Gender in MBA admissions

📅 written
We've been working hard on Optics, our new business school research reports, for the past month or so. Optics represents a different way of thinking about finding the right school. Instead of relying on self-reported metrics from the school, or from surveys conducted by some magazine, Optics looks at the only data that matters: what was a school able to do for its most recent students, and can it do the same for you?
Our approach is simple: we use a variety of public sources to build career records for every graduate from each program. Then, we examine that data to answer complicated questions, like "which school is best for a career-changer aiming to land in Private equity?".
We're still working on getting the product right, but in the meantime, we wanted to take a break & dive into some of what we've learned so far.

B-schools' effects on gender in different industries

Business schools' admissions & employment offices provide fairly comprehensive data to applicants. But, because the data released by schools are at their hearts just pieces of marketing, the granularity of that data often leaves something to be desired.
So, answers to questions a school hasn't decided to answer can be tricky to come by. For instance: many schools boast steadily-increasing proportions of women. We speculate they do this because they view themselves as bearing the responsibility to help lead change in industries. In other words: the more women MBA programs graduate, the more women enter fields where MBAs often hold leadership positions.
At first blush, this seems reasonable. But what if women are choosing not to join male-dominated industries on their own, and the number of women earning MBAs doesn't have a meaningful effect on gender composition? In other words: if MBA programs take more women from private equity and hedge funds than they previously have, but all of those women enter into fields like consulting or marketing, have MBA programs brought any more gender equity to male-dominated industries?
To be clear: we don't have enough data to answer this question with anything close to academic rigor. But, since we're the only game in town with objective, independently-sourced data, we thought we'd take a crack at it anyway.

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Men are from mars, etc. etc.

Okay, on with the show: We're going to lead with the data here & explain a few caveats, notes, etc., afterwards. The gist of these caveats is: our numbers are weighted averages of the past few years, we only look at fields for which we have a significant number of records, and jobs are classified as one type or another according to our own taxonomy. We've also chosen to limit our scope to Wharton, but as we develop Optics, this sort of information will be available for more schools.
First up: a look at the gender makeup by-industry of matriculating and graduating students. Each bar represents the gender breakout among students from that industry. This means that if, say, there are 50 people in the class from jobs we consider 'Marketing,' then the '60 %' in the chart means that ~30 of those 50 people were women.

Admitted students

This chart covers the type of job a student had just before matriculating (not including things like pre-MBA internships).
If this looks bad on mobile, you can view it full-screen at this link.
There's a lot here you likely expected to see: most folks from marketing are women, for example. Some bars, like ~60 % of corporate finance types aren't totally expected, but they're not un-expected either. And some areas where women aren't strongly represented make sense: military admits (~10 % of whom are female) pretty closely track the average percentage of female military officers, for instance.
What's surprising (or at least what we thought was surprising) is how underrepresented women are in certain industries relative to others. For example: we figured women would be underrepresented across all Wall St. gigs, but found it surprising that Private Equity lags behind both Hedge Fund and VC. Since all three of those industries feed from Investment Banking, we sort of expected to see similar representation numbers. Without good numbers on the gender makeup of those industries, though, this data's more 'gee whiz' than anything.

Want to learn more about Optics?

Thanks! We'll be in touch with updates before you know it.
Coming soon. Join the early access list & be the first to see what the data are hiding.

Graduating students

Now that we have a sense for gender makeup among Wharton matriculants, let's see where female Wharton grads take their degrees.
View full-screen at this link.
The empty lines at the bottom mean we have no records of graduates heading into these fields. For fields like military, this makes sense, but we were surprised to see how few graduates head into fields like FP&A (though this could easily be a sampling problem with our data).
Otherwise, there's nothing shocking on its own here, so let's move on to the net impact of admissions & graduate decisions.

Net impact on industry gender composition

This bit bears a little more explanation than the previous two. What we're trying to examine here is a simple question: does the mix of males & females entering business school from a given field match the gender mix of students entering that field post-graduation?
We'll explain more, but first, here's the chart:
View full-screen at this link.
Take a look at 'Hedge Funds.' Since 25 % of admitted students from Hedge Funds were female, but only 17.6 % of graduates headed to Hedge Funds were female, 'Hedge Funds' has a net change of -7.4. Investment Management (e.g., non-hedge fund wealth management), on the other hand, saw a bump of 18.5, implying that the gender mix among graduates heading into that industry tilted female.
We should note here that sending a smaller proportion of women into a field than Wharton took from the field won't necessarily tilt the scales of gender equity in that field in a meaningful way. But at the end of the day, if we're correct and bringing larger numbers of women into male-dominated fields is a goal b-schools have in mind, we'd love to see greater portions of women entering those fields post-MBA. More women in leadership positions, as far as we know, correlates with more women electing to enter the field at junior levels, so insofar as MBA programs can effect that, we think they ought to.

How we think about the data

We don't want to get pedantic about the data, but there are a few important notes to cover. As we mentioned above, our data is made up of raw career records (like resumes), cross-referenced with several public data sources to help us classify each role by industry / seniority etc.

Industries & types of jobs

"Is this job the same as that job?" is a pretty simple question, but for hundreds of years, humankind's been struggling to find the right answer. Even the U.S. Census has weighed in here, developing job classifications with an incredible amount of specificity (like the job classified, like SOC Job Code 39-3093, "Locker room attendant"). Google's even turned its AI brain to the task, but much like autocorrect learning the difference between "it's" and "its", the problem remains unsolved.
This was way too complex for our purposes, so we looked to the schools themselves. Since admissions publishes helpful numbers like "25 % of the class comes from consulting," we figured it'd be simple to just classify admitted students' jobs as admissions does.
But it's not that simple. It turns out that admissions doesn't really classify jobs the way applicants think about them. For example: a prop trading analyst and a commercial loan offer are as different as can be, but they might both be considered "Other financial services" by Wharton admissions. Further, the classifications admissions use for jobs often don't mirror what the employment office uses at the same school. We aim to connect pre-MBA and post-MBA data with Optics, so this complicated things.
In the end, we decided to combine elements from admissions' job classifications with our own understanding of the job landscape. This means we can easily compare schools on terms you get: a hedge fund analyst gets classified as "Hedge Fund," and a startup founder's called "Founder."

Margins of error

[You can skip this if you've got a solid handle on stats, sampling, etc.] We don't have 100 % of the data for every school. In practice, this means most of our estimates would be more accurately described with intervals.
To summarize what we mean, imagine you've got 1000 people and you'd like to know the average height. If you can measure all 1000 people, great--do that. But if you can't, the next best thing is to grab a few at random, measure them, and make assumptions about what that sample average means for the population average. That is: maybe the 100 you've sampled are above-average height, so your "5 foot 10" result is a little off. To account for that sort of error, you'd describe the average height as "somewhere between 5 foot 8 and 6 feet tall." As you added more people to the sample, that range would tighten until it converged on the true population average.
For our data, that means when we say "25 % of all former candlestick makers at Wharton were female," this means that the real number is somewhere around 20 - 30 %. In the future we may disclose actual intervals, but for now just assume a +/- of 5 % on any proportional measurement.

Which years?

We report results a little differently than admissions does. Instead of reporting separate numbers for each year, we take weighted measurements from across the past 3 years. We do this both for the sake of simplicity, as well as to smooth-out any bumps that might occur year-over-year (e.g., lots of private equity admits one year & much fewer the following year).

Want to learn more about Optics?

Thanks! We'll be in touch with updates before you know it.
Coming soon. Join the early access list & be the first to see what the data are hiding.

We hope this post whets your appetite a bit for the sorts of insights we're planning to share in the coming month or two with the release of Optics. An MBA might be the most expensive thing you ever buy, and we're working to make sure you have the best data possible to make it with.

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