Accidental Signals

It is a well-known result in psychology that measures of intelligence correlate across domains. If you are good at math, you are probably also good at verbal reasoning, and vice versa. Therefore, if a company is hiring for a position that requires a variety of skills (as most jobs do), it should pick a candidate who has demonstrated impressive skill in one domain over someone who has demonstrated middling proficiency in a number of different domains.*

Hiring managers’ preference for specialists is widely known. So, smart people, who tend to be good at estimating job-market incentives (thanks to the correlation between domains of intelligence), gravitate toward programs that award narrow, in-demand credentials—even when the credentials are nominally equivalent (a BA in linguistics vs. a BA in English). This can create a feedback loop in which fields that are perceived as “challenging,” like computer science, attract challenge-seeking entrants, raising the standards of proficiency within the field and making it more difficult for a newcomer to rise above average. On the other hand, majors that are perceived as relatively easy, like sociology, may attract less-ambitious students, leading to deflated expectations and dissuading hard workers. (I have met a few people who told me that they majored in the liberal arts because they were “bad at math.” I have never met a STEM major who chose their field because they were “bad at writing.”)

(National Center for Education Statistics, Undergraduate Degree Fields)

Nothing in this argument depends on the skills actually conferred by a given degree program or the skills actually required by an employer. A tech company needs managers and product designers just as much as it needs engineers, but positions in management and product design are typically reserved as rewards for distinguished engineers rather than handed out to people who majored in management or design. This is because a coder’s science degree(s), plus a few years of demonstrated commitment to the company, signals greater potential for success in the new role than some stranger’s design credential, which may relate more to the job but is also easier to obtain.

The problem comes when we confuse these signals of potential with the thing itself. To depart from credentials for a moment, one such signal is “grit” (tenacity, work ethic, passion), which Angela Duckworth argues is the best predictor of career success. This conclusion is obvious if taken as a prescription for moneymaking. But the book (Grit: The Power of Passion and Perseverance, 2016) becomes more interesting if we read it as a diagnosis: an attempt to organize American capitalism’s scattered, ever-changing incentives under a handy label. It is this explanatory power that makes grit conceptually coherent. Successful people come from a range of economic and educational backgrounds, but what they all have in common is—see book title.

However, grit’s conceptual strength comes at the cost of moral clarity. In a later chapter, Duckworth begins to caution that grit is morally neutral: good and bad people (she mentions Hitler) alike can be possessed of grit, so we should not regard someone as morally virtuous just because they are gritty. This is correct, but Duckworth fails to take the further step of disclaiming the converse: she assumes that moral people will see value in grit and pursue it for its own sake. On the contrary, we (moral people, presumably) ought to question the value of grit, because the positive traits by which Duckworth defines it can be easily reframed as negatives: Where does tenacity end and stubbornness begin? What about work ethic and workaholism, or passion and temper? We can think of a few geniuses whose “passion” had disastrous consequences for their personal and professional lives.

While you may cultivate success through grit (and leverage your success for moral good), will you, in your power, recognize the worth of someone who is highly intelligent and hardworking but has not developed grit as a signal of this potential? Under a different incentive structure—an unstable family environment, for example—an effective learner might develop, in place of tenacity, the ability to recognize a losing battle; in place of discipline, flexibility; in place of passion, consideration of communal needs. These latter traits are not incompatible with grit’s essence, but they may interfere with grit’s signal when it is measured by prompts like, “I finish whatever I begin” (to which the respondent answers on a scale from “not like me at all” to “very much like me”). Grit is an abstract concept that attempts to unify many disjoint values under one label. It may reliably indicate career potential—it may even be the most reliable such indicator available—but it does so only “by accident”: it signals the presence of a collection of traits that employers value, not the merit that undergirds the traits themselves.

So it goes with credentials. Achieving a high level of expertise in any field is hard. But the accidental arrangement of the job market at a particular moment in time has stiffened competition in some fields and weakened it in others. Today, it is relatively easy to parlay a STEM degree into a number of careers. That is not because everyone “needs” to know differential equations, but because knowing that someone passed a differential equations (MATH 235) tells us more about their overall intelligence than knowing that they can speak intermediate French (FREN 235). A STEM degree is a proximate signal of being good at following directions, which is in turn a proximate signal of career success.

Ideally, employers, grad schools, and other such gatekeepers would shun such imperfect signals. Instead, they ought to evaluate each candidate on an individual basis, combing through their background in search of desirable traits. But this is cost-intensive, so gatekeepers bet on the reliability of cheap signals like credentials and test scores instead. They do not work perfectly, but they work well enough, most of the time.

* You may have heard that the instruments we use to measure “intelligence” are biased toward people from dominant cultural backgrounds and people of financial means. This is a good argument against the use of standardized testing in college admissions, but it does not contradict the claim that I am using here, namely, that intelligence between domains (and within an individual) is correlated.

Math Imitates Life

You can write a linear program to determine the minimum-weight structure needed to support a fixed load. The optimal solution is a leaf!

Here are some more linear programming notes.