Empirical Legal Studies

"Results-to-Table" in Stata

While Stata--to my mind, anyway--still has not "fixed" the "unnecessary" degree of complexity in its graphics capabilities, it's nice to see it attend to the critical and often-challenging "results-to-table" migration task in v.17. While some may simply "hand-transcribe" results from the computer screen onto a table created for or embedded into Word documents, the more adventurous likely play with various "outreg" and related commands (e.g., "outreg2"). Stata has recently turned its attention to an alternative (or complementary) approach with updated commands that facilitate users "customizing" table presentations derived from various results. Unlike most efforts thus far, coding for this approach is "native" to Stata. Again, while I'm not entirely sure whether Stata has, once again, made the process unduly complicated, for anyone interested an introductory (and explanatory) video is here.

"Judging" Autonomous Vehicle Liability

Conventional wisdom implies that, on balance, judges, as a group and in their professional capacity, tend to disfavor new technologies. This judicial lurch towards disfavoring can present important policy implications as, especially in common law systems, judges can play a critical role in the regulation of new technologies. One example that will likely play out soon in real-time involves autonomous cars.

Dominating the experimental judicial decisionmmkaing literature are studies that rely on "mock" judges deciding "experimental" case hypotheticals. All too often, however, these "mock" judges involve either undergraduate students or a non-random collection of MTurk respondents. By contrast, findings from a recent paper, Judging Autonomous Vehicles, by Jeff Rachlinski (Cornell) and Judge Andrew Wistrich (Ca Central Dist. Ct), both valued Cornell colleagues, reflect an important advancement insofar as they derive from actual, real-live sitting judges (N=933).

In their studies, the authors presented hypothetical cases in which judges had to assess fault or determine damages for either an accident caused by an autonomous vehicle or by a human driver. What the paper finds is that "judges will treat automated vehicles with skepticism. In the related experiments we conducted, judges treated an autonomous vehicle that had been involved in a car accident more harshly than a human-driven vehicle involved in an essentially identical accident.” As these results comport with others prior research focusing on other technologies, larger policy implications loom. As the authors note, "at least when an innovation is artificial, non-human, and a dramatic departure from existing practices, judges act as a brake, delaying and hindering its adoption." The paper's abstract follows.

“The introduction of any new technology challenges judges to determine how it into existing liability schemes. If judges choose poorly, they can unleash novel injuries on society without redress or stifle progress by overburdening a technological breakthrough. The emergence of self-driving, or autonomous, vehicles will present an enormous challenge of this sort to judges, as this technology will alter the foundation of the largest source of civil liability in the United States. Although regulatory agencies will determine when and how autonomous cars may be placed into service, judges will likely play a central role in defining the standards for liability for them. How will judges treat this new technology? People commonly exhibit biases against innovations such as a naturalness bias, in which people disfavor injuries arising from artificial sources. In this paper we present data from 933 trial judges showing that judges exhibit bias against selfdriving vehicles. They both assigned more liability to a self-driving vehicle than they would to a human-driven vehicle and treated injuries caused by a self-driving vehicle as more serious than injuries caused by a human-driven vehicle.”

Diff-in-Diff

Owing to the steadily increasing popularity of Diff-in-Diff models across an array of fields, including law, Stata's newest version (v.17) builds out its Diff-in-Diff modeling capacities (a brief summary tutorial video is here). While Stata's brief video provides a quick introduction, Brady Neal's more detailed Diff-in-Diff modeling video (here) might assist those seeking more comprehensive information. Obviously, Diff-in-Diff models are not without important limitations, including the all-important parallel trends assumption. That such modeling limitations exist, however, do not distinguish Diff-in-Diff specifications from other competing empirical strategies.