Empirical Legal Studies

Pro Se Civil Litigation in Federal Courts

Public and scholarly attention to pro se litigation is growing. In Mapping the Civil Justice Gap in Federal Court, Roger Michalski (Oklahoma) and Andrew Hammond (Oklahoma) provide a helpful empirical perspective.

Careful attention to pro se litigation is warranted. The sheer volume of pro se litigants will surprise many, if not most. According to the authors' calculations, last year 12 percent of the non-prisoner civil cases filed in federal district courts (more than 25,000 cases) were pro se. Aside from the scope of this issue, "pro se litigants raise unique practical, doctrinal, and normative concerns for the legal academy, the bar, and the federal bench.”

Not surprisingly, methodological challenges limit empirical assessments of pro se litigation. While the paper is appropriately mindful of these limitations, it nonetheless does not "let the perfect be the enemy of the good." As such, the paper analytically confines itself to describing "where pro se litigants live and identify the communities they inhabit." To be sure, while a modest contribution it nonetheless  adds to the knowledge base. That said, analytic limitations endure. "None of our claims speak to the characteristics of pro se litigants directly, only indirectly. This is an important limitation of this study and raises bias concerns. Bias might creep in because it is possible that pro se litigants are systematically atypical of the neighborhoods in which they live."

To accomplish its goals, and limitations aside, the paper exploits "a massive data set of 2.5 million federal dockets from a ten-year period, we obtained addresses of non-prisoner pro se litigants. We then geolocated these addresses and cross-referenced that information with demographic and economic Census data." After various data exclusions (e.g., prisoner suits and cases involving habeas, immigration/deportation cases, naturalization, and bankruptcy claims), what remained included approximately 20,000 federal civil cases per year.

The papers dwells on two core findings. One involves pro se litigants' "profound ordinariness." A second finding involves exceptions to the first. "Even after accounting for population and income, pro se litigants are more likely to reside in communities that are not homogenously white. And numerous rural communities feature fewer pro se litigants than expected.” An excerpted abstract follows.

"Unrepresented litigants make up a sizable and normatively important chunk of civil litigation in the federal courts. Despite their importance, we still know little about who these pro se litigants are. Debates about pro se litigation take place without sufficient empirical information. To help fill some of the gaps in our understanding of pro se litigants, this Article takes a new approach by mapping where pro se litigants live."

Graphics, Options, and Color

While Stata's graphics options (and related coding) have always struck me as unnecessarily complex, others seem to be inspired by them. While in the past "color options" involved monochromatic versus gray-scale, journals increasingly permit actual "color" options. Thus, to this end, I want to pass along a pair of related, though distinct, StataList discussions describing (with helpful coding)  various basic color options for box-plots and histograms.

Moving From Raw Data to More Helpful Variables

For many research projects scholars have to manipulate existing raw data into more useful derived variables. One obvious approach is "hand-coding," or some related form of Excel file manipulation. That is fine for folks experienced with Excel, but for others (including experienced Excel users) such a task typically invites some degree of human error. Another approach is to rely on Stata coding for variable creation. A recent (and, I hope, building conversation on the State List) illustrates a not an atypical example.

The questioner begins with Current Population Survey data (from the Census Dept.) that include individual-level information on, e.g., year, state, and a dummy variable for employment status (0=employed, 1 = unemployed, 2 = not in labor force). What the CPS data set does not include, however, is a state-level unemployment rate by year and state variable. So, the question is how to most efficiently extract what is desired (the creation of a new state-level unemployment rate by year and state variable) from what exists in raw data form. The discussion thus far has elicited at least one very helpful step-by-step examples of how to accomplish this goal with three lines of code (click here).

To be sure, other options also exist. That said, clear explications of how to move from what one has in terms of data to what one wants in terms of a new variable are sometimes hard to find.

Marginal Effects

While journals have traditionally focused on a key variable's direction and statistical significance, over time an increasing number of journal editors (and reviewers) ask for explications of a variable's substantive and "practical" influence. This trend is unlikely to abate anytime soon. And this is precisely where marginal effects (and Stata's margins command) come into play.

While no longer a new topic, I want to pass along the following resource, Using Stata’s Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects, by Richard Williams (Notre Dame--sociology). The document is, at once, accessible, thorough, and yet not over-bearing as it walks readers through the basics of the  margins command. The presentation is greatly enhanced by step-by-step illustrations and the necessary coding. In all, quite a helpful resource for those seeking a quick tutorial.