Jonathan S. Masur

Unemployment and Regulatory Policy

In an earlier article, Regulation, Unemployment, and Cost-Benefit Analysis, we argued that regulatory agencies should incorporate the costs of unemployment into cost-benefit analyses of proposed regulations. We argued that alternatives to including unemployment costs in cost-benefit analysis — including feasibility analysis and job loss analysis — make little sense because they do not specify the threshold at which job loss is excessive and do not explicitly make tradeoffs between unemployment effects and social gains. Our paper was cited in a 2012 draft OMB report that sought advice from commentators as to whether cost-benefit analysis should incorporate unemployment costs and, if so, how it should do so. This chapter, prepared for a volume on the treatment of unemployment costs within cost-benefit analysis, builds and expands upon that earlier work. We first respond to some important questions and critiques that commentators have raised regarding our paper in the intervening years since we published it. We then discuss some broader issues raised by the debate about the incorporation of unemployment costs into cost-benefit analysis, including the role of “second-order” or remote costs and benefits and the treatment of the ex ante incentives of regulation.

Climate Regulation and the Limits of Cost-Benefit Analysis

Over the past two years U.S. regulatory agencies have issued fourteen regulations that take into account the effect of industrial activities and products on the global climate. The regulatory activity so far has already set precedents on which future regulation will rest. Yet despite the potentially momentous consequences, it has received no comment in the law review literature. This Article examines the record of these agencies and criticizes the methods they have used to calculate the social cost of carbon emissions. We also develop a larger theme about the relationship between cost-benefit analysis and politics. The best case for cost-benefit analysis is that its recommendations are politically neutral in the sense of drawing on widely shared intuitions about human well-being. But cost-benefit analysis cannot cope with inherently political questions involving contested normative issues. Policymakers will have to find alternative tools when those questions predominate

Retribution and the Experience of Punishment

In a prior article, we argued that punishment theorists need to take into account the counterintuitive findings from hedonic psychology about how offenders typically experience punishment. Punishment generally involves the imposition of negative experience. The reason that greater fines and prison sentences constitute more severe punishments than lesser ones is, in large part, that they are assumed to impose greater negative experience. Hedonic adaptation reduces that difference in negative experience, thereby undermining efforts to achieve proportionality in punishment. Anyone who values punishing more serious crimes more severely than less serious crimes by an appropriate amount - as virtually everyone does - must therefore confront the implications of hedonic adaptation. Moreover, the unadaptable negativity of post-prison life which is caused by the experience of imprisonment results in punishments that go on far longer than is typically assumed. Objectivist retributive theories that fail to incorporate these facts risk creating grossly excessive punishments. Certain retributivists have disputed the claim that adaptation is important to punishment theory, but their arguments are unavailing.

Well-Being Analysis vs. Cost-Benefit Analysis

Cost-benefit analysis is the primary tool used by policymakers to inform administrative decisionmaking. Yet its methodology of converting preferences (often hypothetical ones) into dollar figures, then using those dollar figures as proxies for quality of life, creates systemic errors so large as to deprive the tool of value. These problems have been lamented by many scholars, and recent calls have gone out from world leaders and prominent economists to find an alternative analytical device that would measure quality of life more directly. This Article proposes well-being analysis (WBA) as that alternative. Relying on data from the field of hedonic psychology that tracks people’s actual experience of life — data that has consistently survived scrutiny by passing the social science tests of reliability and validity — WBA is able to provide the same policy guidance as CBA without CBA’s distortionary conversion of preferences to dollars. We show how WBA can be implemented, and we catalog exhaustively its superiority over CBA. In light of this comparison, we conclude that there is no reason for CBA to continue as the decisionmaking tool of choice for administrative regulation.

Unquantified Benefits and the Problem of Regulation Under Uncertainty

Regulatory agencies are required to perform cost-benefit analysis of major rules. However, in many cases regulators refuse to report a monetized value for the benefits of a rule that they issue. Sometimes, they report no monetized value; at other times, they report a monetized value but also state that not all benefits have been quantified. On occasion, regulators also refuse to monetize or fully monetize costs. These practices raise a puzzle. If a regulator chooses not to monetize all the benefits or all the costs, it is not doing cost-benefit analysis. If it is not doing cost-benefit analysis, what is it doing? To investigate this question, we compiled a data set consisting of all major regulations issued by agencies from 2010 to 2013. We come to three conclusions. First, there are countless examples where agencies fail to fully monetize the benefits and costs of regulations. Second, in most cases, agencies could easily monetize or partially monetize those benefits and costs. Third, even where monetization would be difficult, the agencies could and should have made explicit the implicit valuations they relied on and supported those valuations as much as possible with empirical evidence. We then proceed to explain how agencies could engage in cost-benefit analysis even when they do not have a reliable basis for estimating valuations. Even where they lack complete data, agency regulators may be able to make reasonable guesses about the harms or benefits from regulations. In many cases, these guesses will be based on the experience and latent knowledge of the agency staff. These preliminary guesses constitute Bayesian prior probabilities. While agencies should be permitted to “guess” — that is, supply a subjective prior probability — they must also be required to update their estimates as they gain new information.As the last act of its 2014-2015 Term, the Supreme Court struck down a major EPA regulation limiting mercury emissions from electrical power plants.1 The formal legal reason was EPA’s failure to consider the costs of regulating mercury before deciding that it must be regulated.2 But the costs of the regulation—

How Patent Damages Skew Licensing Markets

9.6 billion—would not have attracted such attention if they had not seemed so disproportionate to the regulatory benefits. The only mercury-related benefits that EPA could measure and include in its analysis related to the possibility that mercury exposure would slightly reduce the IQ of the children born to women who consumed fish high in mercury while pregnant.3 Against

Raising the Stakes in Patent Cases

9.6 billion in costs, EPA calculated only

Innovation and Incarceration: An Economic Analysis of Criminal Intellectual Property Law

5 million in benefits—a ratio of 1,920 to 1.4 The imbalance in this ratio had a significant impact upon the court. As Justice Scalia wrote for the majority in Michigan v. EPA, “One would not say that it is even rational, never mind ‘appropriate,’ to impose billions of dollars in economic costs in return for a few dollars in health or environmental benefits.”5

Well-Being and Public Policy

If a litigated patent has previously been licensed to a third party, the courts generally adopt the terms of the prior agreement as the best measure of damages. However, while administratively convenient, this “licensing-based damages” standard creates problematic incentives and undermines the efficient commercialization of patented inventions. It rests on the trivialized (and generally false) presumption that a patent license is like a commodity, with the patentee charging a common price to all comers. As a consequence, patentees distort their future recovery prospects – and by extension the outcomes of future licensing negotiations – whenever they license their patents, whether or not today’s agreement will be a good proxy for tomorrow’s dealings or disputes. Knowing this, patentees are discouraged from licensing at anything less than a high royalty rate, even if they could reach many additional mutually-beneficial agreements on more modest terms. The result is that patent holders rationally cut off the bottom segment of the licensing market, creating substantial deadweight loss. This injures not only patentees, but also prospective licensees and their consumers. The standard creates additional problems by encouraging secrecy and “gamesmanship” in patent licensing. We propose that the licensing-based damages standard be abandoned, and that damages should generally be awarded ad hoc. This does not mean that private parties should ignore comparable licenses in their private dealings; it simply means that courts should not use them as a measure of damages. That this necessitates some speculation does not suggest it is the less desirable approach, for it is better that damages be somewhat random than systematically harmful. Further, while the licensing-based damages standard is clearly easy to apply, there is little reason to believe it is accurate in a typical case. As such, its apparent lack of randomness does not suggest that it is producing good results.