4.0 Introduction

Economists sometimes point out that achieving a goal of zero pollution not only would be prohibitively expensive but also, indeed, might well be counterproductive. The view is that we should balance the costs and benefits of pollution reduction and seek, in general, to achieve an efficient level of pollution. The idea that any level of pollution is “efficient” strikes many people as a bit odd. This chapter thus begins by defining the efficient pollution level; then illustrates how marginal analysis can be used, both in principle and in practice, to identify the efficient pollution level; discusses the utilitarian ethical defense of the efficiency standard; and finally, reviews the challenges to real-world application of benefit–cost analysis.

4.1 Efficiency Defined

To understand what is meant by an efficient level of pollution, we need to look closer at the concept of efficiency. The term efficient in everyday parlance means a situation in which no resources are wasted. Economists use the term in a related but more specific way. The economic definition of efficiency was introduced by the Italian economist Vilfredo Pareto in 1909 and is named in his honor.

Pareto-efficient situation: A situation in which it is impossible to make one person better off without making anyone else worse off.

When economists say an outcome is efficient, they almost always mean “Pareto efficient.” We’ll drop the Pareto most of the time as well, adding the modifier only when we want to remind the reader that we are using this specific definition.

The advantage of pursuing efficiency is that, conditional on the existing distribution of income, it makes the “economic pie” as big as possible. In fact, at the efficient outcome, the net monetary benefits produced by the economy are maximized. This means that the total of all benefits that can be given a monetary value, both human-made and those generated by nature, minus all costs of production, both private and external, will be as large as possible at the efficient point. How do we know this? The answer: By applying the definition of efficiency. If it were possible to make someone better off without making someone else worse off (meaning that we are at an inefficient point), we could always make the economic pie of net benefits bigger by moving toward efficiency.

The first point to make about efficiency is that it need not be fair. This is illustrated clearly in Figure 4.1, which shows two economic pies. Pie 2 is more efficient than Pie 1 because it is bigger—it maximizes the benefits to society as a whole. Yet, Pie 2 is clearly much less fair than Pie 1. Both in absolute and relative terms, B is worse off with Pie 2. Thus, on its own, efficiency may not be useful as a guide to good social outcomes. (One of us recalls first learning about Pareto efficiency in my our microeconomics class and asking the teacher if it would be possible to have an efficient slave society. The answer was yes—freeing the slaves might not be “efficient” because, in monetary terms, the masters might lose more than the slaves would gain.)

Yet, whenever the economic pie is enlarged, it is at least possible for everyone to get a bigger slice in absolute terms. As shown in Figure 4.1, a move from Pie 1 to Pie 2 could in principle provide both A and B bigger slices. Thus, any move toward efficiency can in theory be a win-win situation. We have already considered one such case: By restricting fishing in New England, the government could generate enough resource rent to both compensate the fishers put out of business and allow the remaining boats to earn a decent living. Let us look at another such case involving the use of California’s scarce water resources.

California is a semiarid state with a vast agricultural industry and rapidly growing urban centers. Water is scarce, and an unlikely coalition of corporate farmers and environmentalists has supported moving to an open market in water rights to increase the efficient use of existing supplies.¹ Farmers seek a profit from the sale of their water, while environmentalists are interested in forestalling the construction of ecologically disruptive new dams in the state.

To simplify a complex story, farmland in California comes endowed with rights to a percentage of water from a given reservoir; currently, farmers use about 85 percent of the state’s water. The price for agricultural water charged by state and federal governments is much lower than that for metropolitan use: for example, the agricultural price may be $10 per acre-foot, while the urban price was $100 per acre-foot. This low price for agricultural water has resulted in such clearly inefficient but profitable uses as growing irrigated hay in arid regions. This practice is inefficient because it shrinks the size of California’s economic pie. The water could be used to produce output in other sectors of the economy with a higher monetary value—other less-water-intensive crops, industrial products, or perhaps even green lawns (which raise the value of homes) in metropolitan areas.

We can analyze the situation by assuming the existence of two markets for water in California: agricultural and metropolitan, with no transfer in between. This situation is illustrated in Figure 4.2A. The state could move toward efficiency by combining the markets into one, as illustrated in Figure 4.2B, thus generating a single price of around $70.

One way to achieve this goal would be for the government bodies selling the water simply to raise the price to its market level, in this example, $70. One statewide initiative calling for a similar approach was defeated, not surprisingly, with heavy opposition from farm interests. Such an effort would clearly be efficient as the water not bought by farmers would be freed up for metropolitan use. However, it was thought to be unfair in the way it penalized farmers by changing the rules of the game midstream.

Yet, as is always the case when moving toward efficiency, a Pareto-improving alternative policy exists—one that actually does make everyone better off. In California, farmers could continue to purchase their old allotments at $10 but be allowed to resell them without restriction to the urban sector. In this case, a farmer could continue to grow his/her hay but, by doing so, would be passing up substantial profit opportunities. By simply reselling his/her water, he/she could make $90 per acre-foot! Under this policy, farmers would gradually abandon inefficient farming practices and become “water tycoons.” Reforms of this nature are now being sought in California.²

Either policy—a single price or subsidized prices with water marketing—would put a lot of farmers out of the farming business, and the California economy would shift toward greater production in the industrial and service sectors. Overall, economists would predict that the monetary value of California production would rise as water flowed into higher value uses. Thus, both policies are efficient, though the first one is unfair in many ways. (It is worth noting that both policies would also encourage urban growth in California; many people consider such growth a problem in itself. Are green lawns in the suburban desert really an “efficient” use of water—that is, one that makes people happier overall? We take a closer look at the complex relationship between growth and social welfare later, in Chapter 11.)

One reason that why economists like efficient outcomes is that, as in the California case, when moving from an inefficient outcome to an efficient outcome, it is at least possible to achieve a Pareto improvement that makes everyone better off without making anyone else worse off. This means that equity need not, in theory, be sacrificed when moving from a less to a more efficient outcome. More often, however, there are almost always winners and losers from any change in economic policy, even those that increase economic efficiency. As long as the gains to the winners are bigger than the losses to the losers, a move will be efficient. Keep in mind then that efficiency and fairness are different notions. Efficient outcomes need not be equitable (or moral or fair), though they may be. At the same time, equitable outcomes need not be efficient, though they may be.

4.2 Efficient Pollution Levels

You may recall that we are supposed to be discussing the “right” level of pollution. How does efficiency fit in here? Let’s take the simplest example of pollution one can think of by following two workers, Brittany and Tyler, into their office in the morning.

They sit down at their desks, and Tyler pulls out a pack of smokes and lights up. Brittany hates cigarettes, but there’s no rule against smoking in the office. Tyler’s been smoking about five a day. Brittany is pretty desperate, so she considers a bribe. “How much would I have to pay you to smoke one less cigarette per day?” Tyler thinks it over. “One cigarette? I can put up with that for four dollars,” he says. “Two per day?” she inquires. “That’ll be tougher. You’d have to pay me six more dollars for that one.” The third cigarette, it turns out, could be eliminated for a bribe of an additional $8. They keep at it, eventually leading to Table 4.1.

The table reveals that, due to his addiction, Tyler is increasingly reluctant to give up each additional cigarette. Indeed, even after receiving a total of $28 for the first four, he would have to receive an additional $12 to quit smoking altogether.

Brittany has her own notion of the benefits of pollution reduction in the office. Getting rid of the first cigarette is essential to making the environment tolerable: she’d be willing to pay $10 to do so. Eliminating the next cigarette would make a big improvement but is not absolutely necessary. It’s worth $8. Her private benefit schedule for cigarette reduction is illustrated in Table 4.2.

The benefits of additional pollution reduction decline for Brittany as the number of cigarettes smoked falls, because the health damage and discomfort she experiences also decline. Thus, she’s willing to pay only $2 to get rid of the last cigarette, perhaps because she can take her daily coffee break (choose your poison) when Tyler chooses to light that one up.

Note that we’re focusing on reducing the number of cigarettes (units of pollution) one at a time. Economists call this marginal analysis. The last unit of pollution reduced is called the marginal unit; the costs (to Tyler) of reducing that unit are called the marginal costs, and the benefits (to Brittany) from reducing that unit are called the marginal benefits. Comparison of marginal costs with marginal benefits will help us zero in on the efficient level of pollution.

To help us determine the efficient level of cigarette reduction, Figure 4.3 graphs the marginal costs and benefits of giving up cigarettes. On the horizontal axis, we have the number of cigarettes reduced per day; on the vertical axis, dollars. Because marginals represent changes in total values as we move from one unit to the next, it is conventional to graph marginal values between the units on the $X$ axis. For example, the marginal cost of the fourth cigarette reduced is $10. Because this is the change in total cost as we move from three to four cigarettes reduced, you will notice that the $10 value is graphed halfway between three and four.

The curve labeled “Marginal costs of pollution reduction” illustrates the cost of giving up additional cigarettes by Tyler. It slopes upward, reflecting that the first cigarette smoked can be given up at a low cost by Tyler, although he would have to be mightily bribed to give up smoking altogether. The curve labeled “Marginal benefits of pollution reduction” reflects the value of a progressively less smoky environment to Brittany. It slopes downward because the health risk and discomfort from breathing secondary smoke decrease as the number of cigarettes is decreased.

You can probably guess where the efficient level of pollution reduction is going to be. ($X$ marks the spot.) Indeed, two cigarettes reduced is the efficient number. Why? Because, at any other level of pollution, both parties can be made better off by trading. To see this, consider the following:

This example is worth a close study (or, as we tell our students, this one will be on the test). To make sure you follow it, take a minute to explain to yourself why it is that, at any level of pollution other than three cigarettes smoked (or two reduced), both parties can be made better off through a trade. Three cigarettes smoked is the efficient level of pollution, because only at three cigarettes, it is impossible to make one party better off without making the other worse off.

Here is an outcome that, while being efficient, would strike many people as being unfair. Why should Brittany have to pay Tyler not to slowly poison her? This question is, as we will see, crucial in the discussion of a safety pollution standard. But, efficiency defenders respond that the issue of whether polluters have a right to pollute or victims have a right to prevent pollution should not necessarily be settled in the victim’s favor. While agreeing that fairness is an important issue, they ultimately feel that it is a matter of value judgment and thus lies outside the realm of economics.³ But, as we will see, the efficiency standard, in fact, has its own basis in “value judgments.”

A more consistent defense of the efficiency standard is that, because efficient outcomes maximize the monetary size of the total pie, consistently pursuing efficient outcomes does, on balance, benefit most people over time. While Brittany might lose out from this level of cigarette pollution, she will benefit from efficient regulation elsewhere. For example, she may get lower priced strawberries if pesticide use is regulated at an efficient and safe, as opposed to a more stringent and costly level.

In this section, we have employed marginal analysis to identify the efficient pollution level—where the marginal benefits and costs of pollution reduction are equal. At any other level of pollution, it is possible to make all parties better off by moving toward efficiency. This section has also illustrated that efficient outcomes need not accord with standard notions of fairness. We now move on to consider the relationship between a marginal analysis of pollution reduction and one based on total costs and benefits.

4.3 Marginals and Totals

As noted, focusing on marginal costs and marginal benefits allowed us to isolate the efficient pollution level. This section digresses for a moment to illustrate the relationship between marginal and total cleanup costs and the marginal and total benefits of cleanup. The bottom panel of Figure 4.4 reproduces the marginal relationships shown in Figure 4.3 while the top panel graphs the total costs of cleanup (to Tyler) and the total benefits (to Brittany).

Both sets of curves illustrate the same information. The total costs of pollution reduction rise at an increasing rate, generating a curve that is bowed upward; another way of saying this is that the additional or marginal cost of each cigarette given up rises. Similarly, the total benefits of cleanup rise at a decreasing rate, producing a downward-bowed curve; thus, the marginal benefits of pollution reduction are falling.

How can we move from one set of curves to another? The marginal cost curve represents the change in total costs. Thus, as the figure illustrates, the marginal cost of the first cigarette reduced, $4, is just the change in the total cost curve between zero and one cigarette reduced. Similarly, the marginal benefit of the fifth cigarette reduced, $2, is the change in the total benefit curve between four and five cigarettes reduced. The marginal curves graph the total change in $y$ for a one-unit change in $x$. But, this is just the “rise” over the “run” of the total curve. Thus, the marginal curves graph the slopes of the total curves.⁴

Moreover, the area under the marginal cost curve equals the total cost. For example, the marginal cost of the first cigarette reduced, $4, plus the marginal cost of the second, $6, equals the total costs of two cigarettes reduced, $10. But $4 is just the area under the marginal curve between zero and one, while $6 is the area under the curve between one and two. We use this relationship often in the chapters ahead.

Finally, note that the efficient pollution level does not occur where total costs equal total benefits (at four cigarettes reduced). At this point, because total benefits and costs are equal, the net monetary benefits to “society” are zero. Instead, the efficient level occurs where the total benefit curve lies farthest above the total cost curve. Here, the net monetary benefits to Brittany and Tyler combined are maximized. At the point where the total benefits and costs are equal, we know that we have reduced pollution “too much” under an efficiency standard. At this point, the marginal costs of reduction exceed the marginal benefits, given the conventional shapes of the benefit and cost curves.⁵

To summarize, the relationship between the marginal and total benefit and cost curves is a straightforward one. The marginal curves graph the change in the total curves or, equivalently, their slope. The upward slope of the marginal cost of reduction curve thus reflects the total pollution-control costs, which rise at an increasing rate. Similarly, the downward slope of the marginal benefit of reduction curve results from an assumption that the total benefits of reducing pollution increase at a decreasing rate. Controlling pollution to a level at which the total benefits of reduction equal the total costs results in too much control from an efficiency perspective.

4.4 The Coase Theorem Introduced

One interesting aspect of the efficiency perspective is that, under certain circumstances, whichever way initial rights over pollution are granted, the efficient level of pollution doesn’t change! To see this, think for a minute about a situation in which Brittany is granted the right to ban smoking in the office. Will she do so?

Upon referring to Figure 4.3, we can see that the answer is no. If Tyler were willing to give up his last cigarette for $12, he would enjoy smoking that cigarette for more than, say, $11.99 in cash. Thus, he should be willing to pay up to that amount to be able to smoke it! Brittany, on the other hand, is now in the position of taking bribes, and her marginal benefit curve indicates that she would rather have $2 than a smoke-free environment. So, the two can strike a deal on the first cigarette. Similarly, because Tyler values the second at up to $10, and Brittany will sell him a “smoking right” for anything over $4, there is room to deal. Finally, Tyler would pay $8 for the third cigarette, and Brittany would accept (though she would be making only $2 in profit, it is still profit!). Notice that they would not go on to four cigarettes though, because Tyler would pay only $6 for it, and Brittany would demand $8.

We have just shown that, for a simple case of pollution reduction uncomplicated by transaction costs and free riding (discussed in the previous chapter), the efficient outcome is independent of whether pollution is legal. If polluters and victims can easily and effectively bargain, private negotiation should arrive at the efficient outcome, regardless of who has the initial right to pollute or prevent pollution. This result is known as the Coase theorem, after the Nobel Prize–winning economist Ronald Coase.⁶

Some have interpreted the Coase theorem to imply that, from an efficiency perspective, it does not matter who has to pay for pollution—victims or polluters. Either way, one arrives at an efficient solution. (Of course, on fairness grounds, most would argue that polluters should generally have to pay for damages.) However, as Coase himself recognized, the theorem holds only under highly limited circumstances.

In fact, efficiency is generally better served under a polluter-pays principle. This is true for two reasons. The first of these is the public good inherent in pollution cleanup, as discussed in the previous chapter. In real-world settings, a single polluter typically affects a broad community. Requiring polluters to pay for the privilege of polluting is more likely to generate an efficient outcome than does a policy that legalizes pollution and requires victims to pay polluters to reduce emissions. Having the polluters pay reduces the free riding and transaction costs associated with the latter policy.

More importantly, the assignment of liability has significant long-run effects. If Brittany paid Tyler not to smoke, she would very likely soon find all the smokers in the office moving their desk close to hers! More generally, if firms are given the right to pollute (or are subsidized to reduce pollution), their costs will be lower. In the long run, this practice encourages entry into the market and creates more pollution.⁷ For example, when taxpayers at large pay for the construction of landfills, households and firms have little long-run incentive to minimize their production of garbage. On the other hand, if landfill construction costs are financed by “pay by the bag” disposal fees, waste producers have an incentive to modify their long-run waste production strategies.

As we will see later, the Coase theorem is quite useful when analyzing the initial distribution of permits in a marketable permit system. For now, though, this example clearly illustrates the claim not only that zero pollution levels are expensive (poor Tyler suffers severe nicotine withdrawals) but also that a solution more efficient than banning exists in which all parties are made better off.

To review this section, the Coase theorem demonstrates that in the absence of transaction costs and free riding, the efficient pollution-control level can be achieved through negotiation, regardless of who has the legal right to pollute or prevent pollution. In the real world, however, efficiency is generally best served by following a polluter-pays principle. This is true both because of transaction costs and free riding and because long-run incentives for entry into the polluting industry are reduced when the polluter pays.

4.5 Air Pollution Control in Baltimore: Calculating the Efficient Standard

To give you a feel for how the efficiency standard might be applied in practice, let us look at a study that estimated the marginal benefits and marginal costs of reducing suspended particulate emissions in Baltimore under the current type of regulation.⁸ Suspended particulates are small particles of ash or soot emitted as a by-product of burning fossil fuels for power or transport. They contribute to respiratory ailments, some of which are fatal; they also soil clothing and buildings and reduce visibility. Figure 4.5 graphs the estimated marginal costs and benefits of different total suspended particulate (TSP) standards.

The marginal cost curve has the same shape as that in Figure 4.3; reduction of particulate emissions becomes increasingly costly. To move from a standard of 110 to 109 parts per million (ppm) would cost about $3 million, while tightening the standard from 95 to 94 ppm would cost an additional $16 million. This is because under the regulations in Baltimore, source types with relatively low costs of reduction must trim their emissions first. To meet tougher standards, facilities facing higher control costs must also reduce emissions.

The marginal benefit curve, on the other hand, is relatively flat. Unlike the cigarette case, the additional benefits of reduction do not decrease as the pollution level falls. Instead, the authors of the study assume that tightening the standard from 110 to 109 ppm yields roughly the same benefits—reduced death, sickness, and soiling and improved visibility—as moving from a standard of 100 to 99 ppm. They estimate these benefits to be around $10 million for each one-unit decrease in the particulate standard.

To arrive at this monetary figure for benefits, the authors value each life saved at $2 million, each lost workday at $100, and each restricted activity day at $25. Monetary benefits were also estimated for soiling and visibility. Chapter 6 discusses the means by which environmental economists attempt to estimate dollar values for these “priceless” elements of our lives. But to briefly preview, as suggested by the smoking example, economists generally measure the benefits of pollution reduction based on society’s willingness to pay (WTP) for that reduction. The benefits of less smoke in the office were precisely measured by Brittany’s WTP for fewer cigarettes. Although this WTP approach has problems, which we will explore in detail later, it captures the basic idea of trade-offs between a cleaner environment and all other goods.

The efficient standard occurs at about 98 ppm. With a looser particulate standard, the additional benefits from reducing pollution up to 98 ppm would be greater than the additional costs. However, moving to a standard tighter than 98 ppm would entail additional costs exceeding the additional value of the benefits. Thus, the net monetary benefits—the estimated value of clean air enjoyed by the citizens of Baltimore minus the cleanup costs borne by Baltimore firms (and ultimately, to some extent, Baltimore consumers)—are maximized at the efficient standard.

4.6 The Ethical Basis of the Efficiency Standard

Let us look one last time at the cigarette example and use our utility and social welfare functions to clearly focus on the ethical assumptions underlying the efficiency standard. First, there are no “equity” weightings: Tyler and Brittany’s utilities equally count in the overall social welfare. Second, no distinction is made between the utilities of victims and beneficiaries of pollution—Tyler’s need for cigarettes holds just as much weight as Brittany’s need for clean air. Together these conditions imply that the social welfare function underlying the efficiency standard looks like this:

Note the negative sign over Tyler’s cigarette consumption in Brittany’s utility function. Cigarettes are a “bad,” not a “good,” for her and so lower her utility. The social welfare function clearly illustrates the value judgments underlying the efficiency standard. By treating victims and beneficiaries equally, efficiency proponents do not acknowledge a “right” to protection from harmful pollutants.

Recall that, as we have stressed, the efficiency standard does not require that losers be compensated, even though this is possible. Suppose that company policy originally banned smoking in the office, but the office manager then decreed that Tyler could smoke three cigarettes a day and offered Brittany no compensation in return. This is still an efficient outcome because in dollar terms, the gain to Tyler is greater than the loss to Brittany.

It is worth stressing this point because many textbooks refer to the efficient standard as “optimal” or “socially optimal.” But unless, as economists, we are prepared to make judgments about who should win and who should lose in our society, efficient pollution-control outcomes are not optimal for society. Rather, they simply balance the costs and benefits of pollution reduction at a level where the net monetary benefits are maximized.⁹

Because a move to efficiency almost always creates losers as well as winners, such a move is not “socially optimal.” Rather, the best defense of efficiency is that, over time, most people (not just polluters) eventually reap net benefits from a move toward more efficient pollution standards. In concrete terms, most of us are both consumers of goods whose prices are raised by environmental regulation and beneficiaries of cleaner air and water. Efficient regulation, according to its proponents, is the best way to balance these two concerns.

4.7 Real-World Benefit– Cost Analysis

To this point, this chapter has looked at the efficiency standard, and benefit–cost analysis, in theory. In the real world, benefit–cost studies currently play an important, but not dominant, role in U.S. environmental regulation. Air, water, and hazardous waste are primarily regulated under a safety standard, and cost considerations play a relatively minor role. For example, the Clean Air Act requires air standards that protect human health, while the Clean Water Act sets zero discharge into navigable waters and an intermediate target of “fishable and swimmable waters” as its final goal. In neither case are compliance costs mentioned.

And so, for example, the Environmental Protection Agency (EPA) passed a regulation to tighten smog standards for which its own estimated costs ($600 million to $2.5 billion) exceeded the measurable benefits ($100 million to $1.5 billion). Why did it do so? It did so for reasons of safety. Despite the negative benefit–cost picture, the regulations were expected to save lives and allow more people to safely exercise outside.¹⁰ In fact, the courts have ruled that the air and water protection statutes, along with national hazardous waste legislation, explicitly prohibit the benefit–cost tests for new regulations. On the other hand, pesticides, insecticides, and other toxic substances are regulated under an efficiency standard. Pesticides cannot be banned unless the benefits of doing so are (roughly) shown to exceed the costs.

However, even for the safety statutes, the EPA is required to conduct formal benefit–cost analyses of any new regulation expected to cost more than $100 million. These benefit–cost studies, known as regulatory impact analyses (RIAs), are prepared for new safety standard regulations, such as the ozone tightening discussed above, even though their results cannot legally determine the regulatory decision. Why do them then? Under a safety standard, the agency is still directed to select “the regulatory alternative maximizing net benefits from those alternatives within the scope of the law.” In our terminology, the EPA is supposed to examine several different options and, among all the options that achieve a safety goal, pick the most efficient.¹¹

The major distinction between benefit–cost analysis in theory and that in practice is the presence, sometimes overwhelming, of uncertainty in the estimation of costs and benefits. As we will see in the next two chapters, it can be quite difficult to put a dollar value on the benefits and costs of a proposed environmental policy. In fact, rather than Figure 4.6, a realistic marginal cost–marginal benefit (MC-MB) diagram might resemble Figure 4.7. Here, the MC of pollution reduction lies in the range between the two thick lines, and MB within the range between the two thin lines. This generates a wide band of potentially efficient outcomes instead of a single point. This presence of significant uncertainty, in turn, creates the potential for political influence in the benefit–cost process, as no particular outcome can be labeled “efficient.”

Given that conservatives often push the hardest for the use of benefit–cost tests, many environmentalists view benefit–cost analysis as merely a political tool for rolling back environmental gains, in part by burying the regulatory process under a mound of paperwork. And at its worst, benefit–cost analysis is indeed used as an excuse to gloss over hard moral choices and provide a rubber stamp for preconceived notions about the right course of action.

However, at its best, a benefit–cost study clarifies the decision-making process. A good benefit–cost study follows the accepted procedures for estimating benefits and costs, provides a clear statement of all assumptions, points out uncertainties where they exist, and suggests realistic margins of error. Good benefit–cost analysis contrasts with bad benefit–cost analysis (those studies that violate these basic requirements). The latter are commonly sighted in offices in Washington, DC, and in state capitals. Partisan think tanks often hire researchers to generate benefit–cost analyses in support of their favored policies. Similarly to most tools, the benefit–cost methodology can be used for good or for evil.

One advantage of benefit–cost analysis, according to its advocates, is that it substantially reduces political influence in the policy arena. A naive claim in its favor is that benefit–cost analysis “allows the numbers to speak for themselves.” In reality, if one tortures the numbers sufficiently, they will often confess to the analyst’s viewpoint. The real advantage of benefit–cost analysis is that it places limits on the type of data torturing that can go on. For example, an economist may set out to prove that more stringent regulations of landfills are desirable on efficiency grounds. Within admittedly broad limits, however, his/her benefit and cost estimates are constrained by the methodology that we will outline in the next two chapters. It may well be that the numbers simply cannot credibly support his/her case.

How elastic are the bounds within which good benefit–cost studies must stay? This section explores three ways in which political influence affects the benefit–cost process: the hard numbers problem, agenda control, and paralysis by analysis.

Benefit–cost studies can often provide decision-makers with a false sense of precision. This is known as the hard numbers illusion. The apparently “hard” numbers in a summary table for a benefit–cost study are often quite soft when uncertainty and incomplete benefit coverage are factored in. Yet, decision-makers are often not interested in “uncertainty” and incompleteness; they want an answer and often one that supports their particular political agenda.

Political influence also shows up subtly in its impact on natural and social scientific debates. Corporate interests have used their resources in trying to control the scientific agenda by funding conferences and targeting research in certain areas. For example, the American Paper Institute and the Chlorine Institute have aggressively funded and promoted research downplaying the health effects of dioxin, a chemical used in these industries. The difference between the perspectives of industry, government, and academic scientists is reflected in a poll showing that 80 percent of the first group, 63 percent of the second group, and only 40 percent of the last group believed that a threshold existed below which exposure to cancer-causing agents was risk-free.¹² Even academic scientists, however, must often obtain research funding from industry.

Finally, when an efficiency test is enshrined as law, opponents of regulation can resort to the legal system and exploit the uncertainty in the process to delay or block the implementation of regulations. This is known as paralysis by analysis. One major environmental statute that used to require regulations to pass an efficiency test is the Toxic Substances Control Act (TSCA).

Under TSCA, the EPA tried for over 10 years to phase out the remaining uses of asbestos, a mineral product well known to cause lung cancer. The two big uses left were in cement pipe and automobile brakes. In the early 1990s, an appeals court rejected the EPA’s proposed phaseout of these products on several grounds. Although the EPA had in hand what it thought was a good benefit–cost study showing a substantial surplus of benefits over costs, the court ruled that the EPA (1) had not adequately considered less-costly alternatives, (2) had not adequately evaluated the environmental dangers of substitute products (non-asbestos brakes and piping), and (3) had not followed proper procedures for public comment on the dangers of asbestos.

Without dwelling on the merits of the case, it should be evident that trying to “prove” in a court of law that any regulatory decision not only has positive net benefits (benefits greater than costs) but also is the most efficient option (maximum net benefits) is a virtual impossibility. Partially as a result of this decision, in 2016, congress replaced TSCA’s benefit–cost standard with a safety standard.¹³

Clearly, benefit–cost analysis cannot pinpoint the efficient pollution level with the accuracy suggested by diagrams such as Figure 4.6. And given the uncertainties, politics influences the benefit–cost process, just as it would any alternative decision-making tool. A recent report harshly critical of benefit–cost analysis concluded: “Cost-benefit analysis cannot overcome its fatal flaw: it is completely reliant on the impossible attempt to price the priceless values of life, health, nature and the future…Cost-benefit analysis has not enriched the public dialogue; it has impoverished it, covering the topic with poorly understood numbers rather than clarifying the underlying clashes of values.”¹⁴

Despite this, however, benefit–cost analysis methodology does provide a “consensus” framework for helping to judge the balance between measurable benefits and costs. There is such a thing as good benefit–cost analysis. However, the only way to make sure that the good benefit–cost analysis carried out is to assure adequate review of the study and opportunities for appeal by the affected parties. Given the technical nature of benefit–cost methodology, some have suggested that the government provide funds to community groups and others who seek to challenge government studies. And legal standards that mandate efficiency are a poor idea on their own terms. Benefit–cost analysis is just not capable of identifying efficient outcomes with a precision that can stand up in court.

4.8 Summary

The efficiency approach raises the question of “how much pollution?” in a marginal-cost, marginal-benefit framework. In principle, we can replace “cigarettes reduced” with “pollution cleanup” in Figure 4.3, and the diagram will show us the efficient amount of global warming pollution, nitrous oxide, sulfur dioxide, CFCs, dioxin, DDT, waste oil, particulates, heavy metals, litter, nuclear waste, or PCBs that “should” be in the environment. Of course, to make this approach operational, one needs to estimate a dollar figure for both the costs and benefits of reducing each unit of pollution. The benefit figure will include quantifiable savings such as those on medical care. But, as in the Baltimore case, it also must take into account less easily calculable benefits such as human lives saved and cancers avoided.

But why should any pollutant be in the environment? Society is willing to suffer from pollution because it is an essential by-product of some good or service that people desire and because cleanup is not free. As shown in Figure 4.8, the “supply curve” for pollution cleanup is just the marginal cost of reduction curve; it shows the increasing cost to society in eliminating additional units of pollution. This cost will be determined by the technology available for controlling the pollution. The curve, for example, would shift down (toward the $x$-axis) if cheaper ways of reducing pollution were discovered.

Society, on the other hand, clearly has a demand for pollution reduction. The “demand curve” for cleanup is the marginal benefits of reduction curve; it illustrates the increasing damages inflicted on people or the environment as the amount of cleanup decreases. The location of the curve depends on a variety of factors, such as the number of organisms (including people) affected, weather conditions, and defensive measures taken by those affected. In the Baltimore case, for example, the curve would shift up (away from the $x$-axis) if more people moved into the city.

The efficient quantity of pollution from the production of a desired product will occur just at the point where the additional costs of reduction are equal to the additional benefits of reduction. Any more reduction, and the additional monetary costs borne by members of society exceed the additional monetary benefits; any less, and net social monetary benefits can be gained.

As our discussion of the open access and public good problems in the previous chapter made clear, economists do not think that free markets generate the efficient level of pollution. In the cigarette example, we saw that self-interested private parties, through their own negotiation, might arrive at the efficient level of pollution. But, this was a rather special example, featuring perfect information about costs and damages, clearly defined property rights, zero transaction costs, and no free riding. In the real world, “markets for pollution” seldom develop naturally, and a more likely outcome in an actual office would be an inefficient one. In the 1970s, the open access outcome would have meant complete pollution—five cigarettes per day; a safety-based regulation banning smoking completely is likely these days.

This chapter has employed the notion of marginal costs and marginal benefits associated with pollution reduction to illustrate how one might identify an efficient level of pollution. We have also seen that a move to more efficient pollution control almost always generates winners and losers, and because the losers are seldom compensated, such a move cannot be considered socially optimal. The best ethical defense of efficiency is thus that, because it maximizes the size of the measurable economic pie, over time, “most” people will benefit from more efficient pollution control. Finally, in the real world, benefit–cost tests are not a primary driver behind most U.S. environmental regulations, which tend to be safety based. When benefit–cost analysis is used as a basis for action, however, the primary challenge for economists is to provide sufficiently certain estimates of benefits and costs for practical guidance. When uncertainty looms large, and the efficient outcome is not clear, then benefit–cost analysis can become a politicized tool via mechanisms such as hard numbers illusion, agenda control, and paralysis by analysis. In the next two chapters, we will explore the methods that economists in fact use to estimate the benefits and costs of environmental and resource protection, with more or less precision.

KEY IDEAS IN EACH SECTION

REFERENCES

1. Ackerman, Frank. 2008. Poisoned for pennies. Washington, DC: Island Press.
2. Bromley, Daniel. 1990. The ideology of efficiency: Searching for a theory of policy analysis. Journal of Environmental Economics and Management 19(1): 86–107.
3. Coase, Ronald. 1960. The problem of social cost. Journal of Land Economics 3 (October): 1–44.
4. Gray et al. 2015. Allocating California’s water. Sacramento: Public Policy Institute of California.
5. Heinzerling, Lisa, and Frank Ackerman. 2002. Pricing the priceless: Cost-benefit analysis of environmental protection. Washington, DC: Georgetown Environmental Law and Policy.
6. Kneese, Alan V., and William D. Schulze. 1985. Ethics and environmental economics. In Handbook of natural resource economics, ed. A. V. Kneese and J. J. Sweeney. Vol. 1. New York: Elsevier.
7. Mohring, Herbert, and J. Hayden Boyd. 1971. Analyzing “externalities”: “Direct interaction” versus “asset utilization” frameworks. Economica 38: 347–61.
8. Oates, Wallace E., Paul R. Portney, and Albert M. McGartland. 1989. The net benefits of incentive-based regulation: A case study of environmental standard setting. American Economic Review 79(5): 1233–42.
9. Revesz, Richard L., and Michael A. Livermore. 2008. Retaking rationality: How cost-benefit analysis can better protect the environment and our health. Oxford: Oxford University Press.
10. US EPA. 1991. Final regulatory impact analysis of national primary drinking water regulations for lead and copper. Washington, DC: U.S. EPA, Office of Drinking Water.