February 07, 2017
The proposal would have required that 75% of graduates taking the bar pass within two years of graduation. I suspect in a Trump Administration, there will be less danger of the ABA losing its accreditation role, but I can imagine a more aggressive Education Department in the future wondering what the explanation could be for rejecting such a standard.
More details here.
February 04, 2017
Judge Gorsuch should speak out in defense of judicial independence in light of Trump's latest disgraceful behavior
February 03, 2017
Deans of 20 ABA-approved law schools in California call on California Supreme Court to intervene and reset the scores for bar passage
February 02, 2017
...but founding the "Fascism Forever Club" does raise questions about one's judgment, even allowing for age!
(Thanks to Michael Swanson for the pointer.)
ADDENDUM: It appears Judge Gorsuch attended a high school run by quite liberal Jesuits (unlike the late Justice Scalia who went to a famously conservative Jesuit high school in New York). I imagine his liberal teachers tended to deride conservatives as "fascists," ergo the conservative students decided to "zing" them back!
ANOTHER: This story confirms that it was, indeed, a joke (and not even an actual club).
January 31, 2017
Brad Hillis called this data compilation he did to my attention; I haven't verified its accuracy, but the recent (2005-17) data looks roughly right. Readers can weigh in at Wikipedia to correct the data if need be. Neither list is adjusted for class size.
Here are the twenty law schools that have produced the most Supreme Court clerks since 1882:
Rank/ Law School/ # clerks / % of all clerks
1) Harvard 607 27%
2) Yale 396 18%
3) Chicago 156 7%
4) Stanford 137 6%
5) Columbia 135 6%
6) Virginia 110 5%
7) Michigan 87 4%
8) Georgetown 61 3%
9) Berkeley 59 3%
10) NYU 54 2%
11) Penn 48
12) Northwestern 42
13) Texas 35
14) GW 26
15) Duke 21
16) UCLA 19
17) Notre Dame-17
18) BYU 13
19) Indiana 11
And here is Mr. Hillis's list of the top 20 law schools which have produced the most clerks since 2005 through 2017 (again, note that Harvard is more than twice the size of Yale, Stanford, and Chicago; that Virginia, Columbia, and NYU are about twice the size of the latter; etc.):
January 27, 2017
Some want to play an "indispensable" role in the search for a new Dean. I'm sure student feedback on candidates will receive some weight, but that's about it. Were I a betting man (I am not), I would bet on John Goldberg or John Manning--both current HLS faculty--to be chosen as the new Dean.
January 23, 2017
January 19, 2017
UPDATED: MOVING TO FRONT FROM YESTERDAY
Here's the report:
As of 1/6/17, there are 134,007 applications submitted by 21,711 applicants for the 2017–2018 academic year. Applicants are down 4.2% and applications are down 2.2% from 2016–2017.
Last year at this time, we had 40% of the preliminary final applicant count.
Although there has been a trend towards increasingly later applications, this figure does suggest that we are going to see a slight, but not negligible, decline in applicants this cycle.
UPDATE: But now LSAC reports that LSAT-takers in December were up nearly 8% from the prior year! The likely explanation though, is a scheduling change, which led more applicants to skip the early fall LSAT in favor of the December one. But that would also account for the decline in applicants noted in the 1/6/17 report. So my guess now is that we won't be seeing any decline in the applicant pool this year, so we really are at "the new normal."
Established datasets, proxies, and customized data collection: The case of international LLMs (Michael Simkovic)
How should researchers make tradeoffs between the costs of data collection, the speed of the analysis, the precision of the measurements, reproducibility by other researchers, and broader context about the meaning of the data: how we might compare one group or one course of action to another, how we might understand historical trends, and the like?
Must we always measure the precise group of interest, with zero tolerance for over-inclusion or under-inclusion? Or might one or a series of proxy groups be sufficient, or even preferable for some purposes? What if the proxies have substantial overlap with the groups of interest and biases introduced by use of proxy groups are reasonably well understood? How close must the proxy group be to the group of interest?
These are important questions raised by a group of legal profession researchers which includes several of the principal investigators of the widely used After the JD dataset.
Professors Carole Silver, Ethan Michelson, Robert Nelson, Nancy Reichman, Rebecca Sandefur, and Joyce Sterling (hereinafter, Silver et al.) recently wrote a three-part response (Parts 1, 2, and 3) to my two-part blog post from December about International LLM students who remain in the United States (Part 1) and International LLM students who return to their home countries (Part 2). The bulk of Silver et al.’s critique appears in Part 2 of their post, and focuses mainly on Part 1 of my LLM post.
My post, which I described as “a very preliminarily, quick analysis intended primarily to satisfy my own curiosity” used U.S. Census data from the American Community Survey and two proxy groups for international LLM (“Masters of Law”) graduates to make inferences about the financial benefits of LLM degrees to international students who remain in the U.S. Silver et al. agree with several of the limitations of this analysis that I noted in paragraphs 5 through 8 of Part 1 of my post. They also note that historically, many LLMs have returned to their home countries and argue that the benefits of LLM programs to returning students may be greater than the benefits to those who remain in the United States. (While I am skeptical of this last claim—especially if we focus exclusively on pecuniary benefits—it seems likely that both groups benefit).
Silver et al. have also helpfully made several additional points about limitations in my proxy approach and ways in which proxies could over-count or under-count foreign LLMs. The most important of these limitations can be addressed with a few modifications to the LLM proxy group approach. Those interested in the technical details are encouraged to read footnote 1 below.
Returning to broader questions about the use of proxy groups, my view is that proxy groups can be helpful and potentially necessary for certain kinds of analysis.
Suppose that we wish to know the temperature in New York’s Central Park before we take a stroll, but we only have temperature readings for LaGuardia and Newark airport. While neither of those proxies will tell us the precise temperature in Central Park, they will usually be sufficiently close that we can ascertain with a reasonable degree of certainty whether we should bring our winter coats, wear sweaters, or proceed with short sleeves. Indeed, readings from Boston or Philadelphia will probably suffice, particularly if we’re aware of the direction and magnitude of typical temperature differences relative to Central Park.
Should we refuse to venture out until we can obtain a temperature reading from Central Park itself?