Discussion: Lauren Rivera's Pedigree on Elite Students and Elite Jobs

Discussion: Lauren Rivera's Pedigree on Elite Students and Elite Jobs

I just finished reading Lauren Rivera's Pedigree: How Elite Students Get Elite Jobs, and I have to say it was a highly interesting read into the internal dynamics of hiring at what she calls elite professional services (EPS) firms like Goldman Sachs and McKinsey.

Her argument is simple: while sociologists have heavily focused on elite reproduction through universities (what might be termed the "Harvard" thesis), the reality is actually more complicated. Getting admission to a top school is insufficient to guarantee entrance to the elite. Rather, elites are generated partly as part of the process of entering the labor markets, namely through EPS firms.

Much of the book is devoted to her fieldwork working at one of these firms and describing each of the stages of the interview process for new graduates. We see constantly that definitions of cultural fit are key to getting hired, and that these definitions tend to be similar (although not identical) to the culture of elites. In other words, firms hire elite students from elite backgrounds not because of their parentage, but because of the social norms and cultural capital those parents provided. In this mission of illuminating inequality at the upper-end of the income spectrum, the book does an admirable job.

However, I felt the book did not probe deep enough into why these firms hire the way they do. Rivera makes the point that almost none of the EPS firms actually keep track of their applicant data and connect it with actual on-the-job work outcomes, particularly at law firms where interviews were entirely unstructured. How do they get away with this? Having written about one startup in this space, why have competitors been unable to disrupt this industry (at least so far)?

The answer is sort of lurking in the book: these jobs just aren't that difficult. Or perhaps more carefully, these jobs are part of a pipeline, where the vast majority of knowledge is gleaned as one travels through it. The only "requirement" to start down the pipeline is the cultural fit needed to work on these teams (i.e. just getting hired). Everything else can essentially be learned on the job.

All of this status making on the part of these firms is designed to hide this basic fact. Everyone sort of knows this when you hire a McKinsey engagement for a few hundred thousand or a few million and get one senior engagement manager and a couple of green business analysts. This is a large part of the argument made by Matthew Stewart in his book The Management Myth.

And yet, companies do it anyway.

What every one of these EPS firms is trading on is ambiguity. A company CEO is about to make a consequential market decision. A cheap consulting firm can probably answer the question for a small price, but since it is an important question, we must spend more to ensure that the answer is absolutely correct.

Similarly, a startup wants to go to an IPO. It probably can be crowdsourced, or at least done in a novel way. Google had all kinds of hiccups with their IPO process when the company decided to use a Dutch auction, but in the end, did any of that really matter to the health of the company? There is risk and ambiguity involved, and so of course we naturally head to the bulge bracket investment banks because we don't want to take the risk that something might go wrong.

Although the EPS element isn't here, this logic also applies to the debacle that was Obamacare. The government has now built a digital service team that is designed to efficiently provide IT services to government agencies at a fraction of the cost of contractors. As Mikey Dickerson, one of these tech mavens, described to FastCompany about Obamacare's rollout:

"They set aside hundreds of millions of dollars to build a website because it was a big, important website. But compare that to Twitter, which took three rounds of funding before it got to about the same number of users as ­Healthcare.gov—8 million to 10 million users. In those three rounds of funding, the whole thing added up to about $60 million.

In my mind, among the most important challenges of this century for startups is how to get past this mindset. People automatically conflate price with quality, without doing any more critical analysis. In fact, we probably do more critical analysis choosing between two Indian lunch buffets. In business, where failures can be career-ruining, we immediately run to the most expensive service provider.

If we are ever going to break these incumbent players (and break them we should), we are going to have to help consumers and business decision-makers truly rethink how they buy services. This start with simply believing that the performance of these sorts of services can be measured, and that there is value in keeping costs low, even when decisions are consequential.

One of the reasons that algorithms are beating humans is simply that this ambiguity is no less with the computer than with the flesh. We can no better judge McKinsey's performance than we can a computer's, they both appear to the observer to be something of a black box. In my view, this is one of the critical reasons why computers are making such headway into professional services these days (along with their price competitiveness).

I'm glad that pipelines for training still exist in many EPS firms, and wish more people had access to them. But for all of the talk about recruiting, the real message lies later, in the actual work that these firms do (and really don't). Elite reproduction is an important phenomenon, but the real story lies further down the pipeline.

Image by reynermedia used under Creative Commons.