Empirical Legal Studies

Side-By-Side vs. Overlayed Plots

At the risk of delving too deeply into Stata's graphics coding "weeds," two different ways to present the same graphs help illustrate graphing possibilities.

By convention, a "side-by-side" presentation of two distinct, though related, plots (with confidence intervals) in Stata looks like the following:

If the plots are snug enough in how they relate, however, additional coding can produce a single "overlayed" graph:

For those intrigued, the coding underneath these two possibilities is included here.

Federal Judicial Data: Greater Transparency or Opacity?

While not itself an empirical paper per se, The Necessary and Proper Stewardship of Judicial Data, by Zachary Clopton (Northwestern) and Aziz Huq (Chicago), engages in a discussion near-and-dear to most legal empiricists' heart. Specifically, their paper sets out to develop "the first comprehensive descriptive, doctrinal, and normative analysis of judicial data.”

As the authors note, judicial (especially Art. III courts) data are "valuable in governance and for those interested in understanding how they are being governed. In the absence of hurdles or walls, it can illuminate the operation of one branch of the national government. Constitutional lapses can be brought to light through retail or wholesale data." As such, data flowing from Art. III courts and institutions can promote various public goods by, in part, advancing a better understanding of, or improvements to, judicial institutions.

One critical anomaly is that, as compared to the legislative and Executive branches, judicial branch data remain "largely terra incognito for scholars and the general public alike. Instead, public attention rushes to high-profile cases of leaked Supreme Court opinions and private influence campaigns.” While why this is so remains unclear (to me, anyway), that it is so, however, is obvious to any empirical legal scholar who labors and writes in this space. And the stakes incident to the "transparency v. opacity" debate grow more acute as new scholarly tools emerge (e.g., natural language processessing, text-scrapping).

As the authors make clear, under current policy and practice judicial data "might be lost, dissipating into air, or kept under wraps, never yielding up their secrets." In contrast, a commitment to greater transparency invites characterizing judicial data as a "new public asset that can then be tapped to create public goods for the benefit of all."

School and Crime; Cause and Effect

Criminologists increasingly worry about a "school-to-prison pipeline" problem. Such worries are typically inflamed incident to public school districts' typical reaction (or, to some, over-reaction) to tragic episodes of student violence, especially mass-violence. An increasing number of empirical efforts seek to identify and better understand causal mechanisms. To this end, a recent paper by Todd Jones (Miss. St.-econ) and Ezra Karger (Fed. Res.-Chicago), School and Crime, exploits data from the National Crime Victimization Survey and explores "seasonality" patterns.

As it relates to "seasonality" patterns, the paper's main take-away is that juvenile criminal activity in general, and student-on-student crime in particular, largely maps onto the school calendar. More specifically, the paper documents a population-level outcome: across all children—those who are compliers for education-based policies and those who are not—attending school causes an average increase in the likelihood of student-on-student crime. While the overall patter is suggestive, the underlying mechanisms are less so. That is, whether a school's institutional setting itself (e.g., institutional-level policies and practices)--or, for example, that student-on-student crime is simply a function of student density levels--is the critical mechanism remains unclear. The paper's abstract follows.

“Criminal activity is seasonal, peaking in the summer and declining through the winter. We provide the first evidence that arrests of children and reported crimes involving children follow a different pattern: peaking during the school year and declining in the summer. We use a regression discontinuity design surrounding the exact start and end dates of the school year to show that this pattern is caused by school: children aged 10–17 are roughly 50% more likely to be involved in a reported crime during the beginning of the school year relative to the weeks before school begins. This sharp increase is driven by student-on-student crimes occurring in school and during school hours. We use the timing of these patterns and a seasonal adjustment to argue that school increases reported crime rates (and arrests) involving 10–17-year-old offenders by 47% (41%) annually relative to a counterfactual where crime rates follow typical seasonal patterns. School exacerbates preexisting sex-based and race-based inequality in reported crime and arrest rates, increasing both the Black-white and male-female gap in reported juvenile crime and arrest rates by more than 40%.”

New Stata Graphing "Toys"

While Stata's graphing capabilities are robust, coding remains complex in places. While the coding "load" has come down a bit over the years, the "bells & whistles" have remained relatively stagnant over the years. One recent change on the "bells & whistles" front involves an increased capacity to "colorize" standard graphs. While the example (below) illustrates how levering color in graphics can enhance (and/or clarify) what the data are telling you, I remain curious about how journals (and their publishers) will choose to deal with the increased production costs associated with color in published papers.

Any potential cost issues aside, a recent Stata newsletter (click here) explains and illustrates (with sample coding) how one can transform a standard twoway scatter plot from this:

To this: