Court Opinion

ID: 7861819
Source: CourtListenerOpinion
Date Created: 2022-09-08 17:56:02.70465+00
Date Added: 2024-06-11T16:31:00.456504
License: Public Domain

STEPHEN F. WILLIAMS, Circuit Judge,
concurring:
I write separately to address the UAW’s apparent assumption that application of the significant risk/feasibility analysis associated with § 6(b)(5) is necessarily more protective of health and safety than a cost-benefit criterion. This is not self-evidently true.
First, if OSHA applies cost-benefit analysis, then more risks seem likely to qualify as “significant” within the meaning of Benzene; many risks that may seem insignificant if their discovery triggers regulatory burdens limited only by feasibility, as under § 6(b)(5), may be significant if the consequence is cost-justified corrective measures. Cf. Cass R. Sunstein, After the Rights Revolution 196 (1990) (explaining Benzene’s significant risk requirement as an artificial device for handling the unlimited character of the regulatory burden).
Second, even where the application of cost-benefit analysis would result in less stringent regulation, the reduced stringency is not necessarily adverse to health or safety. More regulation means some combination of reduced value of firms, higher product prices, fewer jobs in the regulated industry, and lower cash wages. All the latter three stretch workers’ budgets tighter (as does the first to the extent that the firms’ stock is held in workers’ pension trusts). And larger incomes enable people to lead safer lives. One study finds a 1 percent increase in income associated with a mortality reduction of about 0.05 percent. Jack Hadley & Anthony Osei, “Does Income Affect Mortality?”, 20 Medical Care 901, 913 (September 1982). Another suggests that each $7.5 million of costs generated by regulation may, under certain assumptions, induce one fatality. Ralph L. Keeney, “Mortality Risks Induced by Economic Expenditures”, 10 Risk Analysis 147, 155 (1990) (relying on E.M. Kitagawa & P.M. Hauser, Differential Mortality in the United States of America: A Study of Socioeconomic Epidemiology (1973)). Larger incomes can produce health by enlarging a person’s access to better diet, preventive medical care, safer cars, greater leisure, etc. See Aaron Wildavsky, Searching for Safety 59-71 (1988).
Of course, other causal relations may be at work too. Healthier people may be able to earn higher income, and characteristics and advantages that facilitate high earnings (e.g., work ethic, education) may also lead to better health. Compare C.P. Wen, et al., “Anatomy of the Healthy Worker Effect: A Critical Review”, 25 J. of Occupation Medicine 283 (1983). Nonetheless, higher income can secure better health, and there is no basis for a casual assumption that more stringent regulation will always save lives.
It follows that while officials involved in health or safety regulation may naturally be hesitant to set any kind of numerical value on human life,1 undue squeamishness may be deadly. Incremental safety regulation reduces incomes and thus may exact a cost in human lives. For example, if analysis showed that "an individual life was lost for every $12 million taken from individuals [as a result of the regulation], this would be a guide to a reasonable value tradeoff for many programs designed to save lives.” Keeney, “Mortality Risks Induced by Economic Expenditures”, 10 Risk *68Analysis at 158. Such a figure could serve as a ceiling for value-of-life calculated by other means, since regulation causing greater expenditures per life expected to be saved would, everything else being equal, result in a net loss of life.

. Preference-based techniques are a commonly used approach, but are subject to such pitfalls as wealth bias, age bias and inconsistency. See, e.g., Lewis A. Kornhauser, "The Value of Life”, 38 Clev.St.L.Rev. 209 (1990). For example, if estimates of the benefit of reducing risks are based on the affected workers’ willingness to pay for risk reduction, low-paid workers will receive less protection than better paid ones. See id. at 221. There are, however, solutions. The wealth bias problem, for example, could be avoided by estimating the willingness to pay of persons of median or mean wealth. See id. at 221-22.