Modeling and projecting USNWR law school rankings under new methodologies

I mused earlier about the “endgame” for law schools “boycotting” the rankings. It’s apparent now that USNWR will not abandon the rankings, and it’s quite unclear (and I would guess doubtful) that these rankings with different metrics will be less influential to prospective law students or employers than the past. But the methodology will change. What might that mean for law schools?

I assume before they made a decision to boycott, law schools modeled some potential results from the boycott to determine what effect it may have on the rankings. We have greater clarity now than those schools did before the boycott, and we can model a little bit better some of the potential effects we’ll see in a couple of months. I developed some models to look at (and brace for) the potential upcoming landscape.

I’ve talked with plenty of people at other schools who are privately developing their own models. That’s great for them, but I wanted to give something public facing, and to plant a marker to see how right—or more likely, how wrong!—I am come spring. (And believe me, if I’m wrong, I’ll write about it!)

First, the criteria. USNWR disclosed that it would no longer use privately-collected data and instead rely exclusively on publicly-available data, with the exception of its reputational survey data. (You can see what Dean Paul Caron has aggregated on the topic for more.) It’s not clear whether USNWR will rely on public data other than the ABA data. It’s also not clear whether it will introduce new metrics. It’s given some indications that it will reduce the weight of the reputational survey data, and it will increase the weight of output metrics.

The next step is to “model” those results. (Model is really just a fancy word for educated guess.) I thought about several different ways of doing it before settling on these five (without knowing what the results of the models would entail).

	Current weight	Model A	Model B	Model C	Model D	Model E
Peer score	0.25	0.225	0.225	0.225	0.2	0.15
Lawyer/judge score	0.15	0.125	0.125	0.125	0.1	0.1
UGPA	0.0875	0.09	0.09	0.09	0.1	0.1
LSAT	0.1125	0.12	0.13	0.16	0.17	0.15
Acceptance rate	0.01	0.02	0.03	0.02	0.03	0.03
First-time bar passage	0.03	0.05	0.1	0.07	0.05	0.12
10 month employment rate	0.14	0.3	0.25	0.23	0.25	0.3
Student/faculty ratio	0.02	0.04	0.04	0.03	0.05	0.05
Librarian ratio*	0.01	0.01	0.01	0	0	0
Ultimate bar passage	0	0.02	0	0.05	0.05	0
Other factors	0.19	0	0	0	0	0

Note that at least 19% of the old methodology is being cut out of the new methodology. Note, too, that there’s some diminished weight to, at least, the peer score and the lawyer/judge score. That means these categories have to be made up somewhere else. There’s only so much pie, and a lot of pieces simply have to get bigger. Despite the purported focus on outputs, I think some increased weight on inputs will be inevitable (absent significant new criteria being added).

I added a potential category of “ultimate bar passage” rate in three of the five models. It’s a possible output that USNWR may adopt, as it is based on publicly-available information and uses outputs, something USNWR has said it intends to rely upon more heavily.

I also added a “librarian ratio” in two of the five models. But it’s a different one from the existing librarian ratio. USNWR has indicated it will not use its internal library resources question (which was a kind of proprietary calculation of library resources), but it has not indicated that it would not use a student-faculty ratio equivalent for full time and part time librarians, so I created that factor in two of the five models.

If I had to guess, I would guess more minimal adjustments are most likely, highlighted more by Models A & B, but I think there is certainly the possibility for more significant changes, as I highlighted in Models C, D, & E.

Crucially, in all of them, the 10-month employment metric is significantly increased in all models, an assumption that may be wrong, but one that also increases some “responsiveness” (read: volatility) in the rankings, as highlighted below. (I also had to reverse-engineer the weights for the employment metric, which may be in error, and which could change beyond the changes USNWR has presently indicated.) This is one of the most uncertain categories (and the most likely I erred in these predictions), particularly given how much weight it receives in almost any new model. It is also likely going to be the most significant thing law schools can do to move their rankings year to year. If you are wondering how or why a school moved significantly, it is likely attributable to this factor. Getting every graduate into some kind of employment is crucial for success.

I used last year’s peer and lawyer/judge scores, given how similar they tend to be over the years, but with one wrinkle. On the peer scores, I reduced any publicly “boycotting” schools’ peer score by 0.1. I assume that the refusal to submit peer reputational surveys from the home institution (or, perhaps, the refusal of USNWR to count those surveys) puts the school at a mild disadvantage on this metric. I do not know that it means 0.1 less for every school (and there are other variables every year, of course). I just made it an assumption for the models (which of course may well be wrong!). Last year, 69% of survey recipients responded, so among ~500 respondents, the loss of around 1% of respondents, even if quite favorable to the responding institution, would typically not alter the survey average. But as more respondents remove themselves (at least 14% have suggested publicly they will, with others perhaps privately doing so), each respondent’s importance increases. It’s not clear how USNWR will handle the reduced response rate. This adds just enough volatility, in my judgment, to justify the small downgrade.

Next, I ran all the data from the schools, scaled them, weighed them, and ranked them. These are five different models, and they led to five different sets of rankings (unsurprisingly). I then chose the median ranking of each school among the five models. (So the median for any one school could be from any one of the models.)

Let me add one note. From these five different models, there was very little variance between them, despite some significant differences in the weighting. Why is that? Well, many of these items are highly correlated with one another, so adjusting the weights actually affects relatively little. The lower you go, however, the more compressed the rankings are, and the more volatile even small changes can be.

You’ll also note little change for most schools, perhaps no more than a typical year’s ups and downs. Unless the factors removed or given reduced weight worked as a group to favor or disfavor a school, we aren’t likely to see much change.

One last step was to offer a potential high-low range among the rankings. For each of the five models, I gave each school a rank one step up and one step down, to suggest some degree of uncertainty in how USNWR calculates, for instance, the 10-month employment positions, or diploma privilege admission for bar passage, among other things. That gave me 15 potential rankings—a low, projected, and high among each of the five models. I took the lowest of the low and the highest of the high for a projected range. With high degrees of uncertainty, this range is an important caveat.

Below are my projections. (Again, it’s apparent a lot of schools are doing this privately, so I’ll just do one publicly for all to see, share, and, of course, critique. I accidentally switched a couple of schools in a recent preview of this ranking, so I’ve tried to double check as often as I can to ensure the columns are accurate!) Schools should not be overly worried or joyful at these rankings (or any rankings), but they should manage expectations about how they might handle any changes with appropriate stakeholders in the months ahead. Because these are projected “median” rankings, it will not cleanly add up in a true rank order relative to one another (e.g., two schools are projected at 10, and one school is projected at 11).

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School	Median projected rank	Projected range		Current rank
Yale	1	1	3	1
Stanford	1	1	3	2
Chicago	3	1	4	3
Harvard	4	3	6	4
Penn	5	3	8	6
Columbia	6	4	8	4
NYU	6	4	8	7
Virginia	8	5	9	8
Berkeley	9	8	12	9
Duke	10	8	13	11
Northwestern	10	8	13	13
Michigan	11	9	14	10
Cornell	13	10	14	12
UCLA	14	12	15	15
Georgetown	15	14	17	14
Vanderbilt	16	14	18	17
Washington Univ.	17	15	19	16
Texas	18	16	20	17
USC	19	15	20	20
Minnesota	20	19	23	21
Florida	21	19	23	21
Boston Univ.	22	20	25	17
Georgia	22	19	25	29
North Carolina	24	21	27	23
Notre Dame	24	21	27	25
BYU	26	23	31	23
Emory	26	22	33	30
Ohio State	27	24	30	30
George Washington	28	25	34	25
Wake Forest	29	24	33	37
Arizona State	30	26	33	30
Boston College	31	26	34	37
Fordham	33	30	37	37
Irvine	33	30	38	37
Alabama	34	30	37	25
Iowa	36	33	42	28
George Mason	36	30	42	30
Texas A&M	37	30	42	46
Illinois	38	33	42	35
Washington & Lee	39	34	43	35
Utah	39	34	44	37
Wisconsin	42	37	51	43
William & Mary	43	39	47	30
Pepperdine	43	41	49	52
Villanova	43	38	47	56
Indiana-Bloomington	46	41	51	43
Florida State	46	42	53	47
Davis	48	44	58	37
Arizona	48	46	55	45
Maryland	48	44	53	47
Washington	48	44	53	49
SMU	48	43	53	58
Baylor	53	46	56	58
Kansas	53	46	58	67
Colorado	55	47	61	49
Cardozo	55	48	61	52
Temple	56	53	58	63
UCSF (Hastings)	58	56	72	51
Richmond	58	55	63	52
Wayne State	58	51	64	58
Tulane	60	56	69	55
Tennessee	61	54	66	56
Oklahoma	61	58	67	88
Loyola-Los Angeles	62	58	69	67
Houston	64	60	69	58
Miami	66	61	69	73
South Carolina	66	61	72	84
San Diego	68	64	74	64
Northeastern	68	61	74	73
Seton Hall	68	61	77	73
Connecticut	69	66	78	64
Florida International	69	61	80	98
Missouri	73	67	78	67
Drexel	73	62	83	78
Georgia State	73	66	80	78
St. John's	73	68	83	84
Oregon	76	68	83	67
Case Western	76	68	85	78
Penn State Law	77	72	85	64
Kentucky	77	68	85	67
American	77	72	87	73
Denver	80	72	85	78
Marquette	82	72	85	105
Texas Tech	84	74	92	105
Cincinnati	85	81	92	88
Lewis & Clark	85	81	92	88
Penn State-Dickinson	86	83	92	58
UNLV	86	83	93	67
Loyola-Chicago	86	83	92	73
Pitt	86	83	92	78
Stetson	86	78	93	111
Chicago-Kent	92	85	93	94
Nebraska	93	91	99	78
Rutgers	94	91	102	86
Drake	94	91	99	111
St. Louis	95	93	99	98
St. Thomas (Minnesota)	96	92	105	127
West Virginia	97	93	108	118
Michigan State	98	94	106	91
Louisville	99	94	104	94

As I’ve gone through “winners” and “losers” in previous posts on individual metrics, not all of those shake out in the final rankings, which include far more than just the isolated categories I looked at earlier. But some obvious winners emerge: Georgia, Texas A&M, and Villanova each see significant improvement, regardless of which version of the methodology is used.

You may well ask, “Why is X over Y?” or “How can A be ranked at B?” The answer is, I gave you the model weights and my assumptions, and this is the result it puts out. It’s all publicly available data, and, again, many schools are privately doing this already and know their areas of strengths and weaknesses as to other law schools.

At the end of the day, we’ll see how wrong I am.

But I think it’s also at least some sign that the shakeup, for most schools, may not be nearly as dramatic as one may suppose.

UPDATE 1/18/2023: I had originally thought I made an error with the calculations I used on the bar passage rates, but some schools have created some inconsistencies that I had to back and check. I was right the first time!

Which law schools are affected the most by the USNWR dropping at-graduation employment rates?

Following up on my post, “Who's likely to benefit from the new USNWR law school rankings formula?,” I’ve continued to look at other changes to the rankings. USNWR is now only using publicly-available data. I went through some of those changes, but I wanted to look at another one: the absence of at-graduation employment outcomes.

USNWR does not publicly disclose precisely how it weighs the various employment metrics, but it does offer some relative cues, and it also says it gives “full weight” to full-time, long-term, bar passage-required or J.D. advantage jobs. How a school places in that category is highly correlated with how it places in the overall employment category. Now, changes to this category are coming, as I noted (i.e., USNWR will give “full weight” to school-funded positions and to students pursuing graduate degrees).

Setting aside that change for the moment, USNWR also offers 4% of its ranking based on a school’s at-graduation employment rate. This metric tends to favor the more “elite” law schools that place a significant number of graduates into judicial clerkships or large law firms, because those employers tend to hire before a 3L has graduated. That, however, is not data collected by the ABA, which only collects data in 10-month employment statistics.

I looked at “elite” employment outcomes for students 10 months after graduation, and compared them to the overall at-graduation employment rate reported to USNWR. (“Elite” being placement in private practice law firms with 101 or more attorneys, and in federal judicial clerkships.) In this first chart, you can see a pretty good relationship between the at-graduation rates and the elite law firm placement rates. (You can also see a number of schools that don’t report their at-graudation rate

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Now here’s the chart for the relationship between those same jobs and the 10-month employment rate. As you see, overall employment rates rise significantly among the schools with the least “elite” employment outcomes. It means that the shift from at-graduation to 10-month may well favor placement into public interest, government, and smaller law firm jobs compared to how those positions have been handled in the past.

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On to the change in methodology. I ran the numbers from last year’s figures to see what would happen if the 10-month employment rate were weighted at 18% instead of its present 14%, and abolished the at-graduation employment rate. I only used the top-line full-weight “employment” figures, so those are less precise than trying to use the proprietary blend USNWR uses for its actual ranking; but, I did standardize each score and looked at where it fell. While imprecise, this should give a “band” of the schools most likely to over-perform and under-perform based on this change alone. It should be noted that many schools do not presently share at-graduation employment statistics with USNWR, and probably all of them would be better off, to some degree or another.

SCHOOLS LIKELY TO BENEFIT

At grad v. 10 month

Elon 10.2%, 78.7%

Dayton 30.0%, 87.1%

Willamette 30.6%, 85.2%

Texas A&M 46.9%, 93.8%

Gonzaga 31.7%, 83.7%

Regent 31.2%, 83.1%

Houston 31.9%, 81.9%

Arkansas 39.3%, 86.6%

Northern Illinois 26.3%, 77.5%

Samford 31.4%, 80.7%

Arkansas-Little Rock 7.1%, 64.6%

DePaul 32.3%, 79.3%

Campbell 33.6%, 78.6%

North Dakota 29.9%, 76.1%

Idaho 30.6%, 76.5%

Seattle 32.4%, 76.9%

Liberty 24.6%, 71.9%

LSU 41.9%, 82.0%

Oklahoma 30.2%, 74.5%

Belmont 36.0%, 78.0%

These tend to be schools that do not place an overwhelming number of students into large law firms or judicial clerkships, but that do have a fairly strong 10-month employment rate relative to their peers. Interestingly, there are not any California law schools on the list, a cohort I had assumed might benefit most from the state’s difficult bar examination and perhaps a higher “wait and see” approach from prospective employers.

Now, to schools more likely to be adversely affected.

SCHOOLS LIKELY TO BE ADVERSELY AFFECTED

At grad v. 10 month

Massachusetts-Dartmouth 33.9%, 47.5%

Yale 89.2%, 89.2%

Stanford 88.5%, 89.0%

BYU 82.8%, 85.9%

Northwestern 87.9%, 89.5%

CUNY 36.1%, 56.5%

Loyola-New Orleans 52.1%, 66.9%

Vanderbilt 82.2%, 86.1%

Georgetown 83.1%, 86.8%

NYU 86.6%, 89.5%

Berkeley 86.7%, 90.0%

Chicago 94.6%, 95.1%

Columbia 95.3%, 95.6%

USC 76.2%, 83.6%

Virginia 92.7%, 94.3%

Cornell 90.3%, 92.8%

Montana 81.2%, 87.0%

Irvine 58.7%, 72.7%

Connecticut 58.6%, 72.9%

Harvard 88.1%, 91.8%

Recall, of course, it is on this one metric alone I’m looking at the change. And recall because the schools’ data are standardized in each category, those likely to gain or lose may look a little different than one may expect on the raw numbers alone. But it’s a mix of schools that have a very high at-graduation employment rate and receive a significant boost relative to their peers; and schools that are fairly low in both categories that were farther outliers in the at-graduation rates.

There are many other changes that could help or adversely affect other schools. Note, for instance, that I suggested in an earlier post that BYU could gain significantly in some other categories; here, it appears they could be adversely affected more. Texas A&M, to name another, performs well here, as it did in other places. How much weight USNWR gives to any change matters greatly.

But I think this highlights just how uncertain many changes are in the upcoming rankings. As I pick off different categories, there are schools likely to change their performance in each category. How those shake out in the end—whether they tend to be beneficial or not—remains to be seen.

By knocking off expenditure metrics and devaluing peer reputation scores in the new USNWR formula, did law schools just kill the faculty's golden goose?

As Aesop tells it, there was a goose that laid golden eggs. The greedy farmer saw the goose and thought there must be more gold inside the goose. The farmer kills the goose and finds nothing special inside—but he has now lost the ability to gather any more golden eggs.

It may not be the same story with the USNWR boycott and subsequent rankings changes. Law schools may well have attacked the goose thinking it was a wolf. But upon its demise, it may well be that law schools have permanently lost one of their most significant bargaining chips with central universities in trying to secure more funding for the law school.

Let me at the outset point out that I’ve long been critical of many aspects of the USNWR rankings, including expenditure data. It’s been opaque and been a kind of arms race for schools to figure out which accounting tricks they can use to raise their expenditure figures. And let me add that eliminating is in many respects a good thing, because the costs often fell on student loan borrowers and tuition hikes. So the analysis below is a small violin for many, indeed!

But a sober look at the change is in order. I posited yesterday about a potential effect of eliminating the expenditures-per-student metric:

By the way, it’s worth considering a new and different incentive for law schools situated within universities right now. Law schools could presently make the case to central administration that high spending on resources, including on law professor salaries, was essential to keeping one’s place in the rankings. No longer. It’s worth considering what financial incentive this may have on university budgets in the years ahead, and the allocation of resources.

From some offline and private conversations, this factor has been one of the most eye-opening to the law professoriate.

In the past, law schools could advocate for more money by pointing to this metric. “Spend more money on us, and we rise in the rankings.” Direct expenditures per student—including law professor salaries—were 9% of the overall rankings in the most recent formula. They were also one of the biggest sources of disparities among schools, which also meant that increases in spending could have higher benefits than increases in other categories. It was a source for naming gifts, for endowment outlays, for capital campaigns. It was a way of securing more spending than other units at the university.

And indirectly, the 40% of the formula for reputation surveys, including 25% for peer surveys and 15% for lawyer/judge, was a tremendous part of the formula, too. Schools could point to this factor to say, “We need a great faculty with a public and national reputation, let us hire more people or pay more to retain them.” Yes, it was more indirect about whether this was a “value” proposition, but law faculty rating other law faculty may well have tended to be most inclined to vote for, well, the faculty they thought were best.

Now, the expenditure data is gone, completely. And peer surveys will be diminished to some degree, a degree only known in March.

Some increase in the measurement of outputs, including bar passage data and employment outcomes, will replace it.

For law faculty specifically, and for law schools generally, this is a fairly dramatic turn of events.

To go to a central university administration now and say, “We need more money,” the answer to the “why” just became much more complicated. The easy answer was, “Well, we need it for the rankings, because you want us to be a schools rated in the top X of the USNWR rankings.” That’s gone now. Or, at the very least, diminished significantly, and the case can only be made, at best, indirectly.

The conversation will look more like, “Well, if you’re valued on bar passage and employment, what are you doing about those?”

A couple of years ago, I had these long thoughts on the hollowness of law school rankings. For schools that lack the confidence in their institution and lack the vision to be able to articulate the value of the institution without reference to rankings, rankings provided easy external validation. They have also provided easy justification for these kinds of asks over the years.

Those easy days are over. Funding requests will need to look very different in a very short period of time.

Are there are other things that law schools can point to for a specific investment in law faculty in the USNWR rankings? Well, one such measure may have been citation metrics, which I had some tentative but potentially positive things to say as USNWR considered those. But law schools mounted a pressure campaign to nix that idea, too.

At the end of the day, then, the rankings formula will have very little to say with anything about the quality of law school faculty or the school’s financial investment in its faculty. An indirect case, of course, including a diminished peer reputation score. And faculty do contribute to bar passage and to employment outcomes. There will still be a faculty-student ratio.

But I think the financial case for law schools may well look very different in the very near future. This will be almost impossible to measure, and the anecdotes coming from these changes may well be wild and unpredictable. It’s also contingent on other USNWR changes, of course. But it’s a question I’ll be trying to watch closely over the next decade.

Who's likely to benefit from the new USNWR law school rankings formula?

Melissa Korn at the Wall Street Journal dropped the news today that USNWR plans on changing its formula for the law school rankings:

In a letter sent Monday to deans of the 188 law schools it currently ranks, U.S. News said it would give less weight in its next release to reputational surveys completed by deans, faculty, lawyers and judges and won’t take into account per-student expenditures that favor the wealthiest schools. The new ranking also will count graduates with school-funded public-interest legal fellowships or who go on to additional graduate programs the same as they would other employed graduates.

This is a remarkable sea change. As I recently pointed out, I did not anticipate much of a match between the boycotting law school’s tactics and complaints, and the ultimate USNWR response. I pointed out earlier that many of the complaints were about information that was already publicly available. And sure enough, the per-student expenditures, which were not the subject of complaints but the thing USNWR could not independently collect without voluntary participation from schools, are the first on the chopping block.

So which schools might it benefit or adversely affect the most? Let’s take a look at these three categories, and one other variable I’ll mention at the end.

1. Reduced weight to reputational survey data. Reputational surveys get 40% of the overall weight in the rankings—a lot. That’s 25% for the “peer” survey (i.e., other law school survey respondents), and 15% from the “lawyer/judge” survey.

Let’s start with the “peer” survey. Among the current overall-rated “top 50” schools, the schools that would benefit the most from this change (i.e., their peer score has lagged their overall score). (And this is just a raw comparison of rank v. rank; there are more nuanced issues dealing with the weighted Z-scores, scaling, and the like for another day…. And, of course, differences can be exaggerated the farther down the rankings one goes, a reason I also confined this to the “top 50” for now.)

POTENTIAL WINNERS, PEER CHANGE

George Mason (65 v. 30)

BYU (52 v. 23)

Alabama (36 v. 25)

Florida (31 v. 21)

Utah (47 v. 37)

Wake Forest (47 v. 37)

Texas A&M (56 v. 46)

Georgia (36 v. 29)

How about the other side? That is, the schools that would be adversely affected the most from the change? Again, focusing just on the existing USNWR “top 50” for now:

POTENTIAL LOSERS, PEER CHANGE:

UC-Irvine (20 v. 37)

Washington (36 v. 49)

Colorado (36 v. 49)

UC-Davis (24 v. 37)

Wisconsin (31 v. 43)

Boston College (27 v. 37)

Emory (20 v. 30)

There are a few others in the middle of some interest, because near the top smaller variations matter more. NYU (3 v. 7) and Georgetown (11 v. 14) are harmed the most.

Now, the degree to which this benefits or harms a school entirely depends, of course, on how much USNWR chooses to reduce the weight of the category.

For schools presently outside the “top 50,” schools that stand to gain the most include Wayne State, Baylor, Penn State-Dickinson, Tennessee, and Penn State-University Park. Schools that stand to be harmed the most include Santa Clara, Howard, Brooklyn, Rutgers, Denver, Georgia State, American, and Hastings.

Now, over to the “lawyer/judge” survey. It’s a smaller percentage, and, again, it depends on how much the change in weight goes. For those who stand to gain the most:

POTENTIAL WINNERS, LAWYER/JUDGE CHANGE:

Texas A&M (90 v. 46)

Arizona (77 v. 45)

George Mason (54 v. 30)

BYU (43 v. 23)

Arizona State (49 v. 30)

Alabama (43 v. 25)

Utah (54 v. 37)

Maryland (64 v. 47)

Boston University (28 v. 17)

And who are likely to be adversely affected the most:

POTENTIAL LOSERS, LAWYER/JUDGE CHANGE:

Boston College (24 v. 37)

Washington & Lee (24 v. 35)

Washington (39 v. 49)

Wisconsin (33 v. 43)

William & Mary (20 v. 30)

Emory (20 v. 30)

It should be noted, schools that appear on both lists in the best/worst categories may have much more to be happy/unhappy about.

Outside the top 50, schools most likely to benefit include UNLV, Wayne State, Florida International, Hawaii, Georgia State, Penn State-University Park, and Arkansas. Schools most likelty to be harmed include Howard, Oklahoma, Miami, Michigan State, Hastings, Lewis & Clark, Pittsburgh, and Case Western.

2. Eliminating per-student expenditures. Again, this is not publicly-available data, but it doesn’t take much effort to realize that the wealthiest schools, typically (but not always!) private schools, tend to be harmed the most by this change. Public schools (but not all!) are likely to benefit most. It was by far the biggest differentiator among many schools, giving schools like Yale and Harvard nearly insurmountable leads. Of course, it was also notoriously opaque and subject to manipulation.

If you want to consider the schools most adversely affected, it would be also useful to look at private law schools that have risen sharply in the rankings in recent years, or law schools that have had recent naming gifts or building/capital expenditures that allow an influx of spending.

By the way, it’s worth considering a new and different incentive for law schools situated within universities right now. Law schools could presently make the case to central administration that high spending on resources, including on law professor salaries, was essential to keeping one’s place in the rankings. No longer. It’s worth considering what financial incentive this may have on university budgets in the years ahead, and the allocation of resources.

3. Counting graduates with school-funded public-interest legal fellowships or who go on to additional graduate programs the same as they would other employed graduates. A couple of caveats. First, schools do not have to self-report if their full-time, long-term, bar passage-required or JD-advantage jobs that they subsidize are “public interest” or not, although one could suspect they mostly are public interest. These categories are also very small overall. Among the 35,713 graduates in 2022, 427 were pursuing graduate degrees (1.1%), 276 had school-funded bar passage-required jobs (0.7%), and 56 (0.2%) had school-funded JD-advantage jobs. The change will take pursuing a graduate degree from lesser to full weight, and school-funded positions from a lesser weight to full weight. (This is the only change really directly responsive to some of the elite schools’ earliest complaints.)

But those positions are not all equitably distributed. Who has the most in these three categories, which I’m lumping together for present purposes:

Yale 17%

Stanford 8.7%

District of Columbia 8.3%

San Diego 8.3%

South Dakota 7.7%

BYU 7.6%

Berkeley 7.0%

UC-Irvine 6.6%

Harvard 6.2%

Penn 6.2%

It’s worth considering whether the totals in these positions will climb in future years as the incentives have changed.

4. What fills the gap. USNWR has just announced it would eliminate expenditure data (per-student is 9%, “indirect” expenditures at 1%, if that’s also on the chopping block; as to library resources, it remains unclear for now). It also announced it would give reduced weight to the 40% categories of reputational surveys.

How USNWR backfills its formula to get to 100% will matter tremendously to schools. If you are doing well in all other areas of the rankings, maybe not so much. But if you are a school with relatively poor admissions medians and strong employment outcomes (a great value for students, to be frank!), you benefit much more if the new formula gives more weight to the employment outcomes. And if you are a school with really strong admissions medians but relatively poor employment outcomes, you benefit much more if the new formula gives more weight to the admissions statistics. Time will tell. UPDATE: USNWR has disclosed that it intends to give “increased weight on outcome measures.”

In short, there’s much uncertainty about how it will affect many law schools. I feel fairly confident that a handful of the schools identified above as winners in several categories, including Alabama, BYU, Georgia, and Texas A&M, will benefit significantly in the end, but one never knows for sure. It also has the potential to disrupt some of the more “entrenched” schools from their positions, as the more “legacy”-oriented factors, including spending and the echo chamber of reputational surveys, will receive less value. Law schools must increasingly face the value proposition for students (e.g., lower debt, better employment outcomes), with some other potential factors in the mix, in the years ahead.

UPDATE: It’s worth adding that USNWR has indicated, “We will rank law schools in the upcoming rankings using publicly available data that law schools annually make available as required by the American Bar Association whether or not schools respond to our annual survey.” That suggests other data, including the “employed at graduation” statistics and the “indebtedness at graduation” statistics, at the very least, would also disappear.