Empirical Legal Studies

CELS 2023 -- Schedule/Program

2023 Conference on Empirical Legal Studies

October 13-14, 2023

Schedule/Program

University of Chicago Law School

The program (overview version) for the 17th Annual Conference on Empirical Legal Studies (CELS 2023) is now available (click here).

Access to the detailed schedule, however, is password protected, as it will be the source for accessing the conference papers. It is available to all registered participants in the conference (click here).

Legal Precedent and Decay

While the idea of legal precedent implies some degree of durability, precedents are not immortal. Over time, they lose their capacity to influence action; eventually they cease to be of anything other than historical interest. Although precedential decay's existence is generally accepted, the determinants of precedential decay are comparatively under-studied.

Yun-chien Chang (Cornell) & Geoffrey Miller’s (NYU) recent paper, Decay of Precedent in State Supreme Courts, extends this literature by analyzing citation decay patterns in state supreme court opinions, drawing on three different (and large) data bases. Their paper examines both “backward-looking” precedential decay—the frequency with which opinions in a given year cite to opinions from prior years as well as “forward-looking” decay—the frequency with which specific precedents are cited in later opinions. One key finding includes the extraordinary persistence of exponential decay across all of the data. The paper’s abstract follows.

“This paper investigates the decay of precedents in three unique databases: citations in all published opinions by state supreme courts in 2019 (102,555 citations); citations in all published opinions by state supreme courts in 2003 (114,777 citations); and citations by all state and federal courts to state supreme court opinions issued in 2003 (259,627 citations). We find that the frequency of citation drops off by a roughly constant ratio with each passing year. The large scale of our yearly data and the time studied (1904–2019) suggests that this pattern must be the result of underlying forces not having to do with particular circumstances of time or place. We also find that cases with dissents tend to cite older cases and longer decisions tend to be cited more often and faster. Criminal law cases, as compared to constitutional law cases, tend to cite more recent precedents, while trust and estate cases tend to cite older precedents. We offer some conjectures about possible reasons for the observed differences in decay rates.”

Plotting Coefficients

As I'm seeing more examples of coefficient plots [coefplot command] in the literature (see, e.g., below), I thought the following links might interest some readers. For brief discussions and examples (including Stata coding) click here, here, here, and here.

How to Value Data

While a recent paper, Data Valuation and Law, is not itself an "empirical paper," it is certainly "ELS-adjacent" and, as such, I thought it might interest some. This is particularly so as both AI and "big data" become increasingly important.

Motivating Jordan Berry (USC) and D. Daniel Sokol's (USC) paper is the observation that "while data has become increasingly valuable and important, the law’s attempts to value data have lagged, remaining confused and underdeveloped." To improve matters in this regard, the paper advances a “real options” approach for framing data valuation not because the approach is perfect but, rather, because “it is the least bad alternative available.” The paper's abstract follows.

"Data has become an increasingly valuable asset. Numerous areas of law—including contracts, corporate law, IP, antitrust, tax, privacy, and bankruptcy—require parties and courts to determine the value of assets, including data. Unfortunately, data valuation has been hindered by a lack of clarity over what data is and why it is valuable. This lack of clarity also increases the chances of legal decisionmakers valuing data in inconsistent ways, which would create further confusion, inefficiencies, and opportunities for regulatory arbitrage.

This article proposes a unified framework for valuing data that will promote consistent valuations across fields of law. It begins by conceptualizing data as building blocks: It is of little value on its own. But when placed in skillful and creative hands, it can unlock choices for its holders—choices they would not otherwise have—that can generate tremendous profits. Thus, data constitutes what is known as a ‘real option.’ This article shows how using real options to value data can significantly improve upon existing data valuation practices.”