Empirical Legal Studies

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.

Data on POTUS Pardons

Discomfort with POTUS pardon activity seems to arise anew as administrations (Dem and Rep) turn over and POTUS power transfers. And this discomfort has likely increased over time. While the Constitution’s language in Article II, section 2 regarding the scope of executive clemency and the limitations upon its exercise is quite brief, it bestows upon the POTUS functionally unchecked pardon authority. Insofar as judicial review of POTUS pardon authority is virtually non-existent, the exact legal contours of POTUS authority in this space remain opaque.

How POTUS pardon activity distributes in this zone of potentially nonrenewable Art. II authority is the subject of a recent (and lengthy) RAND report, Statistical Analysis of Presidential Pardons. Distilling their results to a single point, the authors converge on the following, perhaps unremarkable, finding. “If one had to describe a petitioner in our analysis case files more likely to receive a presidential grant of pardon than others (and to do so without controlling for other petitioner and evaluation process characteristics), Table S.2 suggests it would have been a non-Hispanic white male who was a U.S.-born citizen and was in his late twenties when the underlying offense (a white-collar crime such those involving tax violations, embezzlement, forgeries, or counterfeiting) was committed.” The report's abstract follows.

“This report describes a study of pardon petition evaluations that were performed by the Office of the Pardon Attorney (OPA), a unit in the U.S. Department of Justice that processes thousands of requests for executive clemency each year. A statistical examination was conducted on how OPA screens incoming petitions for technical compliance, investigates the facts underlying each petitioner’s request for clemency, and applies substantive standards for evaluating the merits of such petitions when crafting recommendations to the U.S. President to grant or deny relief. Data were analyzed for any patterns in OPA decisions that indicated statistically significant evidence of racial or ethnic bias.”