Empirical Legal Studies

FHA, Disparate Impact, and Statistical Evidence After TX Dept. of Housing

The Supreme Court's recent Texas Department of Housing and Community Affairs v. The Inclusive Communities Project decision makes clear the broad point that the Fair Housing Act allows causes of action based on disparate impact claims rooted in statistical evidence. More specifically, however, the Court held that “[d]isparate-impact liability mandates only the removal of artificial, arbitrary, and unnecessary barriers” and that it is “not an instrument to force housing authorities to reorder their priorities.” 

Consequently, what the decision means at a practical level remains anything but clear. Indeed, as William Consovoy observes at SCOTUSblog (here) "to defeat a disparate impact claim, housing authorities need only bring forth a 'valid interest served by their policies.' Furthermore, 'a disparate-impact claim that relies on a statistical disparity must fail if the plaintiff cannot point to a defendant’s policy or policies causing that disparity.' Indeed, the Court sent a strong signal that the disparate impact claim that gave rise to this dispute will have a difficult time moving forward under this standard."

CELS 2015 -- Call For Papers Deadline Extended

As previously mentioned, CELS 2015 will be held at Washington University Law School, in St. Louis, on Oct. 30-13, 2015. Conference organizers, Adam Badawi, Rebecca Hollander-Blumoff, and Pauline Kim recently extended the Call for Papers deadline to July 10, 2015. Click here for paper submission information.

Data "On-Request" v. "Open" Data

This is a fascinating and complex topic where the rules/norms appear to be shifting in real-time. Some of the complexities are aptly illustrated in a recent post on Andrew Gelman's blog (here).  (For those interested, even more valuable than the post itself is the growing list of comments that vividly illustrate this issue's potential complexities.)

"Defending" ELS

While I personally don't think such defenses remain necessary, I nonetheless note a recent response by Christina Boyd (Georgia--Poli Sci) in the Buffalo Law Review that takes up the task. In addition, Boyd's paper, In Defense of Empirical Legal Studies, includes a helpful summary of (and links to) a few of the leading publicly-available ELS-related data sets (with a poli sci tilt).

Lawyer Malpractice Insurance Data

Much of the empirical work exploring the relation between professional malpractice and insurance focuses on the medical context. Emerging research expands this focus to include lawyers' professional liability claims. In Liability Insurer Data as a Window on Lawyers’ Professional Liability, Tom Baker (Penn) and Rick Swedloff (Rutgers-Camden) exploits the "best publicly available data" and finds: 

"remarkable stability over thirty years in the distribution of claims by area of practice among both small and large firms, a large percentage of claims (64-70%) involving de minimus expense (less than $1000) in the small firm market, and in the large firm market a declining rate of 'real claims' per 1000 lawyers, a declining rate of real average gross loss per claim, and stable real premiums per lawyer since the early 1990s."

The authors properly caution readers about data limitations that restrict their findings' generalizability.

Social Science & Fabricated Data

Even the idea of fabricated data strikes serious academics as both absurd and profoundly offensive. A recent example involving survey research and a now-retracted Science publication (discussed here and here) serves as another regrettable reminder that, as I read somewhere, "In God we trust; all others bring data." If there is any silver lining to this sordid affair, it involves the scam's unraveling by graduate students seeking to replicate the original (bogus and discredited) findings.

UPDATE: Michael LaCour's response (here).