Empirical Legal Studies

QuantLaw 2020--Univ. Arizona College of Law--Call for Abstracts

As a fan--and proponent--of various genres of the "micro-conference" (that is, a 1-2 day topically/thematically-focused conference marked by an emphasis on scholarship by a limited number of junior scholars, a low-ratio of senior-to-junior scholars, and a prevailing norm/expectation that all participants read all presented work prior to the conference), I am delighted to pass along the following (excerpted) information about the upcoming 2020 iteration of Arizona's successful QuantLaw conference from Jane Bambauer (Arizona).

"The 2020 QuantLaw conference will take place February 27-28 (Thursday and Friday) in Tucson, Arizona. QuantLaw is a unique conference for scholars who apply empirical methods to the study of law. The program is both rigorous and laid back. Part of the QuantLaw program involves standard presentations of works-in-progress by our keynote speakers, by our returning ["senior"] gurus, and by faculty members and graduate students from many different disciplines. The rest of the program is designed to be an incubator. We will have a structured conversation over dinner one night that focuses on a methodological or philosophical topic related to empirical legal studies, and we will also discuss brand new works-in-progress while hiking in the beautiful Sonoran Desert.

QuantLaw 2020 will feature programming around the theme of experiments: experimental methods, and how best to combine experimental and observational data to get a more accurate picture. Kathryn Zeller (Boston University) will present a keynote address.

If you would like to present new empirical work related to law or public policy, please prepare an extended abstract (approximately 500-1000 words in length) describing the topic, the tested hypothesis, the methods you plan to use, and whether you expect to have results to share at the conference. Send your extended abstract and a current CV to Prof. Jane Bambauer (click here for contact info) by November 30, 2019. We will give preference to projects using experimental methods given this year’s theme. We will also give preference to graduate students and junior faculty members. Applicants who are selected to present will receive boarding accommodations in Tucson."

Again, those with questions or seeking additional information should contact Prof. Bambauer directly.

Graphing Counterfactual Regression Trend Lines

In a recent Statlist discussion (here), the question was posed about how to: "generate a graph to show how to the GDP per capita trend line would have continued if the conflict [or any discrete event] had not occurred." (emphasis added) Put slightly differently, the question involves how to over-lay two separate time-trend lines: one from actual data reflecting the influence of a discrete event and another trend line reflecting a counterfactual regression trend (that is, as if the discrete event did not happen).

To illustrate, the graph (above) plots and compares "the effect that the civil conflict in Mozambique and Rwanda have had on the GDP per capita growth in the long run" and over-lays a projected regression line reflecting GDP per capita assuming that the civil conflict did not emerge in these two countries.

While the Stata graphics coding can be tricky, the end result is a quickly interpretable graph.

"Zeros" Problems

While not unique to empirical legal scholarship, how to deal with a "zeros problem" is not uncommon, especially given the nature of many research designs and questions in legal scholarship. To take one obvious--and well-trod--example, efforts to model punitive damage awards need to account for their absence in the over-whelming majority of civil cases decided by courts. A recent discussion on the StataList (here) provides yet another take on the array of challenges posed by "zeros" in data sets. The resultant discussions emphasizes, in part, that researchers need to assess with accuracy and clarity, ex ante, precisely what a "zero" means in the context of their data sets. As well, for a recent take on differences between Heckman selection and two-part models, a 2015 JELS paper (here), by Eisenberg et al., remains current.

The Harvard College Admissions Decision, The Import of Statistical Evidence, and Competing Modeling Assumptions

Focusing solely on the methodological aspect of the recent Harvard College admissions decision, one under-appreciated take-away from the district court's opinion (click here for the full decision) is the prominent role that traditional statistical analyses (basically, standard logistic models) played in the trial. If nothing else, the prominent role that statistical evidence bearing on the plaintiffs' claims, and Harvard's defense, played in the trial makes palpably clear, once again, the extent to which sophisticated litigation now--and almost routinely so in some case types--deploys empirical evidence. Indeed, the court's decision devotes more than 40 pages (pp.50-92) of its 130-page opinion to statistical matters.

In the Harvard admissions case, both parties to the litigation formally engaged experts, Professors David Card (Berkeley) and Peter Arcidiacono (Duke), to analyze a shared data set--six years worth of Harvard College admissions decisions. Despite a shared data set the two experts reached opposing conclusions on the critical question about the unique role that applicants' race/ethnicity played in Harvard's admissions decisions. Much of the experts' disagreement pivoted on one key modeling decision--whether to include or exclude admissions data germane to the "ALDC" applications. ("ALDC" applications are those involving: "recruited athletes, legacies, Dean's 'interest list' and children of Harvard faculty and staff.")

In the face of conflicting conclusions from the two experts, Judge Burroughs wrote that while she found both models “'econometrically defensible' and preferred different aspects of each model," in the end Judge Burroughs' decision largely preferenced Card's modeling assumptions over Arcidiacono's. Independent of whether one feels Judge Burroughs' assessment of the competing modeling decisions is persuasive, what is objectively clear is that she took the empirical and statistical evidence seriously and that it played a consequential--and potentially pivotal--role in the trial.