Empirical Legal Studies

Simple Barcharts Options in Stata's Complex Graphics Coding

While I obviously should have stumbled onto this earlier, I'll continue to ascribe it to Stata's unnecessarily (in my view) complicated graphics coding. Indeed, complications hamstring even the "simple stuff."

The example below involves "style" elements incident to the standard, simple barchart. The conventional "graph hbar" command generates the "typical" barchart (on the left). The "statplot" command (user-written, requires downloading), however, generates (to my eyes, anyway) the slightly less "cluttered" and more "intuitive" barchart on the right. (Click here for additional information on "statplot".)

To be sure, this admittedly subtle distinction pivots on mere aesthetics and, of course, neither barchart chart is "wrong" in any way. They simply convey needed information in slightly different ways. But if I took anything from Tufte's classic exposition, The Visual Display of Quantitative Information, at some point "mere aesthetics" can matter. Or, if not "matter," can at the very least help or improve.

Legal Impact Research & Evidence of Racial Profiling

Far too many legal and court scholars (as well as law students, lawyers, judges, etc.) simply assume, at least as a presumptive matter, that court decisions yield the outcomes they seek. Not enough recognize how institutional arrangements, incentives, the "law of unintended consequences," etc., inform how court decisions are implemented "on the ground." A recent paper, An Empirical Assessment of Pretextual Stops and Racial Profiling, by Stephen Rushin (Loyola-Chicago) and Griffin Edwards (UAB-Economics), provides yet another helpful and timely reminder of the efficacy of subjecting formal legal doctrines to data and rigorous research designs.

In their paper the authors exploit a change in Washington State law concerning police traffic stops. While a 1999 Washington State Supreme Court decision (Ladson) barred pretextual traffic stops in 2012 the Washington Court reversed course (Arreola) by permitting "mixed motive" traffic stops. Varying this legal rule opened a door for a DID research design that draws on 8.2M traffic stops in Washington State between 2008 and 2015. And what the results suggest is that the Arreola court decision "was associated with a statistically significant increase in traffic stops and searches of nonwhite drivers relative to white drivers. We find that most of the increase in traffic stop of non-white drivers after Arreola occurred during the daytime, when police officers could more easily ascertain a driver’s race." Overall, this paper will interest not only because of the massive dataset but also for its empirical specification.

Standard (OLS) Regression for a *Binary* Dependant Variable?

Evidently, the question is not as crazy as I likely would have assumed 40, 30, or, perhaps, even 10 years ago. In a widely-discussed draft paper (click here), Logistic or linear? Estimating causal effects of treatments on binary outcomes using regression analysis, Robin Gomila (Princeton--(grad student) social psych) makes a case for OLS. While Andrew Gelman (Columbia--statistics) is among those who disagree (here), he also recommends alternative parallel suggestions and approaches. To the extent that the Gomila paper is partly motivated by the sometimes "muddled" interpretation of coefficients after a logistic transformation, my quick sense is that altering the empirical strategy risks a potential over-reaction.

New Goodness of Fit ("GOF") Test

Testing whether two separate distributions systematically vary typically involves a t-test or ksmirnov test. These two popular GOF tests involve comparing two distributions' means (t-test) or a hypothesized equal distribution (ksmirnov). A recently-released user-written command in Stata, distcomp, moves the needle a bit for GOF testing by assessing the equality of the distribution functions point-by-point. That is, instead of a single rejection (or non-rejection), the distcomp test generates ranges of values in which the two distributions' difference may achieve statistical significance.

To locate and download distcomp use the "findit" or "ssc install" functions in Stata. For a much more thorough explanation and description of the distcomp command click here.