> Men majoring in computer science or engineering roughly doubled their starting salaries by age 40, to an average of $124,458. Yet earnings growth is even faster in other majors, and some catch up completely. By age 40, the average salary of all male college graduates was $111,870, and social science and history majors earned $131,154 — an average that is lifted, in part, by high-paying jobs in management, business and law.
Uh, yeah. From 23-25 you’re comparing engineers vs random 4-year degrees. At 40 you’re comparing engineers vs a group that suddenly includes lawyers and MBAs.
You’d find similar results if you compared baristas to premed students. At 23 the typical barista is earning at least minimum wage while the typical premed student is earning literally nothing (unless moonlighting as a barista...). Suddenly their fortunes reverse at 40 when a significant portion of premed students have turned into established doctors.
23-25 is the time that all the advanced degrees are being earned. If you want a fair comparison you have to compare equivalent education in each age group. You can’t suddenly change the cohort at 40.
Sure, the fact that the career paths diverge is... the whole point. The cohort didn’t change, one part of it went to law school and the other didn’t. Your analogy is totally off base.
Also, I expect salaries are also higher for engineers/developers with a masters or PhD than just a BS, so there’s some of the same effect in that population too. (Although I suppose it’s more likely that engineering students seeking advanced degrees, especially PhDs, are also working full time).
“Computer science and engineering majors ... who were working full time earned an average of $61,744 ... higher than the average starting salary of $45,032 earned by people who majored in history or the social sciences...”
There is actually no career progression that results in a post graduate degree being earned. That is simply not part of career progression. Earning an advanced degree is something that one can choose to do instead of investing in one’s present career. No amount of hard work at your job will turn a four year English degree into a law degree or MBA.
My S.O. has a sociology degree, and worked at JC Penny's after college. She now has an MBA and a JD, and is COO for a healthcare delivery network of several hospitals and a whole load of clinics.
I doubt she is the only one like that.
Even if this is true, it is very misleading to conflate this with 4-year liberal arts degrees being lucrative long term. “English majors out-earn engineers at 40” is an extremely different statement than “English majors who also earn a JD out-earn engineers at 40.” If you have to get a second degree to make a good living as an English major, that says that the first degree itself isn’t very lucrative.
The article claims that what leads to higher earnings are the “soft skills” that liberal arts majors learn that somehow part of later in their careers, but the data they rely on doesn’t point to that at all. What helps liberal arts majors’ average earnings in the future is almost entirely attributed to the sudden inclusion of people holding advanced professional degrees.
Well, I think it's definitely a problem. Maybe while engineers and techies are young and working and making all that money, you use some of it and get an MBA or law degree or some other professional degree? Even better, go to med school. (That one's really hard though, only do this if you really want to be a doctor.)
In any case, education like that would deal with the mid-life income growth issue fairly effectively. (And in the tech industry, with all the ageism, it can be a mid-life "having any income at all" issue. Which is even worse.)
But it does show that compared to peer professions Engineering is badly paid even more so in places like the UK and EU
I don't know where the author is getting the $112k number for all men at age 40 is from, but it seems impossible from my knowledge of the ACS statistics. Perhaps the mean drifts that high with super star executive pay? Regardless, it makes me suspicious of the other numbers in this article- anyone have an idea of how the author could get these earnings statistics from ACS data?
As a PhD student (PhD students are underpaid to an almost criminal extant), you might think that, but start with half of that going to taxes, a $2M home, and escalating expenses. You can make it happen, but you get a bit of a culture shock when you go from making that much money to living off $50k in interest and living in Toledo.
None of those guys are getting 500 grand.
The point is that people like that should probably consider a long term plan to keep their incomes growing at the mid-life stage.
Regardless of the nuance of the stats though, the idea that you'd choose between wildly differing majors based on watered down averages strikes me as a really poor strategy. The variance on salary progression is huge, and it's not what you learned in school but what you do on the job that fuels your advancement. IMHO you're much better off starting by optimizing what you are naturally interested in at a young age, develop your skills along the way, but stay open and ready for new opportunities as you progress through your career.
Machine Learning was a standard offering in CS departments in the early 2000's. It was already a venerable subfield with much of the basic theory having been developed in the 1970's and 1980's. I remember thinking it was something of a curiosity and not terribly practical but it's absurd to imply it sprung from nothing.
That book taught me an important lesson that I wish I'd learned earlier, that for many of my courses reading the textbook would have been a good idea. My lecturer for the ML course was unbelievably tedious, and I realised I'd absorbed almost nothing. I sat down with Mitchell's book for a week before the exam, did all the exercises, and got the highest mark in my degree.
I often wonder how much better I could have done, and how much more I'd have learnt, if I'd put that sort of work into all my other modules earlier in the course.
> This course did not exist until 2003, when Professor Ng taught it for the first time with 68 students, and very little like it existed anywhere on college campuses 15 years ago.
I took a Machine Learning course as part of my degree in 2001/2. About half of my year in the department attended. The course had been running for many years and I understood it to be a standard fixture in CS department curricula across the UK at the time.
Ehh, or just do less work at a place that pays less.
Lots of people at these companies work a strict 40.
Even if there were any citations to back up the claims in this article, it wouldn't make any sense!
It makes me think based on the title that lots of new college hires must be making insane amounts of money right out of school. What happens say, 10-15 years down the road when being a good developer won't cut it and you either go into management or IC it somehow?
I don't think everyone can do that because as you move up there are fewer positions. Also, there are new college grads right behind you willing to do what you're doing for way cheaper. Even if you've kept up with your skills it seems like it's easier than ever to pick up new skills due to democratization of learning.
Averages are a terrible way to do a comparison. Averages aren't terribly useful unless you know that your data is Gaussian (or a sum of IID Random Variable => see the CLT). Folks love to erroneously assume Gaussian.
In this particular problem, I would expect to see a multimodal distribution for the liberal arts majors.. (i.e. some folks have massive salaries (famous authors) and some have small salaries (high school English teachers)).
In such a case, the average can end up at a value that basically doesn't exist in the underlying distribution. So you end up comparing nonsense.
While technically true, I would argue that many of the most successful managers are at least in part successful because they have a really strong background in the field they are managing. Most people want to be managed by people who understand some of the nuances of what they're doing.
"Liberal Arts" isn't a category of undergraduate major. It is it's own category, which is generally only offered at 'Liberal Arts' colleges which offer only that major. However, if you take 'Liberal Arts' to be a 'category of categories', it could potentially include:
Humanities - English Literature, Modern Languages, History, and Philosophy
Social Sciences - Anthropology, Economics, Geography,
Political Science, and Sociology
Creative Arts - Fine Art, Theatre, Speech, and Creative Writing
Sciences – Astronomy, Biology, Chemistry, and Physics
What did the survey say exactly? Well, first of all, from everything I can tell it only includes 4 states, Colorado, Michigan, Texas, and Wisconsin. Not exactly representative, probably, but what schools are included?
School list (for Colorado):
ADAMS STATE COLLEGE
AIMS COMMUNITY COLLEGE
ARAPAHOE COMMUNITY COLLEGE
COLORADO MESA UNIVERSITY
COLORADO MOUNTAIN COLLEGE
COLORADO NORTHWESTERN COMMUNITY COLLEGE
COLORADO SCHOOL OF MINES
COLORADO STATE UNIVERSITY
COLORADO STATE UNIVERSITY - GLOBAL CAMPU
COLORADO STATE UNIVERSITY - PUEBLO
COMMUNITY COLLEGE OF AURORA
COMMUNITY COLLEGE OF DENVER
EMILY GRIFFITH TECHNICAL COLLEGE
FORT LEWIS COLLEGE
FRONT RANGE COMMUNITY COLLEGE
LAMAR COMMUNITY COLLEGE
METROPOLITAN STATE UNIVERSITY OF DENVER
MORGAN COMMUNITY COLLEGE
NORTHEASTERN JUNIOR COLLEGE
OTERO JUNIOR COLLEGE
PICKENS TECHNICAL COLLEGE
PIKES PEAK COMMUNITY COLLEGE
PUEBLO COMMUNITY COLLEGE
RED ROCKS COMMUNITY COLLEGE
TRINIDAD STATE JR COLLEGE
UNIV OF COLORADO AT COLO SPRINGS
UNIVERSITY OF COLORADO DENVER
UNIVERSITY OF COLORADO - BOULDER
UNIVERSITY OF NORTHERN COLORADO
WESTERN STATE COLORADO UNIVERSITY
I didn't want to go through every school, so I just picked University of Colorado, Denver, since it seems like it's probably one of the bigger schools?
I used https://factfinder.census.gov/faces/nav/jsf/pages/searchresu... to compare all the majors. I couldn't find a single non-STEM major that had better incomes than even a single STEM major. I assume this is the dataset he is taking his data from since he links to it.
So I guess what I'm saying is, this article seems to be predicated on a tiny dataset from a few select schools from a few select states, does not include flagship schools, (in fact it seems to only include 3rd and 4th tier schools, no Anne Arbor, Reed, University of Portland, University of Oregon, Rice, UofT Austin, Texas A&M, U of Wisconsin, Lawrence, Milwaukee School of Engineering, Marquette, etc etc etc.
So before you debate 'what this means' you should know that, based on the above, it means nothing, and no general facts can be drawn from it. I couldn't even figure out what methodology he used to come to his conclusions (if any), or even what he means by 'liberal arts' since that's not a category in the data.
All that, and the data I could find completely disagrees with his analysis, and in fact shows the complete opposite.