Frum Career Colleges: Reflections on the Survey Results
Can incomplete data + incomplete thinking + intelligent conversations = insights?
So B'chol D'rachecha's second reader survey has wound down. Sadly, this one didn't attract enough responses (less than 100 in total) to provide good data for serious analysis. But it did generate some very interesting comments and discussions. I'd like to report on some of that here.
First off, though, I should tell you about the data that we did see:
A large majority of those who responded were graduates of Lander/Touro undergraduate programs.
Nearly all have subsequently worked in the field of their study.
Most received no federal student loans or Pell, TAP, and TAG grants to pay for their education.
75% would recommend their schools to others.
75% report overall satisfaction with the support and guidance their schools gave them.
I was also shown the results of a similar survey undertaken some time ago within the Frum Devs Slack group. Because that group is pretty much exclusively made up of working IT professionals, their results will naturally lean heavily towards that sector. That survey (which attracted just over 100 responses) also didn't ask about specific schools or how much students paid for their education. But the results are nevertheless interesting and worth briefly summarizing.
As you might expect, IT pros with college degrees earned higher salaries (an overall average annual income of $108,625) at most points through the first ten years of their careers. But their advantage was not necessarily large enough to cover for their tuition and opportunity costs. Interestingly, it was self-taught individuals (with an average income of $103,386) who probably enjoyed the highest return on their investments both proportionally and in absolute terms. Bootcamp attendees (earning only $74,759) did alright, but nothing spectacular.
What's also interesting is the survey's measures of career growth. College grads experienced an average salary increase of 140% after ten years of work. Those who were self taught saw a 100% jump. And bootcamp grad salaries increased by 90%.
Of course, it should be noted that not everyone has sufficient background, tools, or motivation to succeed at following the self-taught path. And we must also repeat that both surveys relied of very small data sets. So those numbers are far from reliable. Having said that, my own article published on freeCodeCamp's site seems to confirm similar relationships at much higher scales.
Alternatives
Comments noted how the survey seemed to focus on variations on the four year college theme, but not non-degree programs and self-guided (and free) learning approaches. This is certainly true, although that's mostly because of the built-in limits of a single study. If you want to understand anything well, you'll often have to limit the scope of your investigation.
But that doesn't mean that there aren't other highly effective approaches out there. I'm told that many men transition from kollel to the workplace by combining their BTL degrees with specialized credentials like actuarial exams or Cisco networking certifications.
In private conversations, some individuals expressed their respect for programs like WITS in Baltimore and the Agudah's COPE and PCS programs. Others described their successes leveraging personal skills and aptitude in building their own businesses.
The question of consistency was also raised. Programs aren't always static. Even great teachers might fall into easy patterns over time and find it difficult to keep up with fast-changing skills. Entire programs that worked well in the 90s might now be way out of sync with the real world. And institutional faculties evolve: new teachers join and old teachers retire (and some old teachers don't retire).
All of those elements certainly contribute to the full career picture in our communities - and to that picture's complexity.
Intuition
Even if we haven’t got enough hard data to properly assess the state of career education, we can still use our eyes and ears.
It’s obvious, for instance, that no single path is going to work well for everyone. The fact that no two people have access to identical resources, skills, and potential tells us that we should choose programs that are the best fit for what’s there. And the more choices we explore, the better chance we’ll have of getting it right the first time.
Perhaps success in some careers is more the product of non-credentialed factors than your degree or diploma. As an example with which I’m personally familiar, your ability to analyze problems and learn fast might get you further in managing IT infrastructure than a degree in computer science. For one thing, 80% of what you learned in school will probably be obsolete by the time you graduate. Similarly, I wonder if success in special education teaching is more dependent on empathy, dedication, and good people skills than on whatever they taught in college.
Still, the more you’ve thought it through during the planning stage, the less likely you’ll make costly mistakes later.
Beyond Data
As was pointed out during more than one related conversation, the survey ignored the many non-financial reasons people might have for pursuing a career. Whether it’s about shidduchim, personal satisfaction, fear of being different, or the altruistic desire to accomplish something of lasting value, there’s more to a career than just income.
That’s certainly true. But that doesn’t mean it’s a good idea to willfully ignore the financial implications of your choice. By all means, pursue your dreams. But if there are tools out there that can help you do that without also risking significant long-term harm, then why wouldn’t you want to use them?
Colleges love it when their students switch majors after a couple of years (and $50,000). More money for them. But those are funds and years of your life you’ll never get back. Proper up-front research can pretty much eliminate that risk.
So I’d say that resistance to the value of data analysis is understandable, but misplaced. I’ll underline that point with a recent thought from the economist Bryan Caplan. Caplan was addressing (strange) complaints that public school COVID policies should not be based on data. Here’s how he responded:
"Should we really make decisions about children’s health using math? Well, why do you think we teach kids math? The main point of math is to improve human decision-making. Life entails risk. No one is perfectly safe. Not you, not me, not your kids, not my kids. If you can moderately increase safety with mild precautions – like seatbelts – great. If you can drastically increase safety with strong precautions, maybe that’s the right path, too. But microscopically increasing safety with strong precautions isn’t just a bad idea. It is childish."