Journal of Empirical Legal Studies最新文献

Trademark law protects marks to enable firms to signal their products' qualities to consumers. To qualify for protection, a mark must be able to identify and distinguish goods. US courts typically locate a mark on a “spectrum of distinctiveness”—known as the Abercrombie spectrum—that categorizes marks as fanciful, arbitrary, or suggestive, and thus as “inherently distinctive,” or as descriptive or generic, and thus as not inherently distinctive. This article explores whether locating trademarks on the Abercrombie spectrum can be automated using current natural-language processing techniques. Using about 1.5 million US trademark registrations between 2012 and 2019 as well as 2.2 million related USPTO office actions, the article presents a machine-learning model that learns semantic features of trademark applications and predicts whether a mark is inherently distinctive. Our model can predict trademark actions with 86% accuracy overall, and it can identify subsets of trademark applications where it is highly certain in its predictions of distinctiveness. Using an eXplainable AI (XAI) algorithm, we further analyze which features in trademark applications drive our model's predictions. We then explore the practical and normative implications of our approach. On a practical level, we outline a decision-support system that could, as a “robot trademark clerk,” assist trademark experts in their determination of a trademark's distinctiveness. Such a system could also help trademark experts understand which features of a trademark application contribute the most toward a trademark's distinctiveness. On a theoretical level, we discuss the normative limits of the Abercrombie spectrum and propose to move beyond Abercrombie for trademarks whose distinctiveness is uncertain. We discuss how machine-learning projects in the law not only inform us about the aspects of the legal system that may be automated in the future, but also force us to tackle normative tradeoffs that may be invisible otherwise.

The unprecedented occupational risks posed by the COVID-19 pandemic prompted employers to boost wages and federal authorities to propose hazard pay policies. This article estimates a market-based compensating differential for workers facing elevated risks through contact with the public using CPS employment data for 2019–2020 and occupational characteristic data from the US Department of Labor's Occupational Information Network. The estimated premium for exposure was roughly $820 overall and $1000 for essential workers. These premiums fall short of those proposed—but not enacted—by the federal government and are more commensurate with estimates of the value of a statistical life than were the federal proposals.

Using network analysis, we study referrals among plaintiff-side lawyers handling medical malpractice cases in Indiana. The referral network is stratified, with a few highly connected “hub” firms. Firm connectivity follows a power law distribution—suggesting that new entrants tend to associate with already well-connected firms, rather than starting a new network. Regression analysis shows that, for a given firm, connectivity (i.e., node degree) in the referral network and being loyal to a smaller number of firms both lead to better outcomes in non-referred cases. The referral network also became more concentrated over time. The stratification of the market for plaintiff-side representation is reinforced through these processes.

The plain language movement waged a silent revolution in the last generation, passing nearly 800 laws nationwide with little public debate. The movement asserted that it could scientifically show that there is a widespread readability crisis in legal documents, particularly contracts, that are unreadable to most adults. This article presents the largest empirical analysis of these claims to date, utilizing a dataset of 2 million contracts spanning multiple decades and industries and applying machine learning techniques. The study challenges fundamental tenets of the plain language movement. Contrary to prevailing beliefs, consumer agreements have median reading scores almost indistinguishable from those of daily news articles. A critical evaluation further exposes that readability tools endorsed by the movement are shoddy and manipulable and can produce grade-level differences of up to 4.6 years for identical texts. Moreover, the movement's core belief that Americans cannot read past the level of an eighth grader is exposed as an unsubstantiated myth. These findings fundamentally challenge the premises and effectiveness of one of the central consumer protection policies. These results call for a radical rethinking of legal access strategies, suggesting a shift from superficial readability metrics to addressing substantive issues in market dynamics and focusing on truly vulnerable populations. More broadly, this case study serves as a cautionary tale about the propagation of myths in legal scholarship and the potential for well-intentioned reform movements to divert attention and resources from more effective interventions.

The web of over 3000 Bilateral Investment Treaties (“BITs”) is the primary body of international law regulating cross-border investments. Research suggests that these treaties may have had a limited impact on promoting new investments, but that they still may have helped to improve countries' political relationships. In this paper, we document that this pattern was reversed for one of the most prolific signers of BITs: China. Using a stacked-event research design, we find that Chinese BITs are associated with an increase in Bilateral Foreign Direct Investment Flows but a divergence in voting patterns at the United Nations. We then explore two explanations for why the Chinese BIT program led to increased investment while also producing foreign policy divergence: that the domestic political costs of economic engagement with China push countries away, and that there are offsetting international pressures that have stronger pulls than China's efforts. We find no support for the domestic political costs explanation, but we do find evidence that the countries that received increased aid from the United States after signing a Chinese BIT had greater foreign policy divergence with China.

This study investigates the impact of legal representation on the process and outcomes of legal proceedings, focusing on Israeli traffic courts dealing with simple traffic offenses. It finds that legal representation significantly increased defendants' prospects of obtaining plea bargains and of avoiding demerits points. However, legally represented defendants were also exposed to higher fines compared to self-represented defendants. Since points are typically the primary concern for defendants, we contend that legal representation improved case outcomes, overall. Considering the simplicity of the process, the minimal legal expertise required, and the low stakes involved, the representation effect was unexpectedly robust. This effect may potentially be even stronger in more complex cases and where the stakes are higher. Unlike previous observational studies, this study reduces the risks associated with selection bias and produces findings that are more credible and potentially generalizable to other contexts.

Private security officers outnumber police by a wide margin, and the gap may be growing. As cities have claimed to defund the police, many have quietly expanded their use of private security, reallocating spending from the public to the private sector. It is difficult to know what to make of these trends, largely because we know so little about what private security looks like on the ground. On one prevalent view of the facts, a shift from public to private security would mean little more than a change of uniform, as the two labor markets are deeply intertwined. Indeed, academics, the media, popular culture, and the police themselves all tell us that private security is some amalgam of a police retirement community and a dumping ground for disgraced former cops. But if, instead, private officers differ systematically from the public police—and crossover between the sectors is limited—then substitution from policing to private security could drastically change who is providing security services.

We bring novel data to bear on these questions, presenting the largest empirical study of private security to date. We introduce an administrative dataset covering nearly 300,000 licensed private security officers in the State of Florida. By linking this dataset to similarly comprehensive information about public law enforcement, we have, for the first time, a nearly complete picture of the entire security labor market in one state. We report two principal findings. First, the public and private security markets are predominantly characterized by occupational segregation, not integration. The individuals who compose the private security sector differ markedly from the public police; they are, for example, significantly less likely to be white men. We also find that few private officers, roughly 2%, have previously worked in public policing, and even fewer will go on to policing in the future. Second, while former police make up a small share of all private security, roughly a quarter of cops who do cross over have been fired from a policing job. In fact, fired police officers are nearly as likely to land in private security as to find another policing job, and a full quarter end up in one or the other. We explore the implications of these findings, including intersections with police abolition and the future of policing, at the paper's close.

The California Racial Justice Act of 2020 recognized racial and ethnic discrimination as a basis for relief in capital cases, expressly permitting several types of statistical evidence to be introduced. This statewide study of the influence of race and ethnicity on the application of capital punishment contributes to this evidence. We draw on data from over 27,000 murder and manslaughter convictions in California state courts between 1978 and 2002. Using multiple methods, we found significant racial and ethnic disparities in charging and sentencing decisions. Controlling for defendant culpability and specific statutory aggravators, we show that Black and Latinx defendants and all defendants convicted of killing at least one white victim are substantially more likely to be sentenced to death. We further examined the role that race and ethnicity have in decision-making at various points in the criminal justice system. We found that prosecutors were significantly more likely to seek death against defendants who kill white victims, and that juries were significantly more likely to sentence those defendants to death. The magnitude of the race of the defendant and race of the victim effects is substantially higher than in prior studies in other states and in single-jurisdiction research. The results show an entrenched pattern of racial disparities in charging and sentencing that privileges white victim cases, as well as patterns of racial disparities in who is charged and sentenced to death in California courts that are the natural result of California's capacious statutory definition of death eligibility, which permits virtually unlimited discretion for charging and sentencing decisions. This pattern of racial preferences illustrates the social costs of California's failure to follow the Supreme Court's directive in Furman v Georgia to narrow the application of capital punishment over 50 years ago.

The current notice and choice privacy framework fails to empower individuals in effectively making their own privacy choices. In this Article we offer evidence from three novel experiments showing that at the core of this failure is a cognitive error. Notice and choice caters to a heuristic that people employ to make privacy decisions. This heuristic is meant to distinguish between a party's good or bad intent in face-to-face-situations. In the online context, it distorts privacy decision-making and leaves potential disclosers vulnerable to exploitation.

From our experimental evidence exploring the heuristic's effect, we conclude that privacy law must become more behaviorally aware. Specifically, privacy law must be redesigned to intervene in the cognitive mechanisms that keep individuals from making better privacy decisions. A behaviorally-aware privacy regime must centralize, standardize and simplify the framework for making privacy choices.

To achieve these goals, we propose a master privacy template which requires consumers to define their privacy preferences in advance—doing so avoids presenting the consumer with a concrete counterparty, and this, in turn, prevents them from applying the intent heuristic and reduces many other biases that affect privacy decision-making. Our data show that blocking the heuristic enables consumers to consider relevant privacy cues and be considerate of externalities their privacy decisions cause.

The master privacy template provides a much more effective platform for regulation. Through the master template the regulator can set the standard for automated communication between user clients and website interfaces, a facility which we expect to enhance enforcement and competition about privacy terms.