Empirical Legal Studies

Research Design & Twins

By addressing important causation challenges, identical twins provide unique research design opportunities. A well-designed study exploiting identical twins facilitates the need to control for unobservables--including as an important subset, genetic endowments--in analyses that explore the impact of one variable on outcomes of interest. An article in today's NYT (here) describes NASA's current effort to increase understanding of the effects of living in space by studying identical twin astronauts, one of whom will soon return from a full year aboard the International Space Station.

A Helpful Primer on Data Archives

The good folks at UCLA maintain an exceptionally rich and deep collection of online resources germane to ELS. Insofar as much secondary analysis involves pulling data stored at ICPSR, this web-based seminar might interest. While it relates specifically to UCLA's data archive, the general points apply to most data archives, including ICPSR.

Visualizing (and Presenting) Data

While Tufte's classic, A Visual Display of Quantitative Information, endures as the gold-standard, similar efforts, such as Schwabish's more recent An Economist's Guide to Visualizing Data, warrant a look as well. While most agree with Schwabish's general points: “show the data,” “reduce the clutter,” and “integrate the text and the graph," others differ on some of his applications of the points.

Chair in Empirical Legal Studies

In a wonderful sign of the times, a leading research university in Wales (UK) seeks to fill a Chair devoted to ELS. "Swansea University College of Law wishes to further augment its interdisciplinary research through the appointment of a Chair in Empirical Legal Studies. We seek a research leader with training in criminology, social sciences and law whose work is focused in quantitative methods as they are applied to law, institutions of criminal justice, and human behaviour." Further information can be found here. Inquiries should be directed to the Head of College, Prof. John Linarelli (at: [email protected]). Please note that the application deadline is 16 April 2014.

2014 Summer Causal Inference Workshops: Main and Advanced

Information on two separate (Main and Advanced) Causal Inference Workshops at Northwestern Law School this summer follows. Both workshops will be taught by leading causal inference researchers. Registration for each is limited to 100 participants.

Main workshop: Monday – Friday, July 7-11, 2014 [at Northwestern]

Advanced workshop: Wednesday - Friday, August 13-15, 2014 [at Duke]

For information and to register for either or both workshops: (click here)

 

Main Workshop Overview and Target Audience: Most empirical methods courses survey a variety of methods. We will begin instead with the goal of causal inference, and discuss how to design research to come closer to that goal. The methods are often adapted to a particular study. Some of the methods we will discuss are covered in PhD programs, but rarely in depth, and rarely with a focus on causal inference and on which methods to prefer for messy, real-world datasets with limited sample sizes. Each day will include with a Stata “workshop” to illustrate selected methods with real data and Stata code. We will assume knowledge, at the level of an upper-level college econometrics or similar course,of multivariate regression, including OLS, logit, and probit; basic probability and statistics including conditional and compound probabilities, confidence intervals, t-statistics, and standard errors; and some understanding of instrumental variables.

 

Advanced Workshop Overview and Target Audience: The advanced workshop seeks to provide an in-depth discussion of selected topics at the causal inference research frontier.Our target audience is empirical researchers who are familiar with the basics of causal inference (from our main workshop or otherwise), and want to extend their knowledge.

Calls For Open Data Policies in Patent Law

Building off of Johnson & Johnson's recent decision to make all of its clinical data available to scientists around the world (favorable New York Time op-ed here), Dave Schwartz (Chicago-Kent) and co-authors make the case (here) that this same impulse should extend to empirical legal studies as well, particularly studies of patent assertion entities ("PAE"). An excerpt follows.

"Why is more information about PAE litigation not public? After all, the underlying data relates to litigation in the federal courts, and thus does not implicate privacy concerns like in the medical context. However, most of the raw data has been gathered and coded by private companies. For-profit businesses legitimately desire to use the information within their business and to prevent competitors and others from using commercially valuable information. That said, we believe corporate owners should release as much of the raw data (not merely descriptive statistics) as they can. To the extent that the raw data is not released or shared, society should be extremely cautious before relying upon it to make important public policy decisions."