How Bad at Math Are You Really?

Many if not most people do not like math. They will tell you they are “not a math person.” Perhaps you are one of these people. You took exactly as much math in school as they made you, and not ε more!

The problem with being “not a math person” is that math people enjoy a significant earnings premium over non-math people. Take a look at this list of the highest earning degrees (source):

  1. Petroleum Engineering
  2. Systems Engineering
  3. Chemical Engineering
  4. Actuarial Science
  5. Computer Science & Engineering
  6. Nuclear Engineering
  7. Electronics & Communications Engineering
  8. Electrical & Computer Engineering
  9. Computer Engineering
  10. Aeronautical Engineering
  11. Computer Science & Mathematics
  12. Physics & Mathematics
  13. Electrical Engineering
  14. Applied Mathematics

Noticing a pattern? Now take a look at the lowest earning degrees:

  1. Early Childhood Education
  2. Child & Family Studies
  3. Early Childhood & Elementary Education
  4. Child Development
  5. Human Services
  6. Social Work
  7. Therapeutic Recreation
  8. Human Development & Family Studies
  9. Youth Ministry
  10. Elementary Education

The high-earning degrees all use a ton of mathematical and analytical skills and the low-earning degrees all rely heavily on interpersonal and emotional skills. If you’re just not a “math person,” the world probably feels like a very unfair place, where others can earn a multiple of what you earn just for using the analytical abilities they were apparently born with.

I think many of us have a model of the world where mathematical ability is totally exogenous. Some people just have it and others don’t. But this doesn’t align with my experience or with that of my wife, who has tutored many a non-math person and discovered some interesting patterns.

My wife was a very popular tutor for some of the first-year math courses, including introductory calculus. Many students were willing to pay her quite a bit of money to tutor them every week for their entire semester.

So what was her secret? How was she able to coach struggling students to the point where they could pass their math courses?

In her words, they paid her to sit and “watch them do their homework.” She didn’t lecture. She didn’t come up with magical analogies to make tough concepts easy to understand. Her typical interaction went something like this.

Student: “I don’t know how to do this problem.”

Tutor: “OK, start by reading it to me.”

Believe it or not, sometimes this prompt was enough to get the student to understand the problem. They had just looked at the problem and, without fully reading it, decided they couldn’t do it.

But in the cases where this one prompt wasn’t enough, the student would read the problem and my wife would prompt them again.

Tutor: “OK, what do you think the first step should be?”

Sometimes just prompting these students, struggling students, to think about the problems and what they should do next is enough for them to solve it. Other times, when there really is something they don’t know or are confused about, she would give them a little instruction and then let them complete the problem.

But a surprising amount of the time, the sole thing preventing students from solving their math homework amounts to them simply giving up on problems that they could solve with just minutes of effort! Among those degrees I listed above, the mathy ones were earning three to five times as much as the non-mathy ones,* so it would be quite the thing to opt out of that entire segment of the labour market just because you gave up on solving questions you had the knowledge and skills to solve.

Of course, some people have better inherent math skills than others; we can’t all be John von Neumann. But for two people with the same inherent math ability, if one of them gives up thirty seconds into every problem that would take them a minute to solve, and the other one keeps trying for the whole minute, the second has a much higher earning potential.

I have always done well in math classes. I did well in high school, I did well while getting my BSc, while getting my MA, and while completing the first half of my PhD. I passed my comprehensive exams and even got an award for the top score (tied with another student). Most people would hold me up as a model of a “math person.” The kind of person they think they aren’t and can’t be.

But I struggled in every one of those classes. I still struggle when doing math. I’ve been able to do it because I trained myself to keep struggling even if I have to do it for a long time to find the solution. Maybe if we stopped thinking about analytical thinking as something innate and started thinking of it more like sports. Nobody is born able to dribble a soccer ball or run a 10k or swim 20 laps, but the vast majority of people can work to that level with effort and training.

Most of us will never be sports champions, but unlike in sports where all the earnings accrue to the top of the top athletes, you can earn a significant monetary payoff as merely an above-average mathlete. You don’t have to be a von Neumann to complete a computer science degree, and the computer science majors I’ve met have people throwing job offers at them before their degrees are even finished.


* It would be a lower multiple if we considered all the jobs instead of just the top and bottom earners, but still significant.

One thought on “How Bad at Math Are You Really?”

  1. I kind of agree strongly, but my own personal experience makes me want to caveat it.

    Disclaimer; I’m a Systems Engineer / Analyst. So #2 on the list. I’m the senior technical expert in my 20-odd team, working for a high-tech manufacturer.

    But…. I’m an arts graduate (Politics and Econ) who drifted back into quantitative stuff later. Ironically, nearly everyone in the department has better maths than me, or at least more formal qualifications. Why does this work out?

    I mention this because what seems to count in many well paid jobs is “math-ness”; the ability to construct formal models of the world based on quantitative measures. Not pure maths itself. The ability to formally structure and decompose problems with reference to extant engineering models is far more important than the ability to do complex differentiation. It’s notable that many of my engineering colleagues have much worse arithmetic and fluency with statistics whilst I think Fourier transforms are very close to magic….

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