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Myths about the MBA Admissions process
For
the past 5 years, I have been helping friends, clients, and on-line characters
with their MBA essays for the top 30 MBA programs in the world. A combination
of writing and social work skills help me understand what MBA admissions
programs want and how to explain it to people. Some very smart people have paid
me to shred their essays and occasionally their self-esteem. This has put me in
a position to see how difficult the MBA admission process can be for some
people and how many stories and myths are perpetuated year after year as
applicants try to make sense out of it.
How do you define a bucket? Does the Asian category include: Asian-americans, Asians living in Africa, or Asians just born in Asia? Any difference between Indian, Pakistani, Bhutan, or Nepali candidates? How about tech worker? Does an IT consultant go into the tech bucket or the consulting bucket? What if they worked in both IT consulting and management consulting? How about the network administrator for a yak herding co-op? Does he go into the technology bucket or animal husbandry bucket?
Admissions offices could spend the whole year arguing in which bucket they should put the Asian orphan who was raised by Guatemalan nuns who then became an investment banker, left banking to become a monk, started a non-profit milk co-op, then decided to be a brand manager for the organic milk line of an Icelandic consumer products company.
This is a lot of work. MBA admissions staff are usually 5-6 people with degrees in higher education. There is no database administrators or SAS trained analysts who can do all the statistical crunching of these buckets to determine the class that will best maximize value. The staff just tries to fully read all the applications, let alone spend all this time categorizing them in ways that they can slice and dice the data.
What’s the point? Even if you could put everyone into these buckets with a powerful database to do all kinds of meaningful analysis, how many bankers, tech workers, or religious yak herders do you really need in a class? If you picked two Olympic curlers last year do you need any Olympians this year or do you think that bob sledder will add a 0.3 additional r squared of diversity?
All right smart guy, what do you think admissions staff really do then? I think that after the admissions staff has read and rated all the applications and picked enough people to fill the projected seats, they look at the demographic stats. They make sure they have accepted enough woman so it’s not a big frat party and don’t have an overbalance of any career category based on historical trends. That’s only after all the decisions have been made and as a double check on the class composition. It’s not done beforehand to socially engineer a class. All decisions are made on the basis of the individual application alone. Now, I did hear that admissions have seen enough applicants form certain banks or consulting firms that they can tell how the applicant ranked in their class. But the applicant is still judged on their individual merits and the class ranking at their firm is an added piece of information.
So why does this bucket theory still exist? MBA applicants are an analytical group and at their jobs are always looking at data for some insight or arbitrage opportunity. They see some data mining opportunities and assume that admissions are doing it too.
How do you define a bucket? Does the Asian category include: Asian-americans, Asians living in Africa, or Asians just born in Asia? Any difference between Indian, Pakistani, Bhutan, or Nepali candidates? How about tech worker? Does an IT consultant go into the tech bucket or the consulting bucket? What if they worked in both IT consulting and management consulting? How about the network administrator for a yak herding co-op? Does he go into the technology bucket or animal husbandry bucket?
Admissions offices could spend the whole year arguing in which bucket they should put the Asian orphan who was raised by Guatemalan nuns who then became an investment banker, left banking to become a monk, started a non-profit milk co-op, then decided to be a brand manager for the organic milk line of an Icelandic consumer products company.
This is a lot of work. MBA admissions staff are usually 5-6 people with degrees in higher education. There is no database administrators or SAS trained analysts who can do all the statistical crunching of these buckets to determine the class that will best maximize value. The staff just tries to fully read all the applications, let alone spend all this time categorizing them in ways that they can slice and dice the data.
What’s the point? Even if you could put everyone into these buckets with a powerful database to do all kinds of meaningful analysis, how many bankers, tech workers, or religious yak herders do you really need in a class? If you picked two Olympic curlers last year do you need any Olympians this year or do you think that bob sledder will add a 0.3 additional r squared of diversity?
All right smart guy, what do you think admissions staff really do then? I think that after the admissions staff has read and rated all the applications and picked enough people to fill the projected seats, they look at the demographic stats. They make sure they have accepted enough woman so it’s not a big frat party and don’t have an overbalance of any career category based on historical trends. That’s only after all the decisions have been made and as a double check on the class composition. It’s not done beforehand to socially engineer a class. All decisions are made on the basis of the individual application alone. Now, I did hear that admissions have seen enough applicants form certain banks or consulting firms that they can tell how the applicant ranked in their class. But the applicant is still judged on their individual merits and the class ranking at their firm is an added piece of information.
So why does this bucket theory still exist? MBA applicants are an analytical group and at their jobs are always looking at data for some insight or arbitrage opportunity. They see some data mining opportunities and assume that admissions are doing it too.
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