A BETTER CALCULUS FOR REGULATORS: FROM COST-BENEFIT ANALYSIS TO THE SOCIAL WELFARE FUNCTION Matthew D. Adler, Richard A. Horvitz Professor of Law and Professor of Economics, Philosophy and Public Policy, Duke University. [email protected] Working Paper, February 2017 Introduction I. The Social Welfare Function (SWF) Framework A. Ethics, Weak Consequentialism, Welfarism 1. Weak Consequentialism 2. Welfarism B. The SWF Framework: Rules and Axioms C. The Well-Being Function D. Uncertainty II. The SWF Framework as a Guide to Regulatory Policy A. The SWF Framework as a Basis for Environmental, Health, and Safety Regulation B. Cost-Benefit Analysis (CBA) C. A Worked Example 1. Uniform Risk Regulation and Cost Incidence 2. Other Policies 3. A Summary III. Why the SWF Approach Improves on CBA: An Argument A. Defenses of CBA? 1. Long Run Pareto 2. Potential Pareto 3. Rough Proxy for Overall Well-Being B. Objections to the SWF Approach 1. Value Choices and Legal Legitimacy 2. The Tax System C. SWFs as a Regulatory Methodology: A Nuanced Approach Conclusion Appendix 1 A BETTER CALCULUS FOR REGULATORS: FROM COST-BENEFIT ANALYSIS TO THE SOCIAL WELFARE FUNCTION Introduction Cost-benefit analysis (CBA) has become the dominant governmental methodology, in the U.S., for evaluating regulatory policy. Since 1981, a Presidential order has directed regulatory agencies in the executive branch to comply with a cost-benefit standard, where statutory discretion exists to do so, and to prepare an analytic document describing the costs and benefits of major rules.1 These Presidential orders are enforced by OIRA, an oversight body within the Executive Office of the Presidency, which—along with policy offices within each regulatory agency—constitutes a significant bureaucratic structure implementing the construct of CBA.2 CBA is certainly an advance on what existed before 1981. A regulatory agency subject to the CBA order is instructed to characterize the effects of its proposed rules along multiple dimensions of human well-being—beyond the specific dimension (health, safety, environmental quality, etc.) that its organic statute highlights—and to make some attempt, using the metric of dollars, to commensurate effects along these multiple dimensions. This is progress. But CBA is not the endpoint of good regulatory policy analysis. It can be bettered. In this Article, I show how. We can improve CBA-based policy assessment by using the “social welfare function” (SWF) as a tool to evaluate proposed regulations. The SWF framework conceptualizes the status quo and each policy alternative as a pattern of well-being across the population of concern (or, given uncertainty, as a probability distribution across such patterns). CBA uses money as the metric for gauging policy effects on each individual, but money has “diminishing marginal utility”: a $10,000 increase in the money holdings of a millionaire is not the same, in well-being terms, as a $10,000 increase in the holdings of someone with average income: the millionaire gains less in welfare. The SWF approach corrects for the diminishing marginal utility of money by using an appropriately constructed measure w(.) of individual well-being as the indicator of how well each person is doing, and how much he or she stands to gain or lose from a given policy. A related point is that the SWF framework is sensitive to distributional considerations, while CBA is not. The framework can be specified in various ways—both with respect to the specific steps taken in constructing the well-being measure w(.), and with respect to the rule adopted for ranking patterns of well-being. One such rule is “utilitarian”; the utilitarian SWF says that policy P is better than policy P* if the sum total of individual well-being is greater with P. The utilitarian SWF is sensitive to the distribution of income. Ceteris paribus, the utilitarian SWF favors the transfer of a unit of income (dollars) from higher- to lower-income individuals, 1 Exec. Order No. 12,866, 3 C.F.R. 638 (1994). 2 For a summary of the system of regulatory review in the U.S., see ANDREA RENDA, LAW AND ECONOMICS IN THE RIA WORLD ch. 2 (2011). 2 since income has declining marginal utility. So-called “prioritarian” SWFs are yet more sensitive to the arrangement of income and other well-being attributes across the population. SWFs within the prioritarian family prefer, not merely to equalize the distribution of income, but indeed to equalize the distribution of well-being itself. Ceteris paribus, these SWFs favor the transfer of a unit of well-being from someone at a higher level of well-being, to someone at a lower level. The SWF framework has never (as far I’m aware) been put to use by U.S. regulatory agencies. However, it has deep roots in the academic literature. It originates in theoretical welfare economics, in work by Abram Bergson and Paul Samuelson from the 1930s and 1940s and, somewhat later, by Amartya Sen in response to the Arrow impossibility theorem.3 It is the linchpin for the contemporary literature in economics on optimal taxation. James Mirrlees, in path-breaking scholarship from the 1970s, used an SWF to study the problem of optimizing the schedule of income tax rates—balancing the gains to overall well-being from the redistribution of a fixed “pie” of income, against the disincentive to income-generating activities that occurs with taxes.4 Since Mirrlees’ work (work that earned him the Noble Prize), the leading methodology used by economists to address normative questions regarding taxation—the specification of income-tax rates, the choice between different types of taxes, and so on—has been the SWF.5 But the range of application of the SWF approach goes well beyond tax policy. It can, in principle, be used to regiment the normative assessment of any type of tax or non-tax policy. Indeed, SWFs are widely employed in the field of climate economics.6 How to make tradeoffs between the material costs of reducing carbon emissions (costs that may be distributed in various ways among the richer and poorer members of both present and future generations, depending on the policy choice), and the benefits (both environmental and material) of slowing warming (these benefits, too, being distributed both within and across the generations in one or another manner), 3 See MATTHEW D. ADLER, WELL-BEING AND FAIR DISTRIBUTION: BEYOND COST-BENEFIT ANALYSIS 79-86 (2012) (describing the origins of the SWF concept). 4 James Mirrlees, An Exploration in the Theory of Optimum Income Taxation, 38 REVIEW OF ECONOMIC STUDIES 175 (1971). 5 For overviews of the literature on so-called optimal taxation, including the role of the SWF therein, see LOUIS KAPLOW, THE THEORY OF TAXATION AND PUBLIC ECONOMICS (2008); MATTI TUOMALA, OPTIMAL REDISTRIBUTIVE TAXATION (2016); MATTI TUOMALA, OPTIMAL INCOME TAX AND REDISTRIBUTION (1990); GARETH MYLES, PUBLIC ECONOMICS (1995); BERNARD SALANIE, THE ECONOMICS OF TAXATION (2003); Mikhail Golosov and Aleh Tsyvinski, Policy Implications of Dynamic Public Finance, 7 ANNUAL REVIEW OF ECONOMICS 147 (2015); N. Gregory Mankiw, Matthew Weinzierl, and Danny Yagan, Optimal Taxation in Theory and Practice, 23 JOURNAL OF ECONOMIC PERSPECTIVES 147 (2009); Peter Diamond and Emmanuel Saez, The Case for a Progressive Tax: From Basic Research to Policy Recommendations, 25 JOURNAL OF ECONOMIC PERSPECTIVES 165 (2011); ROBIN BOADWAY, FROM OPTIMAL TAX THEORY TO TAX POLICY (2012). 6 See generally W.J. Wouter Botzen and Jeroen C.J.M. van den Bergh, Specifications of Social Welfare in Economic Studies of Climate Policy: Overview of Criteria and Related Policy Insights, 58 ENVIRONMENTAL AND RESOURCE ECONOMICS 1 (2014). On prioritarian SWFs and climate economics, see in particular Matthew D. Adler and Nicholas Treich, Prioritarianism and Climate Change, 62 ENVIRONMENTAL AND RESOURCE ECONOMICS 279 (2015). 3 is a complex ethical problem that has been much explored—and, I suggest, much illuminated— with the SWF construct. Other areas of application of the SWF construct include refinements of GDP as a measure of social condition,7 health care policy,8 environmental, health and safety regulation,9 and inequality metrics (which have a conceptual link to SWFs).10 The “Green Book” (the official policy-assessment document in the U.K., applicable to regulations as well as other types of governmental interventions, such as infrastructure spending) generally instructs decisionmakers to conduct CBA with “distributive weights.”11 CBA with distributive weights is a refinement of the standard unweighted technique, and can be used to approximate an SWF.12 Part I of this Article provides an overview of the SWF framework. Part II describes how that framework might be used as a guide to regulatory policy. The focus of Part II is environmental, health and safety regulation (a.k.a “risk regulation”): governmental interventions designed to reduce individual fatality and morbidity risks.13 Since environmental, health and safety regulation has been the leading substantive area of application of CBA to regulatory policy within the US government, it will also serve in this Article as the exemplar—with reference to which I’ll illustrate how the SWF methodology would function as a tool for regulators. Part II includes a stylized case study: a regulatory intervention reduces fatality risks to various population groups (differentiated by age, income, and health), at some cost to individuals’ incomes. Whether the policy is justified depends upon the pattern of risk reduction and income loss among the various groups—and upon the parameters of the SWF used to evaluate these changes. Using the case study, I illustrate the utilitarian and prioritarian SWFs, and compare these SWFs both to each other and to (two variants of) CBA. 7 See, e.g., Paul Schreyer, GDP, in THE OXFORD HANDBOOK OF WELL-BEING AND PUBLIC POLICY 21-46 (Matthew D.