Those who recall Avvo‘s launch, nearly one year ago, will no doubt remember the chorus of lawyers aghast that we would dare to rate members of the legal profession. We hear little of that these days, as our approach has become accepted – if not always welcomed – within the legal community.
In the spirit of those appalled that legal practitioners could be rated, I was amused to come across this University of Chicago Law School paper that sets forth a methodology for ranking state supreme courts (and compares it with the surprising number of other recent rankings of such courts).
Quelle horreur - How can one possibly rank the complex work undertaken by these lofty collectives of jurists? Easy, if you’re a bunch of law and econ guys: Create a ranking algorithm based on productivity (number of opinions published), influence (on other courts and academia) and independence (from partisan pressures).
Interestingly, the authors had this to say about the utility of rankings:
“The alternative to rankings is, as a practical matter, virtually no information, and public institutions that are not carefully monitored and evaluated will rarely have strong incentives to perform well. Rankings, however imperfect, serve an important information-forcing function. Institutions that do poorly on rankings should have the burden of coming forth with an explanation for their performance; but if the explanation is plausible, then the ranking should be discounted.”
That’s lot like how we think about the Avvo Rating – a good place to start one’s search for a lawyer, but not the end-all-be-all in making the decision.
So how did the states do? Our authors’ top 10:
Arkansas
California
North Dakota
Montana
Ohio
Georgia
Mississippi
Massachusetts
Rhode Island
New York
Sad to see our Washington court, which finished no worse than seventh in the other three rankings, didn’t make the list. Read the paper for the in-depth discussion of why using an unbiased algorithm provides a more balanced picture than the traditional ratings, which typically focused on only one of the algorithm’s three factors.
