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In the United States, cost-benefit analysis is in the ascendancy. For over twenty years, American presidents have required agencies to perform CBA for major regulations; indeed, they have told agencies to regulate only if the benefits of regulation justify its costs.1 Congress has also shown considerable interest in CBA, most prominently in the Safe Drinking Water Act, which asks agencies to produce quantitative assessments of both costs and benefits. For their part, federal courts have adopted a series of principles that promote CBA—saying that if Congress has not been clear, agencies may consider costs, take account of the substitute risks introduced by regulation, and exempt trivial risks from governmental control.

In its enthusiasm for cost-benefit analysis, the United States provides a sharp contrast to Europe, which has shown intense interest in the Precautionary Principle. CBA and the Precautionary Principle can lead in radically different directions. For example, many Europeans argue that the consequences of genetic modification are uncertain, that real harm is possible, and hence that strin gent regulation is readily justified. By contrast, many Americans respond that the likely benefits of genetic modification are far greater than the likely harms and that stringent regulation is therefore unsupportable. With respect to climate change, many European leaders have argued in favor of precautions, even extremely expensive ones, simply to reduce the risk of catastrophe. But in the United States, a highly precautionary approach to climate change has had little appeal to national leaders, even those in the Democratic Party. Of course, the European posture on that topic is complex, not simple; but it is fair to say that with respect to climate change, precautionary thinking has had less appeal in the United States than elsewhere.

The tension between CBA and the Precautionary Principle raises serious questions about risk regulation. To engage in any kind of formal cost-benefit analysis, regulators must make difficult and often speculative judgments about the likely effects of alternative regulatory strategies; they must also turn those effects into monetary equivalents. How should we monetize the worst-case scenarios associated with climate change? For regulators, the easiest task is often the identification of costs, but even here they encounter formidable empirical problems. The monetary expense of regulations of different levels of stringency is difficult to project—especially because regulation often spurs technological innovation, greatly reducing the cost of risk reduction. In the context of ozone-depleting chemicals, the costs turned out to be far lower than anticipated, confounding early efforts at CBA. Interest groups have a stake in saying that regulation will be very costly and in emphasizing the worst cases associated with regulatory controls—perhaps including big increases in energy prices and big decreases in employment. Despite the claims of American industry, many people believe that aggressive regulation of greenhouse gases would have surprisingly low costs. Perhaps we can eliminate worst-case scenarios, much of the time, with lower economic burdens than we anticipate.

The identification of benefits presents even harder empirical problems—and knotty normative and conceptual ones as well. In the case of environmental harm, agencies must begin by estimating, in nonmonetary terms, the savings that are likely to result from regulation, including reductions in mortality and morbidity, along with improvements in visibility, recreation, aesthetics, animal welfare, property values, and more. These estimates are the foundation for the analysis on which agency decisions must largely depend. When science leaves room for doubt, as it often does, agencies typically specify a range of possibilities, representing low-end estimates and high-end estimates in addition to the best "point" estimate. Agencies might, for example, project that a certain regulation will save as many as eighty lives each year and as few as zero, with a preferred estimate of twenty-five.2 These numbers inevitably involve a lot of guesswork. Often the worst-case scenario will be much worse than the best point estimate, and scientists will disagree about whether or not the worst-case scenario is sufficiently likely to deserve serious attention. In that event, how should regulators proceed?

After specifying the likely benefits, CBA requires agencies to engage in multiple acts of conversion, assigning monetary values to human lives, human morbidity, and a range of harms to the environment. Typically, American agencies assign such values on the basis of private "willingness to pay" (WTP). For example, the Environmental Protection Agency values a human life at about $6.1 million, a figure that comes from real-world markets. In the workplace and for consumer goods, additional safety has a price; market evidence is investigated to identify that price. The $6.1 million figure, known as the value of a statistical life (VSL), is a product of many studies of actual risks in the workplace, the housing market, and the market for consumer goods, attempting to determine how much workers and others are paid to assume mortality hazards. Suppose that people are paid $600, on average, to eliminate risks of 1/10,000; suppose, for example, that workers who face risks of that magnitude generally receive $600 in additional wages each year. If so, the VSL would be said to be $6 million.

Where market evidence is unavailable, agencies often produce monetary valuations on the basis of contingent valuation surveys, which ask people how much they are willing to pay to produce certain desirable outcomes—to save coral reefs or endangered species, to eliminate a risk of chronic bronchitis or curable lung cancer, and much more. I recently asked University of Chicago law students how much they would be willing to pay to eliminate a cancer risk of 1 in 100,000 from arsenic in drinking water. The median answer was $50, producing a VSL of $5 million—fairly close to the EPA's $6.1 million figure. Drawing on market evidence and contingent valuation studies, the EPA has valued a case of chronic bronchitis at $260,000, an emergency hospital visit for asthma at $9,000, hospital admission for pneumonia at $13,400, a lost workday at $83, and a specified decrease in visibility at $14.3

All of these figures are contestable. The $6.1 million VSL was chosen not because some authoritative study demonstrates that it is correct, but because it is the mean figure of a number of studies. But why should regulators use the mean figure? Some studies suggest that the figure is in the vicinity of $14 million.4 For particular risks, such as those involving cancer, some people have suggested that the right figure is twice that.5 If we are focusing on worst-case scenarios, perhaps we should accept the higher number, which would dramatically increase our estimates of the benefits from regulation.

For a proposed arsenic regulation, for example, the total benefits might fall at around $23 million (assuming, not implausibly, that eleven lives would be saved, and "discounting" those lives on the ground that they would be saved in the future) or instead at around $3.4 billion (assuming, not implausibly, that 112 lives would be saved and using high-end estimates of a VSL).6 To say the least, a range of $23 million to $3.4 billion leaves regulators with a lot of discretion—especially if the costs of the regulation are in the vicinity of $200 million. In order for CBA to be workable, regulators need to have a relatively restricted range of possibilities.

Once a cost-benefit analysis is produced, what should be done with it? The most ambitious answer is that agencies should adopt regulations only when the likely benefits exceed the likely costs— and that if several possible regulations meet this test, agencies should select the one that "maximizes net benefits." On this approach, CBA provides the rule of decision, one by which regulators should be bound. But two obvious problems arise here. The first is that people's willingness to pay may not capture the benefits, to them, of the protection they seek. Poor people may be willing to pay very little for a risk reduction from which they would gain a great deal—for the simple reason that money is more valuable when you have little of it. The second problem is distributional. We need to know who, exactly, is paying the costs and receiving the benefits. If wealthy people are paying, we might want to go forward with the regulation even if the cost-benefit analysis suggests that we should not.

A more cautious response would be that agencies should generally require benefits to exceed costs, and should also seek to maximize net benefits, but that they need not do so. In this view, the outcome of the CBA provides a presumption but no more. The presumption could be rebutted by showing that the particular situ ation justifies a departure from the result indicated by CBA—as, plausibly, in cases in which poor people would stand to gain a great deal.7 Or the presumption could be rebutted by the decision to create a margin of safety to shield against the worst-case scenarios (see Chapter 3). A still more cautious approach would be that in deciding what to do, regulators should consider the outcome of CBA simply as relevant information—to be considered alongside other relevant information. There are important differences between those who would make CBA determinative and those who would merely make it relevant. But even on the most cautious understandings of the role of CBA, government's choices would be significantly affected by the translation of benefits into monetary equivalents.

To say the least, it is highly controversial to claim that people's protection against risks to life and health is properly measured by their willingness to pay to avoid worst-case scenarios. Thus far, I have been focusing on what people have to gain and to lose from eliminating such scenarios; WTP is at best a proxy for what matters. It is at least equally controversial to use WTP as the basis for policies protecting endangered species, nature, and wildlife. But as we have seen, the Precautionary Principle raises serious problems of its own. How much precaution is the right level of precaution? Are costs relevant to the answer? We have seen that taking precautions against all risks, rather than a subset, is not possible, even in principle. If all risks cannot be reduced at once, how should regulators set priorities?

In this chapter, I approach these questions through a discussion of three illuminating and influential books that offer radically different approaches to regulatory protection, money, and the proper treatment of worst-case scenarios. Frank Ackerman and Lisa Heinzerling believe that CBA is a hopelessly crude tool, one that buries indefensible judgments of morality and politics.8 Drawing on the war on terrorism, they argue for the Precautionary Principle instead—and they want government to focus in particular on worst-case scenarios and on irreversibility. By contrast, Adam Burgess uses the controversy over cell phones to suggest that the Precautionary Principle capitulates to, and even promotes, baseless public fears.9 Objecting to what he sees as excessive fear of new technologies, Burgess argues for careful attention to scientific evidence and for regulation only when the risk is real. Focusing directly on worst-case scenarios, Richard Posner argues for CBA and economic analysis in a context in which it seems least promising: catastrophic risk.10 He contends that climate change, asteroid collisions, terrorism, and other potentially catastrophic problems cannot sensibly be approached without a disciplined effort to quantify and monetize both costs and benefits. But where Ackerman and Heinzerling see CBA as an excuse for regulatory inaction, Posner invokes CBA on behalf of aggressive controls on greenhouse gases and other sources of potentially serious danger. Indeed, his central goal is to draw private and public attention to catastrophic risks that are exceedingly unlikely to come to fruition.

Building on the arguments made by Burgess and Posner, I shall mount a qualified defense of CBA here. Without some sense of both costs and benefits—both nonmonetized and monetized— regulators will be making a stab in the dark. We have seen that human beings have a great deal of difficulty in assessing risks, making them prone to both overreaction and neglect. CBA does not supply definite answers, and as I have said, it is only a proxy for what matters; but it can help to establish which risks are serious and which are not.

But building on the arguments made by Ackerman and Heinzerling, I shall explore some serious problems with CBA. As we have seen, regulators cannot always assign probabilities to bad outcomes, and when probabilities cannot be assigned, the standard form of CBA cannot get off the ground. In addition, willingness to pay is sometimes an inappropriate basis for regulatory policy. Human beings are citizens, not merely consumers, and their consumption choices, as measured by WTP, might be trumped by their reflective judgments as citizens. In any case, willingness to pay is dependent on ability to pay; when the poorest members of societies stand to gain from regulatory protection, they should be protected even if their poverty ensures that their WTP is low. This point helps to illuminate the controversial question whether and in what sense people in poor nations are "worth less" than people in rich nations.

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