GMAT Club MBA Rankings [Full-Time US] 1st Official

Welcome to GMAT Club’s first ever Full Time MBA Rankings!
October 5, 2020


Why MBA Rankings?
Over the last few decades, ranking MBA programs (and other graduate and undergraduate schools) have become more and more prevalent. Some publications have become so desperate to have a ranking, they have merely averaged other rankings so they might have something to publish!

Common complaints include observations that rankings often offer results that are either non-conclusive (such as many programs are tied) or so unexpected and deviate so far from general perception so as to render them unhelpful (at best) and misleading (at worst). GMATClub exists to provide helpful information, and for too long, the vast majority of rankings provide insufficient help to the typical MBA applicant in making a monumental decision.

Over the years, many GMAT Club members have expressed their frustration with the status-quo rankings and have expressed a need to have something more logical and transparent. And, in this a year of craziness and mayhem brought on by COVID-19, since most of the major rankings taking a hiatus of at least one year, we decided that now, more than ever, prospective MBA applicants all over the world would benefit from a fresh perspective.

The market has spoken and we have been listening.

(NOTE: The size of the space between the tiers is proportionate to the actual gap in the aggregate score between the bottom of one tier and the top of the next – AND – for the purposes of this analysis, each time the score drops by 3.0 or more between schools, we established a new tier):



Why Are Only the US Schools Ranked?
One of the great goals we had for this project (and still have) was to combine US and International rankings into a single table where one could look up how Tuck stacks up against LBS (for example). That goal is still very much alive but for the sake of time (and sanity) we have decided to, for now, focus just on the top US Schools.


Why Did These Specific Schools Get Selected for Being Ranked?
We sought to use a consensus list of the 30-50 top schools. Our list was built after considering those schools positively ranked by other ranking systems, seeking commonality across multiple methodologies. We eliminated outliers (i.e., schools that appeared only once or twice, but weren’t consistently ranked well across multiple platforms) in pursuit of consistency; the end result just so happened to be exactly 40 schools. While we believe these forty schools represent the top forty programs in the USA, we understand that others may disagree. Future years may see the list of schools get longer or shorter, and there may be some turnover from year to year.


How Did We Rank B-Schools?
Our approach is exclusively based on years of talking to prospective MBA applicants and current MBA students about their priorities in a program and their hopes for their careers after graduation, and being frustrated about the shortcomings of popular MBA ranking methodologies. Most aspiring MBA students state their primary purpose for getting an MBA is to super-charge their career, and they pick their school trying to assess which one is most likely to do that, with the highest probability of success at the lowest possible risk.

Over the last several months, hundreds of data points were analyzed for potential inclusion in our proprietary models. After much thought and consideration, we limited the parameters to only those factors we believe are most intimately related to comparing why a student might prefer one school over another. In the final analysis, even though several schools might fulfill most (or even all) of each MBA students' needs and desires and no matter how many great schools are out there, each person can still only pick one.

As a result, our models did not dedicate any attention to things such as quality of the school building, location of the school, average temperature in February, how many professors have published papers, etc. Those factors may be important to some but we believe they do not ultimately matter to the typical MBA student while they are pursuing (and being pursued by) prospective employers.

We zeroed in four core areas for this ranking (Brand, ROI, Cohort Strength, Diversity), and have built proprietary models assessing the strength of each of the forty schools in our analysis in each of these four key areas. The strength of each school was assessed in each of the four key areas, and then these sub-scores were weighted to derive an overall score for each school. The composite scores in each area and the final scores range from 0.0 to 100.0.

This is the heart of the rankings; please see more detailed description of each of the four core areas below:


30% Brand
(i.e., the relative and absolute difficulty to gain admission to any specific program, relative and absolute tendency of applicants to prefer any specific program, and the correlation between the strengths of a program and the preferred areas of study expressed by the typical MBA student)

• 20% Composite score is an algorithm based on three key pieces of raw data, and derivatives and combinations of the same: (i) total number of applicants, (ii) total number of admitted students and (iii) total number of matriculating students. Our goal was to try to answer the question: “If a student got into each school they applied to, which one would they be most likely to pick.” We believe our models measure market demand factors and perception factors among other aspiring MBA students. The stronger the market demand for a school, the higher the score.
• 5% Composite score measuring whether or not McKinsey, Bain and/or Boston Consulting Group have determined students admitted to a school are suitable for pre-MBA internship opportunities at their firm; the more companies have approved a program, the higher it’s score; this measures prestige among prospective employers.
• 5% Composite score measuring the perception of a programs’ strengths by the administration at other schools as compared to the stated desires of MBA students at all programs measured; the more tightly a schools reputation across its’ departments correlates with the desires of the typical MBA student’s academic interests, the higher the schools’ score. By way of providing an example, most MBA students want to improve their abilities in marketing – so, if a school has a highly regarded marketing department they will score higher than a school with a highly regarded real estate department (since only a small number of MBA students are seeking this specific expertise).

30% ROI
(1st Year Post-MBA compensation and total program cost (tuition, room, board, books, fees, etc.))

• 20% Composite score measuring the published average first year total compensation (signing bonus, salary, other guaranteed bonuses), adjusted downward based on the percentage of the graduating class who does not report their employment details back to the school. Our model is predicated on the belief that graduates who do not report in are either unemployed (but looking for work), unemployed (and no longer looking for work), under-employed (and embarrassed by it), starting their own business (at low initial compensation), are working at an unknown start-up (and are potentially embarrassed by the lack of prestige and/or a compensation package they know is lower than others in their cohort). In each instance, these non-reporting students are almost assuredly making less than their peers who did report. Since social norms common among MBA students include a bias against self-reporting below-average information, graduates with low-paying jobs and/or at employers they deem unremarkable are less likely to be reflected in the compensation statistics published by the schools. As a result, in this model, a school reporting higher compensation levels from a higher percentage of their graduating class will score more highly than a school with either lower compensation or a lower percentage of graduates reporting (or both).
• 5% Composite score which rewards schools who have graduates who (i) have accepted a job prior to graduation, (ii) have accepted a job prior to 90 days after graduation and (iii) have achieved a high level of compensation in the process. The idea is that graduates would prefer to get a great job that pays well as soon as possible, and the longer they have to wait to achieve this goal, the higher the risk they may not actually achieve it – and since achieving a great, high-paying job after graduation is the primary goal of most MBA students, not achieving the primary goal represents a material aspect of whether or not a school is desirable (when compared to others).
• 5% Composite score reflecting the financial costs to attend a school; tuition, books, room, board, fees, etc. Note that this does not include the opportunity cost of attending other schools, pursuing other degrees, forgoing compensation being earned prior to attending school, etc. A lower cost result in higher score; lower costs reduce the risks a student faces of not achieving a great job prior to (or upon) graduation.

30% Peers
(Cohort intellect and experience)

• 15% Composite score reflecting a schools’ average GMAT score (adjusted based on the percentage of the cohort who has reported their GMAT) and a schools’ average GRE-V and GRE-Q scores (adjusted based on the percentage of the cohort reporting their GRE). In our model, the average GRE scores for all schools reporting their GRE scores were converted to a GMAT equivalent using a widely known and publicly available formula.
  NOTE: Six schools did not provide GRE scores (Booth, Columbia, Goizueta, Kellogg, Marriott and Tuck). To incorporate these six schools into this metric, the lowest GMAT:GRE ratio for all other schools in the tier above, the same tier and the tier below each of these six schools was used to fill in the blank. As a result, we understand that it is possible these six schools may be slightly disadvantaged as a result of their own choice to not publish their GRE scores.
• 15% Composite score reflecting a schools’ average GMAT / GRE score (as discussed above), average GPA, average pre-matriculation work experience, and the relative amount of emphasis a school places on any of those three factors (vs. taking a balanced approach to all three); generally, a school that is consistently above average in each of these three metrics, not favoring one attribute above the others, will score most highly.

10% Diversity
(the percentage of the program comprised of women, international students and traditionally under-represented US minorities)

• 5% Composite score measuring traditional aspects of demographic diversity – the percentage of women, the percentage of international students and the percentage of historically under-represented US minorities. There are three things to note: (i) schools under-indexing in the percentage of women were given extra negative marks in proportion to the amount they under-indexed; (ii) since this is a ranking of US schools, schools who materially over-indexed or under-indexed on the percentage of international students as compared to the mean of all of the Top 40 programs considered here scored lower than those closer to the mean, and (iii) the greater the percentage of historically under-represented US minorities, the higher the score.
• 5% Composite score measuring the relative reliance on international applicants for strengths in the quantitative measures published by a school; since this is a ranking of US programs (not global or international programs), if a school relies more heavily than their peers on the strength of the international applicant pool, that schools’ score in this measurement would be lower than it otherwise would be.

Where is the Data From?
The rankings are based on publicly available data such as school websites, class profiles, employment reports, employer sites, and other public data sources. The rankings currently are based on the class of 2021 information. We anticipate updating the results once the class of 2022 data is released by all of the schools (a handful of programs have released their latest class profile but we have to wait until all programs release the data so we have an equitable comparison).


Who Built These Rankings?
In the early 1990s, Regenerate was considering attending a full-time MBA program. Due to life’s circumstances, he elected to remain in full-time employment, pursuing other avenues for professional development. In the midst of the chaos of the past few months, he began considering going for either an Executive MBA or Online MBA in advance of the second half of his career. After having spent the bulk of the past 25 years in quantitative endeavors, and periodically following the ever-changing ranking systems used by others, he developed strong opinions about shortcomings in the commonly-used rankings. In conjunction with his research into various MBA programs earlier this year, he approached bb about the subject of rankings, sharing his quarter-century of disappointment in the MBA ranking industry.

As discussed at the outset, BB had been considering a rankings project for many years, and after considering Regenerate’s ideas and perspectives, BB decided to ask him to lead the charge for GMATClub’s inaugural MBA rankings.

The data compilation, analytical models and methodology were built by Regenerate, however, several others (led by BB) provided critical feedback and perspective, ensuring the entire project would fulfill the overarching goal – to help to the GMATClub’s community members make one of the most momentous decisions they’ll ever make!

Regenerate and BB wish to extend specific a very “Thank You” to texas101, souvik101990 and @workout for the time and insights shared over the last several weeks.


Are These Rankings Perfect?
Good question. First of all, we are not ranking all 2,000 MBA programs. In this first version we are only focusing on Top 40 MBA Programs. Additionally, while a school might offer several different avenues to achieving the MBA degree, we are only considering a schools’ full-time MBA program (which is typically awarded after 20-22 months of study, broken up only by a summer break between years).

Second, we know the inaugural GMATClub rankings are not perfect and we did not try to make them perfect. We did, however, try to overcome shortcomings we perceive in other ranking methodologies. The approach we’ve taken would fall under the analytical umbrella of “we’d rather be generally right than precisely wrong.” We did not curve fit the data; we did not begin with a target slotting for any school; we did not get cute with weightings of sub-scores; we simply tried to take the data that was relevant, available and complete, consider the questions the majority of MBA applicants ask themselves (and others) when deciding where to apply and attend, and apply a fresh set of eyes and some new ideas towards analyzing the inputs. We understand (and embrace) the notion that the best school for you might be different than the best school for someone else; we understand (and embrace) the notion you may place more importance on being close to home or moving to a different country or perhaps seeking a comfortable 70 degrees in February. While important to some, this ranking does not consider any of those factors (or whether or not the university’s football program is competitive).

Third, because of the first two items mentioned above, there was a great number of potential avenues to make the rankings below far more robust – however – we simply didn’t have full data sets for some of the analytical models that had been built and considered. For example, we wished to consider the issue of whether or not a school had more “regional” or “national” influence, but we were unable to consider the question fully as several schools did not report regional career placement statistics and others did not report the relative compensation packages for domestic vs. international students, and a host of other potentially relevant statistics. In future releases, we hope to roll-out additional analytical tools derived from better / additional data, any of which might impact the relative ranking of schools at that time. At this time, if all of the necessary data could be located and verified, it is anticipated the most important of these additional models would entail an expansion of the ROI calculation to consider the average debt of students at graduation and the percentage of students carrying debt at graduation.

So, this bit of prose lays out a bit of the background as to “why” and a bit of the “how” … we hope you enjoy these rankings and find them helpful. We welcome your feedback for future iterations!
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Thanks so much for posting! Very interesting to see Darden as #1 ranked for ROI!
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Not sure how Darden and NYU rank above Columbia and MIT. And what horizon has been used to calculate ROI? You can’t just take the first years salary as the ROI. The whole point of doing an MBA is to catapult your career over a period of the next 5-10 years.

Posted from my mobile device

My understanding what plays a role in this is not only the nominal dollars but also the % of the class reporting the information. E.g. if only 10% of the class report that they are making $200K/year and everyone else is mum, we are not saying that everyone going to a school will make $200K but if another school has 90% reporting average salary of $100K then it will be ranked higher... and the school that has low number of people reporting will be penalized for the lack of information/transparency.... or at least I think that's how Regenerate has built the rankings

The reasoning for that, if people are not reporting their stats/salary figures, that means they either are not making enough or don't have a job or another reason they chose not to help their school and report data.

The reason for not measuring life-long compensation is that there is NO DATA from a reliable source that shares income/earnings over any time except what's in a school report. There are some flawed and very questionable figures put out based on the student loan payments and that is incomplete.

If you feel this reasoning or calculation is flawed, would love to hear your thoughts.

Thanks!
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Awesome to see this data!

BB answered most of it below ... but, I'll provide additional details below his comments.




So, Darden and Stern placed above Columbia and MIT ... did you read the notes at the bottom of the ranking?

To quote NOTE #5 at the bottom of the overall ranking: "In close to half of the iterations considered, Stern and Sloan distanced themselves from the six schools immediately below them (Yale, Darden, Columbia, Fuqua, Ross and Tuck) and became their own tier. In every instance, these six schools were very tightly bunched and would appear in nearly every possible order. As a result, we believe it would be disingenuous to claim there is a marked quantitative difference in the quality of these six programs (ranked here from 9 to 14)."

(emphasis added)

Now, why did Stern rank more highly? Because the metrics we considered and the proportions in which we considered them made it so. We were agnostic as to which school should rank where. We let the data lead us.

And, similarly, those six schools ranked in a clump in every permutation we considered, and appeared in virtually every conceivable order (sometimes Ross was #9, sometimes they were #14 ... same for each other school in that group) ... which is why we said what we said.

Now, if Columbia had a class demographic that was more similar to the other schools in the top 40, they would have ranked more highly ... but, they don't and so they didn't. Again, they've got an excellent (!) brand and a very strong cohort (in terms of entrance exams, GPA and years of work experience) ... plus a solid ROI score (ranked in between Stanford and Yale on one side and Sloan and McCombs on the other -- four high-quality schools) ... but they were really hurt by their diversity score. Get rid of diversity altogether and they move up. But, we thought diversity is an important consideration -- anyone who is serious about learning, self-improvement and overall excellence does not want to live in an echo chamber -- these professionals and leaders will seek new ideas and different perspectives. This is why a US business school should have a good representation of women in their class ... a good representation of under-represented US minorities ... and ... not overly rely on strong non-US applicants to build a strong US-based program. The criteria would be different if we were ranking MBA programs with a global footprint ... but we're not. This is a ranking of full-time US programs.

Finally, the descriptions that explained each sub-score are, we believe, relatively straight forward ... and ... as described in the write-up, there were numerous areas we would have liked to have analyzed (including our top desire to improve the quality of the ROI analysis, which desire was also described in the write-up above), however we were faced with a variety of data sufficiency issues that kept us from looking at this question from even more angles. Maybe we can be more robust in the future. If you have any suggestions for high-quality and robust sources of applicable raw data, I'm all ears.

Thanks for the questions.
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Glad you left them out. Other than helping with the optics, of making the ranking methodology seem diverse and comprehensive, their utility is subjective.

I'd think students would benefit more from professors who use their time outside classrooms to help them learn and grow, as opposed to churning out theoretical papers in solitary confinement.

Btw, I was curious to know if you've included the proportion of students who manage a career change (industry, role, geography).

For many folks who work with us, other than getting a high salary, an MBA is also a vehicle to manage a career transition.

It'll be great to have this factored somewhere in there, as part of the refinement process.

And yes, with the increased interest in programs outside the US, it'll be good to include top programs in Europe, Canada, Asia too.
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You raise a great point.

We faced enormous issues with data sufficiency; the area you highlight is one such area. We need the same data from each school before we can build a model to analyze all schools in any particular area --- apples to apples; oranges to oranges; kumquats to kumquats.
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Very great initiative GMATClub and the people involved - esp. @Regenerate; kudos to you all!

Taking the example of a product manager, as they would let the customer feedback guide them and not let the opinions of other PMs deter them, I'd like to see the 'Brand' metric being solely driven by employer opinions on the performance of MBA grads from various schools and their resulting propensity to hire from these schools. (perhaps we can bring in some sense-checks by weighing it with the actual % class hired by these employers to see if they walk the talk?)

Since you guys seem to have tested a great bunch of scenarios, is this something you have readily available that this year's applicants (myself included) can use? I understand you may have it for the top employers only such as the MBB and/or FAAMG - but that too helps.

PS: my reasoning is that the 'Brand' is like a tool and it is up to each candidate to use as per their abilities, drive, etc. Hence, giving much higher weightage to general preference, yield, etc. just gives a mixed average. Hypothetically, that is something the schools can try to have a large influence on - I know some schools go to great lengths to keep their yield up.

Good ideas ... however ... as with most interesting avenues to pursue, the data sufficiency issues would block this, as follows:

Top 40 schools ... top 20 employers at each ... survey each employer (probably 20-50 data points requested from each employer at each school) ... enter the data ... verify the data ... analyze the data, adjusting for alumni biases in favor of grads from the school the recruiter graduated from to try to normalize bias ... etc ... All of that is easily hundreds of thousands of data points and hundreds (if not thousands) of hours of work.

The only top-notch employers we could find who'd published a list of schools they'd consider admits to be eligible for a pre-MBA internship were the three MBBs. If Goldman Sachs, Morgan Stanley and JP Morgan had a list, we'd look at it. If FAANG had a list, we'd use it. If top accounting firms had a list, we'd use it. If top consumer products companies had it, we'd use it. Etc.

The trouble is that some / several / many other employers had programs for pre-MBA internships, however, all of the other programs we found were ONLY open to MBA admits fitting particular demographic profiles (irrespective of schools they students were attending), as opposed to being programs which were open to ALL students from a particular school. While we applaud programs to enhance opportunities for traditionally under-represented demographic groups, we're focused on ranking MBA programs (as opposed to ranking employers' diversity initiatives), and so we'd need to find elite internships opportunities open only to ALL students at a list of particular schools.

So, we focused on "BRAND" as principally being an effort to measure which schools are preferred by the applicants and admits themselves -- figuring they (as a group) are informed with their goals and alumni who've achieved their goals, etc. By looking at the potential answers to this question from multiple perspectives, we believe we were able to achieve an answer that's directionally and proportionately representative of the preferences of the aggregated universe of MBA applicants and admits (inclusive of ALL the reasons that ANY student might apply / attend any given school).

If you've got ideas as to how to improve the BRAND (or other) metrics ... without a bajillion data points ... drop me a private note to discuss further. Thanks!
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They had the highest % of their graduating class reporting in on compensation statistics ... and ... while their base salary was a decent bit lower than the very highest salaries reported, they had a the 3rd highest percentage of their graduating class reporting getting a signing bonus (which helps out their overall 1st year compensation stats) ... and ... their cost of attendance is a decent bit lower than most of the other schools in the top 15 ... so, add it all up, and it makes good sense to me.
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hey
this is really comprehensively
Regenerate just wanted to know that is brand perception skewed toward domestic students’ preferences?
because as an international i always thought tepper was the best of the rest (after t15)
considering that prestige recruiters seem more prevalent at tepper

Posted from my mobile device

also, i was going through the apps received by top20 schools in the past few years and despite Cornell being an ivy and ranked higher almost everywhere, it still receives fewer apps than mccombs and marshall?
bb Regenerate
am i missing something?
is it the LA and Austin factor?

Posted from my mobile device

Thanks for the comments!

Brand perception is based on the total number of applicants, the total number of people accepted, the total number of matriculants, and ratios between these three ... plus ... the correlation between perceived program strengths (in various academic departments) vs. the average priorities for intellectual development among MBA students ... and ... whether or not a school is approved by one or more MBB for pre-MBA internships (as a proxy for a program's prestige among high-end employers).

I'm not sure if any of those metrics make it skewed for or against domestic student preferences ...

I'm quite good with you personally considering Tepper the 16th best program. It's a nice program and has helped a ton of people get where they want to go with their careers. It just didn't show up as the 16th best (or higher) school in any combination of variables we looked at this year with this data and over-arching philosophy.



Here's the data for these three schools:

Cornell (Johnson) = 1535 applicants ... 588 admits (38.3%) ... 282 matriculants (48.0% yield)
Texas (McCombs) = 1941 applicants ... 749 admits (38.1%) ... 260 matriculants (35.1% yield)
USC (Marshall) = 1899 applicants ... 568 admits (29.9%) ... 214 matriculants (37.7% yield)

Fewer applicants needed to get a bigger class with the highest yield? Sounds like, when comparing these three schools, the people who apply there want to go there ... and ... it sounds like an argument could be made that McCombs and Marshall are often a backup option ...

In the BRAND score, Johnson and McCombs were (for all intents and purposes) tied ... Marshall was a good bit behind (due in large part to their lack of acceptance by MBB for pre-MBA internships, as none of the MBB had Marshall on their list and two of the three MBBs approved Johnson and McCombs).

Also, FWIW Tepper, Kenan-Flagler and McDonough were all within 300 applications of Johnson. Tepper and Kenan-Flagler were on the rung just below Johnson and McCombs ... but McDonough brought up the rear (for a variety of reasons -- namely they scored lower than both Tepper and Kenan-Flagler in each of the three sub-scores that make up the BRAND score).
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The 2022 Rankings have been posted by the way. Here is the link to the 2nd Edition of the GMAT Club Rankings:

https://gmatclub.com/forum/gmat-club-mb ... 57142.html
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Hello from the GMAT Club MBAbot!

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