’s preferred strategy.
The following policy design discussion is framed around two important simplifications to facilitate understanding. First, policy development is described as if a single coordinating agency were responsible. Agency coordination is a challenge and is further discussed in section 4.3. Second, policy development targets the objective of efficiency. As described earlier, agencies may have other objectives, and various policy choices may affect distributional or equity outcomes that agencies might value.
4.1 The Need for Policy
Policies to promote risk mitigation and adaptation, collectively referred to as self-protection, may be needed even if hunters and ranchers already have some incentives for these measures based on their perceived benefits. For instance, hunters may choose to avoid high-prevalence elk herds rather than to engage in efforts to reduce the densities of these herds. Similarly, while ranchers have strong incentives to consider the private costs and benefits of self-protection, they will typically underinvest (relative to efficient levels) in self-protection activities that generate positive benefits to others (Baumol and Oates, 1988; Hanley et al., 1997). The private costs may outweigh the private benefits of many risk-management activities (Roberts et al., 2012), although these actions may also benefit society. The result of this under-investment is greater risk and lower social economic welfare. Public policies can provide the required impetus for ranchers to adopt more socially efficient levels of risk management.
4.2 Policy Design Choices
Two questions need to be addressed in designing policies that encourage private individuals to engage in a particular disease-controlstrategy (Hanley et al., 1997; Shortle et al., 1998): first, what intended actions or outcomes will serve as the basis or compliance measure for policy mechanisms; second, what specific mechanisms will be used (e.g., economic incentives or a regulatory mandate, and the levels of these mechanisms)?
First Design Choice: The Compliance Measure
A compliance measure is simply an indicator of when sufficient efforts have been applied to satisfy a regulation, or when incentive payments are altered in response to change in efforts. Compliance measures may be defined in terms of particular actions, such as herd vaccination rates, which may be set by mandate or incentivized by subsidizing each unit (e.g., percentage) of the herd that is vaccinated. Alternatively, compliance measures may be defined in terms of some measure of actual or expected performance or outcome resulting from risk-management actions. An example of an actual performance outcome is the number of infected animals that are traced back to a particular ranch; a performance-based policy in this situation might be a tax per infected animal that is traced back to the herd. An example of expected performance is a modeled estimate of the probability a rancher’s herd becomes infected and then spreads infection to other producers, conditional on the ranchers’ risk-management actions; a performance-based subsidy might reward producers for reducing this modeled probability. In the present context, performance-based approaches are likely to be relevant only for ranchers, as it is difficult to imagine a performance-based approach that may be usefully applied to hunters. Risk models would have to be developed to calculate how various practices can reduce transmission probabilities, and then be made available to individual ranchers so that they can determine the risk impacts of their choices. The need to develop quantitative risk models does not need to be viewed as a drawback, because such models are already needed as a basis for any sound risk-management strategy. There is precedent for using risk modeling to implement performance-based approaches for other environmentalproblems where actualimpacts are difficult to measure, including modeled estimates of nonpoint source nutrient pollution from agricultural fields (EPA, 2014) and the modeled probability that a ship introduces invasive species via its ballast water discharge (Karaminas et al., 2000).
In theory, policies based on actions or performance can work for ranchers; but, in practice, there are trade-offs associated with implementation. A policy instrument based on a single observable action would be the easiest to implement and could provide the clearest incentives because it is directly tied to a specific choice. However, many choices might have to be regulated or incentivized to produce the desired effect, and it may be difficult to ensure that each choice is properly regulated or incentivized, especially if there are considerable uncertainties over the parameters used to determine regulation or incentive levels. Errors in setting policy variables will result in individuals over- or underinvesting in the various risk-management actions, with these errors potentially becoming compounded when separate policy variables are applied to many choices.
Basing policies on expected performance does not reduce monitoring requirements (as it remains necessary to monitor the relevant choices that are provided as input into the predictive risk model), but it does reduce the number of policy variables, potentially reducing administrative costs and compounding errors. This approach is also advantageous because it gives private individuals the flexibility to decide how best to carry out risk-management activities. That is, a performance-based approach encourages individuals to improve their performance in the most efficient way possible by using their private knowledge of the costs for making decisions. The drawback of this approach is that individuals may have difficulty using the risk model to properly evaluate the impacts of their decisions.
Second Design Choice: Specific Policy Mechanisms
The second design choice is determining the mechanisms for inducing behavioral changes. Regulations (such as standards that limit risky behaviors) and economic incentives (such as taxes on risk-increasing behaviors and subsidies on risk-reducing behaviors) are the primary options. Regulatory standards simply mandate particular behaviors or outcomes consistent with the agency’s preferred strategy. In contrast, individuals facing incentives retain the flexibility to make their own decisions, although the incentives influence these decisions. Specifically, efficient tax or subsidy rates tied to an activity affecting disease risks would be set equal to the expected external economic impacts of the activity in the efficient outcome. These incentive rates then act as prices that cause individuals to consider the expected external social costs or benefits associated with their choices (Baumol and Oates, 1988). Education programs can play valuable supportive roles but improved knowledge about external benefits is generally inadequate to induce sufficient adoption of costly actions (Ribaudo and Horan, 1999; Horan et al., 2001; Shortle et al., 2012).
Grazing fees represent an example of incentives akin to a tax. Federal grazing fees are currently applied uniformly across the landscape without regard to brucellosis risks (Rimbey and Torell, 2011). By charging larger fees to access higher-risk grazing allotments, this would discourage and reduce access to higher-risk areas while not eliminating this opportunity for ranchers who believe it would be profitable. There is precedent to make grazing allotments dependent on disease risk, as the Bureau of Land Management (BLM) has recently made policy changes designed to separate bighorn and domestic sheep (BLM, 2016). Similar risk prevention policies on USFS- and BLM-administered grazing lands could be effective by encouraging the separation of cattle from elk during the period from late April through late June when most brucellosis-related abortions in elk occur.
Subsidies are often used to positively encourage landowners to change production or land uses on certain sites. For instance, subsidies could be used to encourage landowners to haze elk off their properties, to give hunters more access to private lands, or to implement their own wildlife population controls on private lands. This issue is particularly important given the growth in privately owned land in the GYA where elk may seek refuge from hunting (Schumaker et al., 2012). Economic studies are needed to explore what incentives would be required for landowners to participate in such disease control programs. Certain high-risk, low-profit producers could also be subsidized to cover the costs of permanently switching to alternative livestock or land use activities that do not involve the risk of brucellosis. Another option would be to buy out these producers, but that could be less beneficial to society than encouraging the land to be placed in an alternative, productive use.
Regulatory standards applied to an activity make the most sense when it is in society’s best interest to prohibit flexibility on the effort level applied to that activity (in contrast to pricing mechanisms, which allow flexibility). A situation involving such an outcome is when the expected social benefits of a desirable change in effort (e.g., applying less effort to a risk-increasing activity, or applying more effort to a risk-reducing activity) always exceed the expected social costs of this change (Shortle and Abler, 1997).17 Examples involving risk-reducing efforts might include mandating efforts to prohibit contact among risky animals, such as regulations for quarantining animals and for always hazing elk away from cattle herds. An example involving a risk-increasing effort is to prohibit grazing in high-risk areas at high-risk times. In the case of grazing, this would require only simple modifications to the regulatory approach of a fixed entry
17 Such an outcome, which is referred to as a corner solution, involves a wedge between expected marginal social benefits and expected marginal social costs. This wedge means that even a small change in effort can be highly costly. Therefore, if prices were to be applied in this setting, it might be in society’s best interest to set them large enough that there would be little chance of producers deviating from the desired outcome. Note that the examples provided in the text involve cases whereproducers can vary effort levels. There might also be somewhat extreme cases where an activity is discretely defined, such that one either adopts a technology or does not (i.e., a particular producer cannot choose a degree of adoption). Regulations, possibly in conjunction with a cost-sharing component, might also be preferred in such situations to ensurethat the correct technology is chosen.
date for federal grazing that currently makes no consideration of brucellosis risks (Rimbey and Torell, 2011). Delaying access for grazing until after the elk birthing period would reduce risk.
Hunting permits represent a standard once the permits are distributed, although permit fees are essentially an incentive-based pricing mechanism (a tax) that allocates permits according to individuals’ values for them. Optimally, permits (if distributed freely) or their prices would be defined spatially and temporally to better manage spatial and temporal risks. Permit levels might be increased, or permit prices decreased, in high-risk areas to encourage reductions in wildlife density. In cases where free distribution of permits in high-risk areas might generate insufficient hunting pressure, hunters would have to be paid to hunt or else resource managers would have to implement population controls directly.
To help ranchers endure disease risks, compensation for disease risks and income enhancement mechanisms are often advocated (e.g., WBCT, 2005). These approaches deserve special mention because they generally do not promote disease risk management and can instead generate perverse risk-management incentives. For instance, USDA’s livestock indemnification program pays ranchers the fair market value of infected animals (Hoag et al., 2006; USDA-APHIS, 2016). This program may not fully compensate ranchers for all costs (e.g., business interruption, feeding and care costs for animals, and loss of markets), but it does significantly reduce a producer’s losses. Fewer losses, combined with the fact that producers may not have to take special preventive actions to qualify for these payments, means that indemnities can reduce the expected costs of becoming infected. Consequently, ranchers have fewer incentives to protect their herds from infection (Hennessy et al., 2005; Muhammad and Jones, 2008; Gramig and Horan, 2011), potentially resulting in many producers operating in a risky environment (Baumol and Oates, 1988). Both of these features increase the overall disease risks to society and reduce expected social welfare.
Indemnity programs can be modified to address this problem. For instance, indemnities could be made contingent either on adopting certain observable biosecurity practices (Reeling and Horan, 2015) or on evidence of biosecurity measures being adopted (Gramig and Horan, 2011). An example of this concept is USDA’s recent adjustments to highly pathogenic avian influenza indemnity payments to poultry producers (USDA-APHIS, 2016). Such approaches force agents to bear some risk of losses who otherwise would insufficiently invest in biosecurity, which ultimately incentivizes them to invest in mitigation. The conditions for indemnification can also be developed so that their biosecurity investments protect others in case their herd does become infected (Reeling and Horan, 2015).
Insurance programs to cover non-indemnified losses caused by disease are theoretically possible, but currently they do not exist (Grannis et al., 2004). Examples of related insurance programs include USDA’s Livestock Risk Protection program (USDA, 2014) and USDA’s recently developed Rainfall Index Pasture, Rangeland, Forage (PRF) pilot program (USDA, 2015). As with indemnities, insurance programs could reduce ranchers’ risk-management incentives unless payments or premiums are tied to producer behavior. For instance, premiums might be subsidized for producers who provide evidence of investing in self-protection to reduce brucellosis risks. While an insurance program holds merit conceptually, there are a host of challenges and knowledge gaps. There will need to be sufficient interest by producers for such a program to be viable, since premiums are required to fund payouts. Also, livestock producers often implement fewer-than-expected risk management efforts despite extensive knowledge about the practices (Goodwin and Schroeder, 1994; Pennings and Garcia, 2001; Wolf and Widmar, 2014). This may limit the ability of program managers to modify insurance (and indemnity) programs to create risk-management incentives. There are situations where insurance works well and when it does not (Goodwin and Smith, 2013; Reeling and Horan, 2015), and whether these mechanisms might work well in the GYA is an empirical question that has yet to be addressed.
Multiple mechanisms (taxes, subsidies, or regulations) may be capable of encouraging any particular risk-management action, but the mechanisms differ in terms of their equity impacts––that is, how the economic costs and benefits are distributed. For instance, subsidies involve private individuals being compensated by taxpayers for reducing social risks. Standards and taxes require private individuals to pay the costs of risk reduction activities (unless financial assistance is offered, in which case taxpayers share the burden). Additionally, taxes require the risk-generating agents to pay more and are politically controversial. However, taxes generate funds that could be used to offset the costs of other government activities, either now or in the future, to benefit current or future taxpayers. For instance, tax receipts could be used to fund risk-management activities by various agencies, or they could be used to fund subsidy programs to further reduce risk. Tax receipts could also be used to reallocate welfare within the GYA in a manner that is not tied to risk management––that is, as a lump sum payment not based on disease-related costs (and therefore not a compensation scheme).
Multiple policy mechanisms could lead to the same efficient outcome while distributing economic costs and benefits differently among stakeholders. This means that addressing equity concerns does not necessarily have to come at the expense of economic efficiency. Initial attempts to address equity concerns may therefore involve selecting the right combination of instruments. Further attempts to fine-tune the distribution of economic outcomes among stakeholders might be pursued using lump sum transfer payments that do not influence behaviors (e.g., redistributing a portion of grazing fees or hunting permit fees to all ranchers, regardless of risk-management decisions or infection outcomes). Once these two options have been exhausted, any further attempts to address equity concerns will have efficiency reducing consequences.
4.3 Agency Coordination
Many agencies are involved in managing brucellosis in the GYA, with each agency having its own mission and objectives along with its own stakeholder groups (e.g., NER and USFWS, 2014; IBMP, 2015). Even if all agencies agreed that efficient disease control is a desirable objective, agencies may be unable to coordinate their efforts because each may have other objectives and interests that supersede the level of effort and commitment needed to efficiently and effectively address brucellosis. A lack of coordination can result in limited efforts to manage brucellosis risks as well as limited collection and sharing of information that can improve opportunities for adaptive management.
One reason for coordination failure is that the agencies and associated stakeholder groups who gain most from disease control (e.g., USDA and ranchers) may not be the ones incurring the most costs (e.g., park and wildlife managers, hunters, conservationists, and park visitors). While it may be possible to negotiate a limited degree of coordination in this setting, agencies are unlikely to commit significant resources of their own or of their stakeholders when the benefits largely accrue to others. Promoting broader and more intensive coordination will generally require mechanisms that transfer wealth from agencies and stakeholders who gain from disease control to the agencies and stakeholders who bear the costs from disease control (Ostrom, 1990). Such an institutional arrangement would appear quite different from current collaborative approaches. For instance, the IBMP represents a collaborative strategy for bison negotiated by agencies with different incentives, with the execution and costs dependent on each agency rather than determined by a single organization considering the best interests of society as a whole. Devising mechanisms to share costs and benefits in ways that promote agency coordination is a major topic when developing international environmental agreements (especially those dealing with climate change [Barrett, 2003]) and when developing international defensive collaborations (such as NATO [Sandler, 1977]), and those lessons from other areas could be valuable for promoting coordination among agencies when equity appears to be a concern. In an adaptive management framework, opportunities for coordination are likely to be enhanced when wealth transfers are based on performance outcomes (e.g., reduced elk prevalence in an area) rather than on action-based outcomes (e.g., percent of elk vaccinated in the area) that may appear good in principle but
may not produce the desired effect. It will be hard to sustain coordination if the coordinated strategy does not sufficiently improve the situation.
Managing brucellosis in the GYA is unlikely to yield significant improvements unless the complex social dimensions of the problem are addressed. This will require evaluating trade-offs associated with many disparate interests, promoting cooperation among various regulatory agencies who oversee different facets of the GYA system, and using monetary and other resources wisely to reduce risks. This may involve altering the behaviors of individuals who interact with wildlife and livestock.
An economic valuation can be used to quantify the concerns of GYA stakeholders. A bioeconomic framework that treats the GYA as a coupled ecological-socioeconomic system can then be used to compute broadly defined, long-term costs and benefits associated with proposed brucellosis management strategies. This sort of simple cost-benefit analysis can help to evaluate proposed strategies. However, the power of the bioeconomic framework lies in its ability to be applied in a decision-making context to construct socially desirable strategies. In particular, a bioeconomic decision-making framework can help to identify economically efficient strategies that will generate the greatest net economic gains (benefits minus costs) to society as a whole, taking into account the many market and nonmarket values that our diverse society places on wildlife resources and cattle production both in the short run and the long run. Efficient strategies are likely to involve a variety of control measures that may be applied differentially over space and time to reflect variations in spatial-temporal risks and that target several types of risks to varying degrees: disease transmission among wildlife, cattle exposure to wildlife risks, and economic risks both in the GYA and beyond. Implementing disease control strategies requires coordination by a number of federal, state, and local agencies, which could be challenging because agencies and stakeholders who benefit from disease control are unlikely to be the ones bearing the costs. Mechanisms for sharing costs and benefits will be required for proper coordination and successful disease control.
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