More Settlement Data: California's STAND Act

In the wake of the "#MeToo" movement, in 2018 California lawmakers enacted the Stand Together Against Non-Disclosure (STAND) Act, which sets out to promote greater transparency in the resolution of sex discrimination, harassment, and abuse cases. At the same time, the STAND Act also helps frame analyses of questions relating to "what exactly happens in the wake of [such a] reform."

In another installment from their on-going exploration of "secret settlements," David Freeman Engstrom (Stanford) et al., assess a quarter-million case filings from the Los Angeles County Superior Court for insights on how the STAND Act may have influenced litigation patterns.

Core claims reported in Shedding Light on Secret Settlements: An Empirical Study of California’s STAND Act include what the study did not find. Notably, "the STAND Act did not yield a sharp increase or decrease in case filings. Nor did the Act appear to significantly prolong cases or amplify their intensity." That is to say, the paper argues that evidence from the STAND Act implies that parties will still settle cases even without the inducement of secrecy. An excerpted abstract follows.

Power and Sample Size: The Under-Appreciated Influence of "Noisy" Data

While problems with under-powered studies are well-understood, one under-appreciated aspect involves data noise. This subtle wrinkle is discussed in a recent (and helpful) post by Andrew Gelman (click here). Gelman's main point is that, when it comes to addressing under-powered studies, "the real problem is noise, not N."

Gelman's point is well-taken, if oft-ignored. For many researchers, the go-to remedy for under-powered studies is to simply increase sample size (N). Gelman's post underscores that "[n]oise (variation in effect size, variation in outcomes, and measurement error) is more of an issue than sample size because lots of bad studies are so noisy that, to get a large enough sample size to detect a reliable effect, you’d need N = a zillion, and at that point you’d be averaging over such a wide range of conditions that it’s not clear what you’re estimating anyway." Thus, possible solutions to problems attributable to under-powered studies benefit from multivariate rather than univariate approaches.

CELS 2026 -- Program

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Adding CI Shading to Line Graphs

While Stata's graph commands can be notoriously complicated, some can be worth the effort. For example, adding CI shading to a conventional line graph permits researchers to move from this:

 

to something like this:

 

How to do so in Stata is discussed here.

What's In a Name? Experimental Evidence From German Judges

Questions about whether justice is, indeed, blind, persist. One particular question, whether judges treat litigants differently based on ethnicity, continues to receive attention in the United States as well as Europe.

In a recent contribution to this literature, Do German Judges Treat People With Foreign Names Differently? A Vignette Experiment, the authors, Alexander Morell (Goethe Univ.-Frankfurt) and Aileen Oeberst (Indep.), report experimental findings drawn from 282 sitting German judges designed to assess whether "German judges evaluate a case differently when the defendant bears a Turkish rather than a German name." To do so, the authors executed a preregistered vignette experiment in a 2 × 2 between-subject design featuring two experimental factors: the defendant’s name (Turkish versus German) and the consistency of the witness testimony.

While the study’s main results reassure as they “show little evidence of average differential treatment by defendant name across most outcomes,” one of the study’s robustness checks finds “an interaction between the consistency of the evidence and the effect of a Turkish name." An excerpted abstract follows.

In Memoriam: Prof. Thomas S. Ulen (1946-2026)

It is with a heavy heart that I note the passing of Prof. Tom Ulen, a leading empirical (and theoretical) law and economics scholar. Along with his seminal scholarly contributions, Tom will also be remembered for his kindness, warmth, mentorship, and humor. Tom is already deeply missed by many. (Click here for a memorial notice from the European Association of Law and Economics.)

CELS 2026: Registration Is Now Open

Registration

Registration is now open for the 2026 Conference on Empirical Legal Studies (CELS), which will take place on October 2-3, 2026, at Northwestern Pritzker School of Law in Chicago. Registration, hotel information, travel grant information, and more are available on the conference website: https://cels2026.com.

We encourage participants to register and reserve hotel accommodations as soon as possible.

Hotel Information

We have reserved a block of rooms at The Wade hotel near Northwestern Law at a conference rate of $189 per night. The conference block is available for October 1-2, 2026, and the deadline to reserve a room at The Wade Hotel at the CELS 2026 group rate is Friday, September 11, 2026.

Book at The Wade at the CELS 2026 group rate.

If you would like to arrive earlier or depart later, please contact the hotel directly:

• Hannah Lovett:  hannah.lovett@pyramidglobal.com

Trademark Data From the Japan Patent Office

While trademark law resides a bit afar from my usual "sandboxes," a recent paper in this area accompanies the release of a related open-access dataset. In the paper, The Japan Trademarks Dataset: An Open-access Resource, Jeremy Sheff (St. John's) emphasizes how building such datasets, along with making them open-access, "is thus crucial to ensuring the continued progress of our understanding of trademarks and their operation in the world."

To this end, the Japan Trademarks Dataset (link here) includes "all data preserved by the Japan Patent Office on trademark applications, registrations, and administrative proceedings (including data on parties, examiners, prosecution history events, and interactions with the international trademark system) from the inception of Japan’s trademark system in the late 19th century through September 25, 2019."

Data on AI's Risk of Bias

Employers' recent turn to AI to screen job applications raises concerns about algorithmic bias. This concern, in turn, has fueled efforts to "blind" algorithms to various applicant characteristics, such as, e.g., gender. Whether efforts to "blind" algorithms actually work endures as an empirical question.

Data on this question are analyzed in a recent paper, Blinding Gender: An Algorithmic Bias Experiment. In it, Charlotte Alexander (Georgia Tech--Business) et al. lever 9,000 recommendation letters submitted by applicants to medical residency programs in the service of testing "blinding" efforts.

The paper's core finding emphasizes that "even after blinding, our best performing model successfully predicts applicant gender 63% of the time, indicating that recommendation letters retain implicit linguistic patterns that continue to reveal identity." The authors conclude that "blinding strategies fail as a mechanical matter, in that they do not actually blind AI models to applicants’ gender" as the recommendation letters retain linguistic patterns that reveal applicants' identities (or here, gender). To be sure, as a naive ex ante prediction baseline should yield approximately 50%, whether the 63% rate found in the paper is "enough" to safely scaffold a conclusion that "blinding" efforts "fail" remains debatable. Regardless, and any quibbles notwithstanding, an excerpted abstract follows.

Tax Rates, Salary Caps, and NHL Team Performance

As I come across comparatively fewer empirical tax papers than I do for other law-related fields, I was delighted to stumble across Tax Differentials and Team Performance Under the NHL Salary Cap. In it, Martin Higgins (Concordia Univ. - Edmonton) and Mackenzie Frost Common (Alberta) exploit the NHL's adoption of a salary cap after the 2004-05 NHL lockout to assess the competitive implications of tax rate variation. This is not an abstract question as prior research on labor markets and institutional rules in professional sports implies that compensation constraints (e.g., salary caps) may influence competitive outcomes.