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Chapter 7 Emitting More Light than Heat Lessons from Risk Assessment Controversies for the “Job-Killing Regulations” Debate Adam M. Finkel Although we can choose to think descriptively, quantitatively, or both when we evaluate the pros and cons of whether and how to attack a hazard to health, safety, or the environment, both political leaders and the public increasingly expect that numbers will play a central role. And not just any numbers will do. As our faculties for collecting data, discerning causal relationships, and refining empirical models continue to improve, regulatory analysts are struggling to provide “high-quality quantification .” Those responsible for developing, supporting, or criticizing estimates of regulatory costs in general, and of the effects of regulation on jobs in particular, can either rise to or dodge the challenges of analyzing in the following ways: • thoroughly, neither omitting important categories nor illuminating a small part of the problem while ignoring the larger whole; • humbly, by acknowledging uncertainty in all inputs to analysis and quantifying (to the extent feasible) the uncertainty in the final estimate ; • transparently, by enumerating all major assumptions that underlie the estimate; • objectively, by seeking a mathematically unbiased estimate of the quantity being estimated;1 • logically, by counting each effect exactly once, not twice or more; • responsively, by presenting cost and benefit information that allows individuals who bear disproportionate costs or face disproportionate risks to see their own situation as well as that of the whole population averaged together; and Emitting More Light than Heat 129 • carefully, by keeping separate the empirical estimates of effect from the subjective valuation used to make commensurate those effects that are in different units or that occur at different times in the future. These are all challenges that quantitative risk assessment (QRA) for human health and environmental harms has already confronted, and surmounted with varying degrees of success, over the past several decades . In a larger project supported by the National Science Foundation (Finkel et al. 2006), of which this chapter and volume are parts, my colleagues and I have taken a wide-ranging look at how risk assessors involved in the regulatory process may approach many aspects of their work with more rigor, transparency, and attention to uncertainty than do their colleagues who conduct cost analysis in support of regulatory decision making. As a trailing-edge subcategory of regulatory cost analysis , the study of the effects of regulation on layoffs and unemployment (job impact analysis, or JIA) demonstrates even greater contrasts with the more established methods of QRA. But despite the very different raw materials used by JIA and QRA (production functions and elasticities in the former; toxicologic dose-response relationships and environmental fate-and-transport models in the latter), the controversies that risk assessment has faced down and defused have many close, sometimes exact, parallels to the challenges involved in the quest to improve regulatory economics in general and JIA in particular. This chapter will make explicit many thematic connections between the two spheres of analysis, in order to suggest lessons that job impact analysts can glean from the achievements—and the unfinished business—of QRA. I regard as fundamental to this volume the premise that both analysts and decision makers care about the magnitude of the employment impacts of regulation because we want (or should want) to give them proportionate attention—treating them as neither infinitely important nor absolutely trivial but rather considering them to be as important as their magnitude and perhaps also their distribution warrants. Choosing not to estimate any effects on employment, or declaring that it is too difficult to do so or that it is improper to make them part of the overarching cost–benefit ledger, really leaves us with only two options: to treat them as too unimportant to affect any decision or to treat them as so important as to trump any other aspects of decisions. So without more rigor in how we estimate the job impacts of regulation , we are in effect stuck with the same “precautionary principle” that in my opinion has sometimes stymied environmental health policy—and for which QRA is a principled alternative (Montague and Finkel 2007). As Livermore and Schwartz make clear (this volume), advocates of polar 130 Adam M. Finkel positions on certain regulations have simultaneously asserted either that we cannot afford to regulate due to mass layoffs (especially during a recession) or that we cannot afford not to regulate and turn our back on masses of new “green jobs” (especially during a recession). Of course...

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