A new (and larger) Chetty study on elite college admissions is released today

Although actually, I went back and I guess this all really began with this quote from the NBER paper:

“We find that a white non-ALDC applicant with a 10% chance of admission would see a five-fold increase in admissions likelihood if they were a legacy”

That, as I understand it, is a reference to Table 9. In fact, the full quote is:

We find that a white non-ALDC applicant with a 10% chance of admission would see a five-fold increase in admissions likelihood if they were a legacy; more than a seven-fold increase if they were on the dean’s interest list; and that they would be admitted with near certainty if they were a recruited athlete.

If you look at Table 9, it states a white applicant who as a non-ALDC had a 0.10 admission rate in their model would have a 0.486 rate as a legacy, and 0.751 rate as a dean’s list. I note the odds ratio is a function of base odds (this a result of the formula referenced below). So, according to this model, a white non-ALDC with a 1% chance would go up to 7.9% if a legacy (7.9X), 5% to 30.9% (6.18X), again 10% would become 48.6% (4.86X), 15% would become 60% (4X), and finally 20% would become 68% (3.4X).

Table 9’s methodology is described in Section 4.1, not section 5.1, but also the note to Table 9 found in B.2.9. Briefly, since this is redundant a bit with a prior discussion:

Table 9 is constructed using Model (5) from Table B.7.2R of Document 415-9. This table provides coefficient estimates for the impact that being a legacy, double legacy, dean’s interest list, and disadvantaged applicant has on the probability of admission. When performing the admission probability transformations, we focus on transformations in the base year. The formula for the transformations is provided in the body of the paper.

Again, Model 5 does not include the Personal rating, and the formula in Section 4.1 results in that shifting odds ratio I noted above.

Sure, but the authors use different modeling techniques to answer different questions.

As I just explained in another post, I believe the specific quote that started off this subdiscussion here is traceable to Table 9, which is described in Section 4.1, and uses Model 5 plus a transformation formula that results in a shifting odd ratio as indicated in Table 9.

I’ll use the top 25% income threshold you mentioned as an example. The study lists that 1.4% of persons with top 25% income attended Ivy+ colleges compared to 0.8% of the overall population attended Ivy+ colleges. 1.4% is higher than 0.8%, so Ivy+ students are overrepresented by a factor of 1.4/0.8 = 1.75. That may sound high, but it’s similar to other types of selective colleges, as listed below.

Selective Private: 1.6/0.9 = 1.8x overrpersentation
Ivy Plus : 1.4/0.8 = 1.75x overrpersentation
Public Flagship: 3.9/2.4 = 1.6x overrpersentation

However, this doesn’t tell you that the college name is the driving force in the overrpresentation of top 25% income. Quality of incoming student is certainly relevant, as is choice of major, desired career path, location, parents income/connections, … When you control for factors like these, I suspect the overrpresentation will drop substantially. It also doesn’t suggest that attending an Ivy+ is important for this outcome.

Consistent with this, when they added controls and removed athlete/legacies, the mean income appears to be identical between students who were accepted from waitlist and rejected from waitlist. There was only half a percentile difference in mean income between kids who attended Ivy+ and kids who attended their backup. However, they found more significant differences in portion with top 1% income after age 30 (little difference before age 30).

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The author didn’t include the personal rating in this particular table because his preferred model doesn’t include the personal rating (he thinks it is biased). As one can see from the other tables I referenced, the conclusion would be similar, had he included the personal rating. Had he controlled for personal rating, the legacy boost would actually have been a bit stronger.

For example, table 9 indicates 1% chance of admission increases to 8% for single legacy, under the column 5 model (no personal). If you add in controls for personal rating (column 6 model), then 1% chance of admission changes to 10% for single legacy.

This is a slippery slope, though. Jessica Springsteen worked as hard as anyone to make a US Equestrian team, but she also always had very nice horses - which are very expensive. Her achievement was enabled by money, but it wasn’t bought.

The achievements of wealthy kids are often diminished, and they are also very stressed in trying to “keep up”. Yes, when wealthy kids achieve, they may have quite a tale to tell, but it is exactly this attitude – that exceptional achievement is expected because nothing was in their way – that creates a no-win situation for them.

A wealthy kid who “achieves” is likely to have a stand-out tale to tell. But it generally doesn’t mean they didn’t do something exceptional.

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You are assuming that the very strongest students at these colleges are interested in becoming a Fortune 500 CEO.

I don’t think that’s true. If they are primarily interested in money, finance provides a much easier path to real wealth. If they are interested in building businesses, startups provide a better path. And of the rest, many are interested in academia.

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I’m not sure where you’re getting the data for this - would be helpful if you point to a table/figure/etc as there are so many in this paper.

Anyways, figure 16 looks at 4 measures for Ivy+, flagship public (and again, these are ELITE publics, roughly the best 9 in the country), and the authors’ model of how the Ivy+ students WOULD have fared, if they’d gone to the flagship publics:

Numbers below are flagship/model/Ivy+ and finally (difference of Ivy+ minus model - how many percentage points the Ivy+ attendance adds to the chance of achieving this, per the authors’ model.
Top 1% earnings: 7.0/12.2/19.4 (7.2)
Attend Elite Grad School 2.7/6.2/11.7 (5.5)
Work at Elite Firm 3.8/7.9/25.5 (17.6)
Work at Prestigious Firm 3.9/9.0/24.5 (15.5)

If you refigured the last number in each row as the % increase in chances, rather than the simple bump in percentage points, the differences would appear even stronger.

And again, this is based on the authors’ model, WHICH MAY BE WRONG. I don’t know. But I think at least the authors’ model would suggest a pretty substantial difference on these measures - a benefit to actually attending the Ivy+ versus that same level of student attending UVA or Michigan or UCLA or any of the other top 9 public flagships they use.

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Top 1% level talent (IQ + ambition + effort + grit + perhaps pre-existing financial/social connections) has a variety of interests, at age 18, age 22, age 35, age 55, and so on.

Some will chase the brass ring of leadership of a big (F500) company, others research, others maximizing income/wealth, others creative endeavors (film-making or whatever), and so on. Same is true, presumably, for the second 1% (98th percentile), and so on, down through reasonably high end kids.

The difference is that in 2023 (as opposed to 1973 or 1923), I presume a higher proportion of the top 1% of kids matriculate at the super-elite schools (Ivy+ or however you name/define them). So, in turn, the median and mean kid graduating Harvard in 2023 is likely a lot smarter, grittier, etc. than in 1923. Perhaps those kids in 2023 lag their 1923 counterparts slightly in pre-existing/family wealth/status (scaled for the era), but the much higher values for the other stuff matters a lot more, IMO.

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i have a cat emergency to deal with :cat2: :black_cat: so can’t read the posts yet

but at first I thought this was an attendance chart (in OP’s original post) – but it’s not attendance, but rather admittance rates.

can anyone explain why it dips with admittance rates for the 90% of income?

we have not looked at elite schools because we are donut holers and cant afford what they say we can afford x 4 – with low house equity/growth in the midwest.

@bgbg4us hope your cat is okay…

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From The Economist:

The making of America’s elite (economist.com)

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Harvard is free for families making up to $85k. More than 22% attend for free and 55% receive financial aid, 100% graduate without debt, and the average family contribution is $13k. How to reconcile these figures with the article on the advantages for the wealthy and legaxies?

I wonder if this has anything to do with preserving yield, since families making a middle income may not be able to afford Harvard et al, even if the kid gets in.

I suspect admission of wealthier applicants has to do with more than academics. High achievement in many areas, including the arts, scientific research, publishing writing and so on - and sports like rowing, fencing, squash, are probably more likely among the wealthy and kids in private schools.

Not everyone goes to elite schools to make more money in life. It is true that in 2010 about 40% of Harvard students wanted to go into finance or consulting. As I remember president Drew Faust tried to address the resulting effect on culture by enhancing the applied arts. It is possible that wealthier kids have interesting pursuits not geared to money-making whereas middle class kids have to worry more about income and return on investment. Those priorities may show up in applications.

Elite schools are assembling a class, not admitting students in some hierarchical manner based on a 1580 versus a 1550. The goal is an interesting mix. Unfortunately that mix includes more legacies and athletes then is fair. But it also includes many kids with lower income families who have “overcome challenges.” With financial aid, they can afford to attend.

Again, the donut hole families may be a result of concerns about yield. If this study was about attendance, not admission, it would be easy enough to explain. That said, the relatively high number of students on financial aid and the average family contribution make me wonder if the middle income folks are as left out as the study says. I guess the question is, what is middle income?

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This shouldn’t be a shock to anyone but it will be upsetting to many around here.

Of course there are successful people who didn’t attend an Ivy + university.
Of course there are failures who attend an Ivy +.
Of course there are instances when attending a non-Ivy+ is the best option for a particular student.

Still, the benefits of attending an Ivy+ shouldn’t be disparaged.

If nothing else, I hope this study puts the brakes on the wholesale bashing of students who post an interest in attending a highly selective university as nothing but fruitless prestige-chasing.

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55% receiving financial aid means that 45% do not. You can use Harvard’s net price calculator to see how much income and/or wealth you need to be in the 45% who get no financial aid.

I am actually curious about the ceiling for the amount of income and assets that signify aid. Ten years ago it was $150k and there was sometimes aid above that. I am sure it is quite a bit higher now.

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When our older was applying to colleges, we were all in on Dale&Kruger, and the old Plutarch maxim: “it is not the places that grace men, but men the places” (in fact, DS mentioned it in his Common App essay:)

We are in the proverbial finaid doughnut hole, and our out of pocket costs are very significant to us, so we went in with an open mind, ready to take advantage of merit aid if the value was right.

One thing, however, we told him at the outset: “if you get into MIT, you are going to MIT”.

When he did get in, we left some pretty attractive offers on the table, driven not by the expectations of a financial ROI (he was at the time intent on pursuing an academic career), but by a deep conviction about which place would provide him the best environment for self-actualization.

It very quickly became obvious what an expensive mistake it would have been to do otherwise.

Yes, anecdotes are not data, but on the other hand, statistics do not apply to individual cases. You only have one life.

His younger brother is applying to colleges this year. We are much less gung ho about this whole Dale&Kruger thing now…

Oh yes, athletic recruiting arguably deserves at least as much outrage as legacy admissions.

Regrettably, this is a practice that even MIT engages in (although their athletic preferences are not as strong as most places, so their athletes, on average, are weaker by a smaller margin than their overall student body.)

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At the most generous schools (HYPMS and a few others in the Top 20) you get some aid up to about $200K HHI with “typical” non-retirement assets (and retirement accounts and primary home equity are excluded). And up to ~$250K HHI with more than one kid in college.

You can run NPCs for specific schools of interest, we found their estimates to be pretty much in line with the final awards.

Hmm. I’d have to think about this further but I think this is an assumption that warrants probing. I understand the premise, and the attraction of this “natural experiment” (and certainly it’s “the data we got” and maybe that’s that) but I’m just not immediately convinced that this is the case.

It seems intuitive to me that why person X is ultimately accepted off a wait list over persons Y and Z may very well impact the outcomes the authors are then measuring and comparing.

Whether it’s “this kid will yield,” or fits an IP, or can pay full boat, or fill some other bucket or whatever. These kids may be “equally strong” in an abstract way. But, will the comp-lit student have the same career earnings outcome as the STEM kid? Probably not. But if only one of them is getting taken off the WL I’m not sure a comparison of their future earnings tells us what the authors are trying to tell us. I’m not sure how the authors could’ve controlled for all factors here.

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I compared the figure 1 you mentioned to figure A.1.

Numbers below are flagship/model/Ivy+ and finally (difference of Ivy+ minus model - how many percentage points the Ivy+ attendance adds to the chance of achieving this, per the authors’ model.
Top 1% earnings: 7.0/12.2/19.4 (7.2)

Yes, my earlier post said no significant difference in mean earnings (only half of a percentile) among waitlist accept/reject group (after discussed controls), but more significant differences in portion of alumni with top 1% earnings after age 30. Regarding the latter, the specific distribution by age was:

Age 27 to 29 – Near 0 difference in portion of alumni with top 1% earnings
Age 30 to 33 – Increasingly large difference in portion of alumni with top 1% earnings with each year, with Ivy+ grads up to 5 percentage points more likely to have top 1% earnings at age 33

This raises the question of why there is a difference in top 1% earnings, but not mean earnings. And why that difference is not significant in late 20s, but becomes significant in early 30s. I suspect the primary driver is “elite” finance and to a lesser extent consulting. The author of the paper touches on this as difference, with a larger portion from of students from Ivy+ colleges going in to these fields, as you noted.

Most students who apply to and hope to attend Ivy+ colleges do not start the college aiming for a career in finance. For example, back when the Harvard freshman survey asked about finance/consulting, only ~8% of students said they were interested in finance/consulting after college. Yet in the most recent senior survey, 42% of Harvard said they plan to enter finance and consulting after college. Prior to the 2008 financial crisis, the numbers were even higher. Something happens while attending Harvard that makes a large portion of students plans change from freshmen, steering them in to finance and consulting. I suspect that finance/consulting influence is greater for Harvard than typical non-Ivy+ colleges, such that if a particular kid is accepted from Harvard’s waitlist, he is more likely to enter finance/consulting than if he was rejected from waitlist.

Part of this effect relates to “elite” finance/consulting being one of the few industries that emphasizes prestige of college name. Students see “elite” finance/consulting reps recruiting on campus, hear about their friends applying, learn that they can likely have a greater income with finance/consulting than other career paths, or simply don’t know what to do after graduation and think a 3-year type finance job sounds like a good option while they are figuring out what to do with their life.

The difference in top 1% income between age 27-29 and 30-33 could relate to the typical time delay for career advancement and promotions to reach top 1% income, under “elite” finance/consulting. Varying external economic conditions during varying sample years may also contribute. For example, employees working in “elite” finance are more likely to receive a large enough bonus to push them top 1% income during a period of financial prosperity than a period of financial recession.

I agree with your suppositions overall. Re: this ^^^ quote, I’d ask whether those admits who are accepted off the WL, are any more or less likely to go into these fields than REA/RD admits, controlling for whatever can be controlled for, including intended major vs actual.