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Abstract

The entire U.S. federal regulatory apparatus, especially that part devoted to reducing (or deciding not to reduce) risks to the environment, health, and safety (EHS), relies increasingly on judgments of whether each regulation would yield benefits in excess of its costs. These judgments depend in turn upon empirical analysis of the potential increases in longevity, quality of life, and environmental quality that the regulation can confer, and also of the economic resources needed to “purchase” those benefits—analyses whose quality can range from extremely fine to disappointingly poor. The quality of a risk analysis (from which the benefits of control are derived) or of an economic analysis depends on attributes they share in common, such as the complexity and rigor of the data collection and mathematical modeling, the transparency by which the assumptions used are disclosed, and the humility of the conclusions drawn (particularly the care taken to acknowledge uncertainty in the estimate). This Article is part of a series of investigations by a multidisciplinary team of scholars, examining whether regulatory cost analysis may be systematically less rigorous, transparent, and humble as compared to the corresponding analyses of risk upon which regulations are jointly based. In this particular study, I contrast the attention paid to depicting uncertainty on the “cost side” versus the “risk side” of cost-benefit analysis, and show that regulatory economics has steadily remained about ten to fifteen years behind risk analysis with respect to this important attribute of analytic quality. Various sections of the Article explain why overconfident pronouncements about cost or risk can thwart sensible decisionmaking, demonstrate how an unbalanced approach to analyzing risks versus cost is untenable, and trace the history of attempts to improve the estimation of regulatory cost uncertainty both inside and outside the major federal EHS regulatory agencies. The core of this Article is a combination of a statistical analysis and a set of case studies, showing how much improvement remains to be made on the “cost side” of regulatory uncertainty analysis, and providing various sets of reasons why this particular deficiency arose and persists. If decisionmakers and the public are not informed that the true magnitudes of regulatory cost may be much higher or (more likely) much lower than the overconfident estimates provided with current regulatory analyses, they cannot express their desires for more or less regulatory stringency in light of the resulting uncertainty in net benefit.

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