Statistics and the Law

Harvard Law Professor Elmer Elhauge:

In short, in law, we are currently still largely in the position of the baseball scouts lampooned so effectively in Moneyball for their reliance on traditional beliefs that had no empirical foundation. But all this is changing. At Harvard Law School, as traditional a place as you can get, we now have by my count 10 professors who have done significant statistical analysis of legal issues. We just hired our first JD with a PhD in statistics. The movement is not at all limited to Harvard, and seems to be growing at all law schools.

So we are hardly devoid of empirical analysis of law. We are just, rather, in our early Bill James era, and can expect the analysis to get more sophisticated and systematic as things progress. I expect within a couple of decades we will have our own book distilling the highlights of things we will know then that conflict with what is now conventional legal wisdom.

Mike Kellermann adds:

We are all pretty pleased that Harvard Law now has a stats Ph.D. on faculty. But one of the commenters raises an interesting question; if empirical legal studies are like sabermetrics, who is the legal equivalent of Joe Morgan?

Great question!  As I prepare to start law school this fall, I find these thoughts very intriguing.  Law and Economics is a growing field and one of GMU's specialties.  Jonathan Klick, a GMU alumni with both a PhD in econ and a JD, has made a stellar career by publishing many econometric studies of legal issues.  This underscores the need for me to try to develop better quantitative skills before leaving school.  I'm also interested in seeing if there are any applications for agent-based models in legal scholarship.  I'm particularly interested in areas of religious freedom and technology/patent law and the impact both of these have on economic growth.

Listen to this EconTalk podcast with Russ Roberts and Michael Lewis (author of Moneyball) on the economics of baseball for related thoughts.  Also see my post the difference between econometrics and statistics.