Black Metropolis Research Consortium
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Epstein on Private Discrimination: Searching for Common Ground
No full textIn the midst of a brilliantly independent academic career, Richard Epstein wrote perhaps his most contrarian work in 1992: Forbidden Grounds: The Case against Employment Discrimination Laws. In this short essay, I ask whether there can be any common ground between Epstein and those who generally defend laws against private discrimination, including myself. One possibility is the more subtle contribution Epstein makes in shifting the evaluative discourse from the deontological to the consequential. Without agreeing with his assessment of costs and benefits, one can embrace Epstein’s normative framework (even) in this legal domain. As Epstein provides a compendium of costs to employment discrimination laws, I offer here a compendium of possible benefits against which the costs should be weighed. Doing so leaves plenty of room for disagreement but perhaps surprisingly provides additional support for Epstein’s critique of the Age Discrimination in Employment Act
Visibility and Indivisibility in Resource Arrangements
Get PDFProjects like highways, bridges, pipelines, and wildlife corridors exhibit indivisibilities — we need the whole thing to have anything of value. Many environmental and social goals have a similar all-or-nothing character: staying above or below a certain critical threshold can make all the difference. This essay focuses on the role of visibility in addressing resource dilemmas that have this structure. I examine how two kinds of visibility can help avoid catastrophic consequences and advance desirable ones. The first involves recognizing when an indivisibility is present — that is, appreciating the vulnerability of resources to thresholds and cliff effects before it is too late. The second involves seeing how individual decisions about resources stack together to generate outcomes. When a resource problem suffers from poor visibility along these dimensions, finding ways to clear the view can improve the prospects for cooperative solutions
Promoting Regulatory Prediction
Get PDFIt is essential for environmental protection that private actors be able to anticipate government regulation. If, for instance, the Biden Administration is planning to tighten regulations of greenhouse gas emissions, it is imperative that private companies anticipate this regulatory change now, not a few years from now after they have constructed even more coal- and gas-fired power plants. Those additional power plants will mean more irreversible greenhouse gases, and these plants can be politically challenging to shutter once built. The point is general to private actors making decisions in the shadow of potential government regulation. Better information about future government actions is thus critical for the benefit of both private actors and society at large. In this Article, we consider market-based and non-market-based means by which to generate information about future government action. We find no perfect answer.
We consider three market-based solutions—prediction markets, the use of equity markets to hedge against future government action, and machine-learning and predictive technologies—and three government-based solutions—greater transparency, the development of intellectual property rights in predictive information, and prediction-forcing regulation, which is regulation that requires private actors to make public predictions about future government action. None of these is a panacea. The market-based solutions founder on the limitations and thinness of markets. Government-based solutions come with significant structural downsides related to the division of authority among different levels of government (federal versus state versus local) and different branches of government at each level (executive versus legislative). We conclude that prediction-forcing regulation may be the most promising avenue, though it too is likely not a full solution
Ban-the-Box Measures Help High-Crime Neighborhoods
No full textMany localities have in recent years regulated the use of questions about criminal history in hiring, or “banned the box.” We show that these regulations increased employment of residents in high-crime neighborhoods by up to 4 percent, consistent with the central objective of these measures. This effect can be seen in both aggregate employment patterns for high-crime neighborhoods and commuting patterns to workplace destinations with this type of ban. The increases are particularly large in the public sector and in lower-wage jobs. This is the first nationwide evidence that these policies do indeed increase employment opportunities in neighborhoods with many ex-offenders
Culture and Compliance: Evidence from the European Union Emissions Trading Scheme
No full textI study the role of culture in firms’ compliance decisions in the context of the EU Emissions Trading Scheme, an international regulation implemented in multiple countries with different levels of cultural indicators. To probe causality, I look within countries and exploit the differences in the locations of central headquarters of multinational firms. Using trust as a main cultural indicator, this exercise reveals that installations owned by firms headquartered in high-trust countries were more likely to comply with the regulation than those owned by firms headquartered in low-trust countries, even when they operated in the same geographic area. Using other relevant indicators of culture such as morality and civic virtue yields similar results, which suggests that culture, measured by several indicators, exerts influence on the compliance behavior of firms
The Aggregate Cost of Crime in the United States
No full textEstimates of crime’s burden inform public and private decisions about crime-prevention measures. More than counts of criminal offenses, the aggregate cost of crime conveys the scale of problems from crime and the value of deterrence. This article offers an estimate of the total annual cost of crime in the United States, including the direct costs of law enforcement, criminal justice, and victims’ losses and the indirect costs of private deterrence, fear and agony, and time lost to avoidance and recovery. The findings update crime-cost estimates of past decades while expanding the scope of coverage to include categories missing from past studies. The estimated annual cost of crime is 5.76 trillion including transfers from victims to criminals and 3.92 trillion net of transfers
Regulation and Redistribution with Lives in the Balance
Get PDFA central question in law and economics is whether non-tax legal rules should be designed solely to maximize efficiency or whether they also should account for concerns about the distribution of income. This question takes on particular importance in the context of cost-benefit analysis. Federal agencies apply cost-benefit analysis when writing regulations that generate multibillion dollar impacts on the US economy and profound effects on millions of Americans’ lives. In the past, agency cost-benefit analyses typically have ignored the income-distributive consequences of those regulations. That may soon change: On his first day in office, President Biden instructed his Office of Management and Budget to propose procedures for incorporating distributive considerations into cost-benefit analysis, thus bringing renewed relevance to a long-running law-and-economics debate.
This article explores what it might mean in practice for agencies to incorporate distributive considerations into cost-benefit analysis. It uses, as a case study, a 2014 rule promulgated by the National Highway Traffic Safety Administration (NHTSA) requiring new motor vehicles to have rearview cameras that reduce the risk of backover crashes. As with most major federal regulations that impose large dollar costs, the principal benefit of the rear visibility rule is a reduction in premature mortality. Quantitative cost-benefit analysis typically translates mortality reductions into dollar terms based on the “value of a statistical life,” or VSL. Any distributive evaluation of the rule will depend critically on a parameter known as the “income elasticity of the VSL,” which reflects the relationship between an individual’s income and her willingness to pay for mortality risk reductions. Although agency cost-benefit analyses use the same VSL for all individuals regardless of income, the Department of Transportation—of which NHTSA is a part—has issued guidance on the income elasticity of the VSL for other purposes. When this article applies the Department of Transportation’s income-elasticity guidance in its distributive analysis, the rear visibility rule appears to be “regressive”: it generates net costs for lower-income groups and net benefits for higher-income groups. Rerunning the distributive analysis with equal dollar VSLs at all income levels, the rule appears to be “progressive”: lower-income individuals are the primary beneficiaries and higher-income individuals are the losers. The article goes on to explain why assumptions about the relationship between income and the VSL will have important implications for distributive analyses of other lifesaving regulations.
The article then asks what agencies ought to do: should they incorporate distributive objectives into cost-benefit analysis by assigning greater weight to dollars in lower-income individuals’ hands, and should they assign different dollar VSLs to individuals with different incomes? The two questions are closely linked. Incorporating distributive objectives into costbenefit analysis of lifesaving regulations while maintaining equal dollar VSLsfor rich and poor will potentially produce perverse outcomes that—according to standard economic thinking—actually redistribute from poor to rich. After canvassing options, this article ultimately concludes that the status quo approach—equal weights for low-income and high-income individuals’ dollars, equal dollar VSLs for low-income and high-income individuals—makes practical sense in light of expressive concerns, informational burdens, and institutional constraints. The article ends by reflecting on the case study’s lessons for broader debates over legal system design, and it explains why the issues that arise in the rear visibility case study are likely to affect other efforts to redistribute through non-tax legal rules
Artificial Intelligence and the Rule of Law
Get PDFThis chapter examines an interaction between technological shocks and the “rule of law.” It does so by analyzing the implications of a class of loosely related computational technologies termed “machine learning” (ML) or, rather less precisely “artificial intelligence” (AI). These tools are presently employed in the pre-adjudicative phase of enforcing of the laws, for example facilitating the selection of targets for tax and regulatory investigations (Coglianese and Lehr, 2016). They are also increasingly used during adjudication, for example, to facilitate and guide determinations of individual violence risk during pretrial bail determinations (Huq, 2019). Predictions of a general displacement of human judgment by code-driven counterparts abound (Re and Solow-Niedemann, 2019; Volokh, 2019; but see Wu, 2019). But in near equal measure, that prospect is also loudly decried. Anticipated effects on the fairness, transparency, and equity of adjudicative systems are the main grounds for such resistance (Michaels, 2019; O’Neil, 2016). Even if these criticisms are not framed explicitly in terms of the rule of law, they often overlap with, or are closely adjacent to, the normative concerns that ordinarily travel under that rubric.
Two general questions respecting the rule of law arise from these developments. The more immediately apparent one is whether these technologies, when integrated into the legal system, are themselves compatible or in conflict with the rule of law. Depending on which conception of the rule of law is deployed, the substitution of machine decision-making for human judgment can kindle objections based on transparency, predictability, bias, and procedural fairness. A first purpose of this chapter is to examine ways in which this technological shock poses such challenges. The interaction between the normative ambitions of the rule of law and ML technologies, I will suggest, is complex and ambiguous. In many cases, moreover, the more powerful normative objection to technology arises less from the bare fact of its adoption. It is rather a function of the socio-political context in which adoption occurred and the dynamic effect of technology on background disparities of power and resources. ML’s adoption likely exacerbates differences of social power and status in ways that place the rule of law under strain. Attending to this dynamic draws useful attention to a topic that has been noticed in theorizations of the rule of law (e.g., Gowder, 2016; Wilmot-Smith, 2019), but not extensively examined: the interaction between social and economic dynamics on the one hand, and the rule of law on the other.
The second question posed by new AI and ML technologies has also not been extensively discussed. Yet it is perhaps of more profound significance. Rather than focusing on the compliance of new technologies with rule-of-law values, it hinges on the implications of ML and AI technologies for how the rule of law itself is conceived or implemented. As Taekema (2020), has recently observed, many of the canonical discussions of the rule of law—including Dicey’s and Waldron’s—entangle a conceptual definition and a series of institutional entailments. Fuller (1964), Raz (1979), and Waldron (2011), for example, assume that the rule of law requires more or less specific institutional forms, including courts. They presumably also posit human judges exercising discretion and making judgments as necessary rather than optional. For these institutional entailments of the rule of law, a substitution of human for ML technologies likely has destabilizing implications. It sharpens the question whether the abstract concept of the rule of law needs to be realized by a particular institutional form. It raises a question whether instead technological change might demand amendments to the relationship between the concept(s) and the practice of the rule of law. For pre-existing normative concepts and their practical, institutional correlates may no longer hold under conditions of technological change. At the very least then, specification of institutional forms of the rule of law under such conditions raises challenges not just as a practical matter but also in terms of legal theory.
The chapter begins by presenting a brief introduction to ML’s present and likely future uses in the law’s enforcement and adjudication, noting the different considerations that apply to these contexts. (For the balance of the chapter, I focus on machine learning and use the term “ML” because it is the most pertinent new computational technology, and because the term AI is so broad that it risks confusion). I then consider challenges to rule-of-law values presented by this technological shift. In particular, the chapter explores interactions of ML as a new technology with social and economic arrangements, and the implications of this dynamic for the rule of law. Finally, I consider whether the emergence of ML should force a reconsideration of the ways in which the rule of law is conceptualized and implemented, and whether abstract and the practical reflection on the rule of law can be cleanly bifurcated
