Economists from Harvard and Brown have revealed the secrets to getting into the most prestigious, Ivy-Plus schools.

1. Graduate from one of those schools yourself, many years ago. OK, that one may be tough to do now, unless you can turn back the clock. Being an alumni kid is the factor explaining the largest difference in Ivy-Plus admissions for the top 1% over middle-class families.

2. Send your kid to a prestigious, non-religious, private high school.

3. Get your kid recruited as an athlete.

Genes, finances, and college history may work against many of us in influencing those 3 factors, but they benefit the top 1% to gain a 2x admissions advantage over middle-class families with comparable test scores.

Why pull the legacy card, fork over $40k+ for private school, and travel the country with elite sports teams and trainers?

Graduates from Ivy-Plus schools are:

• 60% more likely to reach the top 1% income status;

• Nearly 2X as likely to attend an elite graduate school;

• 3X as likely to work at a prestigious firm.

Those benefits are all compared to graduates from flagship public colleges. 10% of Fortune 500 CEOs, 25% of US Senators, and 75% of Supreme Court justices attended these Ivy-Plus schools.

I am interested in the public policy and equity implications of this groundbreaking work, and the authors make sound, practical recommendations to address these implications.

But as an analytics geek, I also appreciate 4 elements of their study that mirror best practices I’ve identified over the years:

1. Define the problem you are trying to solve. The authors started with 2 questions, or problem statements (I’ve pulled these directly from their working paper):

   1. “Do highly selective private colleges amplify the persistence of privilege across generations by taking students from high-income families and helping them obtain high-status, high-paying leadership positions?”

   2. “Conversely, to what extent could such colleges diversify the socioeconomic backgrounds of society’s leaders by changing their admissions policies?”

2. Collect the right data. The authors compiled the first individual, anonymous data set combining college applications, admissions, and attendance, test scores, and income tax returns – over many years.

3. Test different analytic techniques. The authors primarily used regression, difference-in-difference, and counterfactual analyses to mine the data, test their hypotheses, and control for other factors.

4. Make recommendations to drive decisions and action. The authors surfaced attention-grabbing insights, made actionable recommendations, and gained NYT and WSJ coverage from their work.

Let me know your thoughts, about the study itself or about my geeky analytics rantings, in the comments below.

Congrats to the authors at Opportunity Insights (Raj Chetty, David Deming, and John Friedman).

#analytics, #data, #analyticsgeek