BL Partners Study (RED) Rep Feedback Loop (BLUE) (SEE OP)
Posted: Wed Mar 25, 2015 7:15 pm
The moderators have taken over the thread explaining my theory of a reputational positive feedback looped and merged it with my thread on biglaw partners. I don't understand why, but this is the way they want it done. Therefore:
To address topic 1, my posts will be in RED. I encourage you to post in RED for clearness.
To address topic 2, my posts will be in BLUE. I encourage you to post in BLUE for clearness.
On behalf of those responsible, my apologies for this inconvenience.
Original title: What schools are best for biglaw? A methodical approach
US News rankings are increasingly unreliable. I have seen some biglaw proximity studies posted on the board and they have begun to change some minds about relying on US News rankings. I submit to the board another study, done by Theodore P. Seto (Harvard Law School, magna cum laude)
The results you are about to see deal with what schools give you the best shot at being partner. This is important because partnership prospects are a LONG TERM goal. So these rankings give you a better picture of LONG TERM placement ability. 9 month rankings are good but not as useful because they only focus on the short term.
Additionally, partnership means you have certain exceptional skills, otherwise you would not become partner. The US News rankings put certain schools at the very top year after year, and those schools may have fabulous placement. However, do they hone those exceptional skills required for a partnership track. Study the data and come to your own conclusions.
Without further adieu, here are the rankings...
1 Harvard
2 Georgetown
3 NYU
4 Virginia
5 Columbia
6 George Washington
7 Michigan
8 Chicago
9 Texas
10 Northwestern
11 Pennsylvania
12 Boston University
13 Fordham
14 UC Berkeley
Link - http://www.swlaw.edu/pdfs/jle/jle622seto.pdf
Original title: School Reputation and the Positive Feedback Loop (is S > Y?)
I watched with amusement as TLS held its collective breath on the eve of the new US News Rankings. Polls were made, contests were held, every subtle shift was analyzed, debated, poked and prodded. It was truly a spectacle to behold. But I soon wondered – isn't this exactly why the same schools appear again and again at the very top of the rankings.
What am I talking about – let me give you an simple illustration. Imagine Mr. X reads the rankings, which change his perception of the various law schools. The very high ranking for school Y makes him believe Y is the best school in the country. This perception endures as he enters law school, and endures after he graduates. Then, in practice, he is asked to participate in the US News poll as part of the reputational score. Because of that earlier exposure to the rankings, he still believes Y is the best school and votes accordingly. This is factored into the reputational score, and thus, US News puts Y at the top again. As I learned about in economics, this is called a positive feedback loop.
You may wonder – how can I break this positive feedback loop. Simple – stop focusing on the reputational score and library book count, and start looking at important things like selectivity, employment, and salaries. Sure, you could break out Excel and calculate this all for yourself. But instead, a study has done this for you, which I submit to the board for its consideration today.
http://tippingthescales.com/2013/10/our ... schools/3/
1. Stanford
2. Yale
3. Harvard
4. UPenn
5. Columbia
6. Duke
7. Northwestern
8. Berkeley
9. Virginia
10, Michigan
11. Chicago
12. New York
13. GW
14. Cornell
You will immediately notice that many different factors are in play, all of them relevant. No library books. No expenses per student. No reputational score. This is purely objective metrics like jobs at graduation, and median private salary. You will also notice that certain schools, like Yale and Georgetown, which get inflated rankings because of "prestige", have dropped once you start looking at purely objective measurements. Certain other schools have risen, in some cases considerably so. I submit that if TLS is going to focus on a T14, it ought to be a T14 of employment and salary, not a T14 of library size and faculty-student ratio.
To address topic 1, my posts will be in RED. I encourage you to post in RED for clearness.
To address topic 2, my posts will be in BLUE. I encourage you to post in BLUE for clearness.
On behalf of those responsible, my apologies for this inconvenience.
Original title: What schools are best for biglaw? A methodical approach
US News rankings are increasingly unreliable. I have seen some biglaw proximity studies posted on the board and they have begun to change some minds about relying on US News rankings. I submit to the board another study, done by Theodore P. Seto (Harvard Law School, magna cum laude)
The results you are about to see deal with what schools give you the best shot at being partner. This is important because partnership prospects are a LONG TERM goal. So these rankings give you a better picture of LONG TERM placement ability. 9 month rankings are good but not as useful because they only focus on the short term.
Additionally, partnership means you have certain exceptional skills, otherwise you would not become partner. The US News rankings put certain schools at the very top year after year, and those schools may have fabulous placement. However, do they hone those exceptional skills required for a partnership track. Study the data and come to your own conclusions.
Without further adieu, here are the rankings...
1 Harvard
2 Georgetown
3 NYU
4 Virginia
5 Columbia
6 George Washington
7 Michigan
8 Chicago
9 Texas
10 Northwestern
11 Pennsylvania
12 Boston University
13 Fordham
14 UC Berkeley
Link - http://www.swlaw.edu/pdfs/jle/jle622seto.pdf
Original title: School Reputation and the Positive Feedback Loop (is S > Y?)
I watched with amusement as TLS held its collective breath on the eve of the new US News Rankings. Polls were made, contests were held, every subtle shift was analyzed, debated, poked and prodded. It was truly a spectacle to behold. But I soon wondered – isn't this exactly why the same schools appear again and again at the very top of the rankings.
What am I talking about – let me give you an simple illustration. Imagine Mr. X reads the rankings, which change his perception of the various law schools. The very high ranking for school Y makes him believe Y is the best school in the country. This perception endures as he enters law school, and endures after he graduates. Then, in practice, he is asked to participate in the US News poll as part of the reputational score. Because of that earlier exposure to the rankings, he still believes Y is the best school and votes accordingly. This is factored into the reputational score, and thus, US News puts Y at the top again. As I learned about in economics, this is called a positive feedback loop.
You may wonder – how can I break this positive feedback loop. Simple – stop focusing on the reputational score and library book count, and start looking at important things like selectivity, employment, and salaries. Sure, you could break out Excel and calculate this all for yourself. But instead, a study has done this for you, which I submit to the board for its consideration today.
http://tippingthescales.com/2013/10/our ... schools/3/
1. Stanford
2. Yale
3. Harvard
4. UPenn
5. Columbia
6. Duke
7. Northwestern
8. Berkeley
9. Virginia
10, Michigan
11. Chicago
12. New York
13. GW
14. Cornell
You will immediately notice that many different factors are in play, all of them relevant. No library books. No expenses per student. No reputational score. This is purely objective metrics like jobs at graduation, and median private salary. You will also notice that certain schools, like Yale and Georgetown, which get inflated rankings because of "prestige", have dropped once you start looking at purely objective measurements. Certain other schools have risen, in some cases considerably so. I submit that if TLS is going to focus on a T14, it ought to be a T14 of employment and salary, not a T14 of library size and faculty-student ratio.
