Higher Education Strategy Associates

Tag Archives: employment

May 16

Jobs: Hot and Not-So-Hot

Remember when everyone was freaking out because there were too many sociology graduates and not enough welders?  When otherwise serious people like Ken Coates complained about the labour market being distorted by the uninformed choices of 17-19 year-olds?  2015 seems like a long time ago.

Just for fun the other day, I decided to look at which occupations have fared best and worst in Canada over the past ten years (ok, I grant you my definition of fun may not be universal).  Using public data, the most granular data I can look at are two-digit National Occupation Codes, so some of these categories are kind of broad.  But anyway, here are the results:

Table 1: Fastest-growing occupations in Canada, 2007-2017

May 16-17 Table 1 Fastest Growing

See any trades in there?  No, me neither.  Four out of the top ten fastest-growing occupations are health-related in one way or another.  There are two sets of professional jobs – law/social/community/ government services (which includes educational consultants, btw) and natural/applied sciences) which pretty clearly require bachelor’s if not master’s degrees.  There are three other categories (Admin/financial supervisors, Technical occupations in art, and paraprofessional occupations in legal, social, etc) which have a hodgepodge of educational requirements but on balance probably have more college than university graduates.   And then there is the category retail sales supervisors and specialized sales occupations, which takes in everything from head cashiers to real estate agents and aircraft sales representatives.  Hard to know what to make of that one.  But the other nine all seem to require training which is pretty squarely in traditional post-secondary education specialties.

Now, what about the ten worst-performing occupations?

Table 2: Fastest-shrinking Occupations in Canada 2007-2017

May 16-17 Table 2 Fastest Shrinking Occupation
This is an interesting grab bag.  I’m fairly sure, given the amount of whining about managerialism one hears these days, that it will be a surprise to most people that the single worst-performing job sector in Canada is “senior management occupations”.  It’s probably less of a surprise that four of the bottom ten occupations are manufacturing-related, and that two others – Distribution, Tracking and Scheduling and Office Support Occupations – which are highly susceptible to automation are there, too.  But interestingly, almost none of these occupations, bar senior managers, have significant numbers of university graduates in them. Many wouldn’t even necessarily have a lot of college graduates either, at least outside the manufacturing and resources sectors.

Allow me to hammer this point home a bit, for anyone who is inclined to ever again take Ken Coates or his ilk seriously on the subject of young people’s career choices.  Trades are really important in Canada.  But the industries they serve are cyclical.  If we counsel people to go into these areas, we need to be honest that people in these areas are going to have fat years and lean years – sometimes lasting as long as a decade at a time.  On the other hand, professional occupations (nearly all requiring university study) and health occupations (a mix of university and college study) are long-term winners.

Maybe students knew that all along, and behaved accordingly.  When it comes to their own futures, they’re pretty smart, you know.


January 18

More Bleak Data, But This Time on Colleges

Everyone seems to be enjoying data on graduate outcomes, so I thought I’d keep the party going by looking at similar data from Ontario colleges. But first, some of you have written to me suggesting I should throw some caveats on what’s been covered so far. So let me get a few things out of the way.

First, I goofed when saying that there was no data on response rates from these surveys. Apparently there is and I just missed it. The rate this year was 40.1%, a figure which will make all the economists roll their eyes and start muttering about response bias, but which anyone with field experience in surveys will tell you is a pretty good response for a mail survey these days (and since the NGS response rate is now down around the 50% mark, it’s not that far off the national “gold standard”).

Second: all this data on incomes I’ve been giving you is a little less precise than it sounds. Technically, the Ontario surveys do not ask income, they ask income ranges (e.g. $0-20K, $20-40K, etc). When data is published either by universities or the colleges, this is turned into more precise-looking figures by assigning the mid-point value of each and then averaging those points. Yes, yes, kinda dreadful. Why can’t we just link this stuff to tax data like EPRI does? Anyways, that means you should probably take the point values with a pinch of salt: but the trend lines are likely still meaningful.

Ok, with all that out of the way, let’ turn to the issue of colleges. Unfortunately, Ontario does not collect or display data on college graduates’ outcomes the way they do for universities. There is no data around income, for instance. And no data on employment 2 years after graduation, either. The only real point of comparison is employment 6 months after graduation, and even this is kind of painful: for universities the data is available only by field of study; for colleges, it is only available by institution. (I know, right?) And even then it is not even calculated on quite the same basis: universities include graduates with job offers while the college one does not. So you can’t even quite do an apples-to-apples comparison, even at the level of the sector as a whole. But if you ignore that last small difference in calculation and focus not on the point-estimates but on the trends, you can still see something interesting. Here we go:

Figure 1: Employment Rates 6 months after Graduation, Ontario Universities vs. Ontario Colleges, by Graduating Cohort, 1999-2015


So, like I said, ignore the actual values in Figure 1 because they’re calculated in two slightly different ways; instead, focus on the trends. And if you do that, what you see is (a blip in 2015 apart), the relationship between employment rates in the college and university sector looks pretty much the same throughout the period. Both had a wobble in the early 2000s, and then both took a big hit in the 2008 recession. Indeed, on the basis of this data, it’s hard to make a case that one sector has done better than another through the latest recession: both got creamed, neither has yet to recover.

(side point: why does the university line stop at 2013 while the college one goes out to 2015? Because Ontario doesn’t interview university grads until 2 years after grad and then asks them retroactively what they were doing 18 months earlier. So the 2014 cohort was just interviewed last fall and it’ll be a few months until their data is released. College grads *only* get interviewed at 6 months, so data is out much more quickly)

What this actually goes is put a big dent in the argument that the problem for youth employment is out-of-touch educators, changing skill profiles, sociologists v. welders and all that other tosh people were talking a few years ago. We’re just having more trouble than we used to integrating graduates into the labour market. And I’ll be taking a broader look at that using Labour Force Survey data tomorrow.

January 17

Another Lens on Bleak Graduate Income Data

So, yesterday we looked at Ontario university graduate employment data (link to: previous).  Today I want to zero in a little bit on what’s happening by field of study.

(I can hear two objections popping up already.  First; “why just Ontario”?  Answer: while Quebec, Alberta, British Columbia and the Maritimes – via MPHEC – all publish similar data, they all publish the data in slightly different ways, making it irritating (and in some cases impossible) to come up with a composite national figure.  The National Graduate Survey (NGS) in theory does this, but only every five years but as I explained last week has made itself irrelevant by changing the survey period.  So, in short, I can’t do national, and Ontario a) is nearly half the country in terms of university enrolments and b) publishes slightly more detailed data than most.  Second, “why just universities”?  Answer: “fair point, I’ll be publishing that data soon”.

Everyone clear? OK, let’s keep going).

Let’s look first at employment rates 6 months after graduation by field of study (I include only the six largest – Business/Commerce, Education, Engineering, Humanities, Physical Sciences and Social Sciences – because otherwise these graphs would be an utter mess), shown below in Figure 1.  As was the case yesterday, the dates along the x-axis are the cohort graduation year.


Two take-aways here, I think.  The first is that the post-08 recession really affected graduates of all fields more or less equally, with employment rates falling by between 6 and 8 percentage points (the exception is humanities, where current rates are only four percentage points below where they were in 2007).  The second is that pretty much since 2001, it’s graduates in the physical sciences who have had the weakest results.

OK, but as many in the academy say: 6 months isn’t enough to judge anything.  What about employment rates after, say, 2 years?  These are shown below in Figure 2.


This graph is smoother than the previous one, which suggests the market for graduates with 2 years in the labour market is a lot more stable than that for graduates with just 6 months.    If you compare the class of 2013 with the clss of 2005 (the last one to completely miss the 2008-9 recession), business and commerce students’ employment rates have fallen only by one percentage point while those in social sciences have dropped by six percentage points, with the others falling somewhere in between.  One definite point to note for all those STEM enthusiasts out there: there’s no evidence here that students in STEM programs have fared much better than everyone else.

But employment is one thing; income is another.  I’ll spare you the graph of income at six months because really, who cares?  I’ll just go straight to what’s happening at two years.


To be clear, what figure 3 shows is average graduate salaries two years after graduation in real dollars – that is, controlling for inflation.  And what we see here is that in all fields of study, income bops along fairly steadily until 2007 (i.e. class of 2005) at which point things change and incomes start to decline in all six subject areas.  Engineering was down, albeit only by three percent.  But income for business students was down 10%, physical sciences down 16%, and humanities, social sciences and education were down 19%, 20% and 21%, respectively.

This, I shouldn’t need to emphasize, is freaking terrible.  Actual employment rates (link to: previous) may not be down that much but this drop in early graduate earnings is  pretty disastrous for the majority of students.  Until a year or two ago I wasn’t inclined to put a lot of weight on this: average graduate earnings have always popped back after recessions.  This time seems to be different.

Now as I said yesterday, we shouldn’t be too quick to blame this on a huge changes economy to which institutions are not responding; it’s likely that part of the fall in averages comes from allowing more students to access education in the first place.  As university graduates take up an increasing space on the right-hand side of an imaginary bell-curve representing all youth, “average earnings” will naturally decline even if there’s no overall change in the average or distribution of earnings as a whole.  And the story might not be as negative if we were to take a five- or ten-year perspective on earnings.  Ross Finnie has done some excellent work showing that in the long-term nearly all university graduates make a decent return (though, equally, there is evidence that students with weak starts in the labour force have lower long-term earnings as well through a process known as “labour market scarring”).

Whatever the cause, universities (and Arts faculties in particular) have to start addressing this issue honestly.  People know in their gut that university graduates’ futures in general (and Arts graduates in particular) are not as rosy as they used to be. So when the Council of Ontario puts out a media release, as it did last month, patting universities on the back for a job well-done with respect to graduate outcomes, it rings decidedly false.

Universities can acknowledge challenges in graduate without admitting that they are somehow at fault.  What they cannot do is pretend there isn’t a problem, or shirk taking significant steps to improve employment outcomes.

September 21

Unit of Analysis

The Globe carried an op-ed last week from Ken Coates and Douglas Auld, who are writing a paper for the MacDonald Laurier institute on the evaluation of Canadian post-secondary institutions. At one level, it’s pretty innocuous (“we need better/clearer data”) but at another level I worry this approach is going to take us all down a rabbit hole. Or rather, two of them.

The first rabbit hole is the whole “national approach” thing. Coates and Auld don’t make the argument directly, but they manage to slip a federal role in there. “Canada lacks a commitment to truly high-level educational accomplishment”, needs a “national strategy for higher education improvement” and so “the Government of Canada and its provincial and territorial partners should identify some useful outcomes”. To be blunt: no, they shouldn’t. I know there is a species of anglo-Canadian that genuinely believes the feds have a role in education because reasons, but Section 93 of the constitution is clear about this for a reason. Banging on about national strategies and federal involvement just gets in the way of actual work getting done.

Coates & Auld’s point about the need for better data applies to provinces individually as well as collectively. They all need to get in the habit of using more and better data to improve higher education outcomes. I also think Coates and Auld are on the right track about the kinds of indicators most people would care about: scholarly output, graduation rates, career outcomes, that sort of thing. But here’s where they fall into the second rabbit hole: they assume that the institution is the right unit of analysis for these indicators. On this, they are almost certainly mistaken.

It’s an understandable mistake to make. Institutions are a unit of higher education management. Data comes from institutions. And they certainly sell themselves as a unified institutions carrying out a concerted mission (as opposed to the collections of feuding academic baronetcies united by grievances about parking and teaching loads they really are). But when you look at things like scholarly output, graduation rates, and career outcomes the institution is simply the wrong unit of analysis.

Think about it: the more professional programs a school has, the lower the drop-out rate and the higher the eventual incomes. If a school has medical programs, and large graduate programs in hard sciences, it will have greater scholarly output. It’s the palette of program offerings rather than their quality which makes the difference when making inter-institutional comparisons. A bad university in with lots of professional programs will always beat a good small liberal arts school on these measures.

Geography play a role, too. If we were comparing short-term graduate employment rates across Canada for most of the last ten years, we’d find Calgary and Alberta at the top – and most Maritime schools (plus  some of the Northern Ontario schools) at the bottom. If we were comparing them today, we might find them looking rather similar. Does that mean there’s been a massive fall-off in the quality of Albertan universities? Of course not. It just means that (in Canada at least) location matters a lot more than educational quality when you’re dealing with career outcomes.

You also need to understand something about the populations entering each institution. Lots of people got very excited when Ross Finnie and his EPRI showed big inter-institutional gaps in graduates incomes (I will get round to covering Ross’ excellent work on the blog soon, I promise). “Ah, interesting!” people said. “Look At The Inter-Institutional Differences Now We Can Talk Quality”. Well, no. Institutional selectivity kind of matters here. Looking at outputs alone, without taking into account inputs, tells you squat about quality. And Ross would be the first to agree with me on this (and I know this because he and I co-authored a damn good paper on quality measurement a decade ago which made exactly this point).

Now, maybe Coates and Auld have thought all this through and I’m getting nervous for no reason, but their article’s focus on institutional performance when most relevant outcomes are driven by geography, program and selectivity suggests to me that there’s a desire here to impose some simple rough justice over some pretty complicated cause-effect issues. I think you can use some of these simple outcome metrics to classify institutions – as HEQCO has been doing with some success over the past couple of years – but  “grading” institutions that way is too simplistic.

A focus on better data is great. But good data needs good analytical frameworks, too.

January 28

The Future of Work (and What it Means for Higher Education), Part 2

Yesterday we looked at a few of the hypotheses out there about how IT is destroying jobs (particularly: good jobs).  Today we look at how institutions should react to these changes.

If I were running an institution, here’s what I’d do:

First, I’d ask every faculty to come up with a “jobs of the future report”.  This isn’t the kind of analysis that makes sense to do at an institutional level: trends are going to differ from one part of the economy (and hence, one set of fields of study) to another.  More to the point, curriculum gets managed at the faculty level, so it’s best to align the analysis there.

In their reports, all faculties would need to spell out: i) who currently employs their grads, and in what kinds of occupations (an answer of “we don’t know” is unacceptable – go find out); ii) what is the long-term economic outlook for those industries and occupations? iii) what is the outlook for those occupations with respect to tasks being susceptible to computerization (there are various places to look for this information, but this from two scholars at the University of Oxford is a pretty useful guide)? And, iv) talk to senior people in these industries and occupations to get a sense of how they see technology affecting employment in their industry.

This last point is important: although universities and colleges keep in touch with labour market trends through various types of advisory boards, the question that tends to get asked is “how are our grads doing now?  What improvements could we make so that out next set of grads is better than the current one?”  The emphasis is clearly on the very short-term; rarely if ever are questions posed about medium-range changes in the economy and what those might bring.  (Not that this is always front and centre in employers’ minds either – you might be doing them a favour by asking the question.)

The point of this exercise is not to “predict” jobs of the future.  If you could do that you probably wouldn’t be working in a university or college.  The point, rather, is to try to highlight certain trends with respect to how information technology is re-aligning work in different fields over the long-term.  It would be useful for each faculty to present their findings to others in the institution for critical feedback – what has been left out?  What other trends might be considered? Etc.

Then the real work begins: how should curriculum change in order to help graduates prepare for these shifts?  The answer in most fields of study would likely be “not much” in terms of mastery of content – a history program is going to be a history program, no matter what.  But what probably should change are the kinds of knowledge gathering and knowledge presentation activities that occur, and perhaps also the methods of assessment.

For instance, if you believe (as economist Tyler Cowen suggests in his book Average is Over that employment advantage is going to come to those who can most effectively mix human creativity with IT, then in a statistics course (for instance), maybe put more emphasis on imaginative presentation of data, rather than on the data itself.  If health records are going to be electronic, shouldn’t your nursing faculty be developing a lot of new coursework involving the manipulation of information on databases?  If more and more work is being done in teams, shouldn’t every course have at least one group-based component?  If more work is going to happen across multi-national teams, wouldn’t it be advantageous to increase language requirements in many different majors?

There are no “right” answers here.  In fact, some of the conclusions people will come to will almost certainly be dead wrong.  That’s fine.  Don’t sweat it.  Because if we don’t look forward at all, if we don’t change, then we’ll definitely be wrong.  And that won’t serve students at all.

January 27

The Future of Work (and What it Means for Higher Education), Part 1

Back in the 1990s when we were in a recession, Jeremy Rifkin wrote a book called The End of Work, which argued that unemployment would remain high forever because of robots, information technology, yadda yadda, whatever.  Cue the longest peacetime economic expansion of the century.

Now, we have a seemingly endless parade of books prattling on about how work is going to disappear: Brynjolfsson and McAfee’s The Second Machine Age, Martin Ford’s Rise of the RobotsJerry Kaplan’s Humans Need not Apply, Susskind and Susskind’s The Future of the Professions: How Technology will Transform the Work of Human Experts (which deals specifically with how info tech and robotics will affect occupations such as law, medicine, architecture, etc.), and from the Davos Foundation,  Klaus Schwab’s The Fourth Industrial Revolution. Some of these are insightful (such as the Susskinds’ effort, though their style leaves a bit to be desired); others are hysterical (Ford); while others are simply dreadful (Schwab: seriously, if this is what rich people find insightful we are all in deep trouble).

So how should we evaluate claims about the imminent implosion of the labour market?  Well first, as Martin Wolf says in this quite sober little piece in Foreign Affairs, we shouldn’t buy into the hype that “everything is different this time”.  Technology has been changing the shape of the labour market for centuries, sometimes quite rapidly.  We will go on changing.  The pace may accelerate a bit, but the idea that things are suddenly going to “go exponential” are simply wrong.  Just because we can imagine technology creating loads of radical disruption doesn’t mean it’s going to happen.  Remember the MOOC revolution, which was going to wipe out universities?  Exactly.

But just because the wilder versions of these stories are wrong doesn’t mean important things aren’t happening.  The key is to be able to lose the hype.  And to my mind, the surest way to get past the hype is to clear your mind of the idea that advances in robotics or information technology “replace jobs”.  This is simply wrong; what they replace are tasks.

We get a bit confused by this because we remember all the jobs that were lost to technology in manufacturing.  But what we forget is that the century-old technology of the assembly line had long turned jobs into tasks, with each individual performing a single task, repetitively.  So in manufacturing, replacing tasks looked like replacing jobs.  But the same is not true of the service sector (which covers everything from shop assistants to lawyers).  This work is not, for the most part, systematic and routinized, and so while IT can replace tasks, it cannot replace “jobs”  per se.  Jobs will change as certain tasks get automated, but they don’t necessarily get wiped out.  Recall, for instance, the story I told about ATMs a few months ago: that although ATMs had become ubiquitous over the previous forty years, the number of bank tellers not only hadn’t decreased, but had actually increased slightly.  It’s just that, mainly, they were now doing a different set of tasks.

Where I think there are some real reasons for concern is that a lot of the tasks that are being routinized are precisely the ones we used to give to new employees.  Take law, for instance, where automation is really taking over document analysis – that is, precisely the stuff they used to get articling students to do.  So now what do we do for an apprenticeship path?

Working conditions always change over time in every industry, of course, but it seems reasonable to argue that job change in white-collar industries – that is, the ones for which university education is effectively an entry-requirement – are going to change substantially over the next couple of decades.  Again, it’s not job losses; rather, it is job change.  And the question is: how are universities thinking through what this will mean for the way students are taught?  Too often, the answer is some variation on “well, we’ll muddle through the way we always do”.  Which is a pretty crap answer, if you ask me.  A lot more thought needs to go into this.  Tomorrow, I’ll talk about how to do that.

September 02

Some Basically Awful Graduate Outcomes Data

Yesterday, the Council of Ontario Universities released the results of the Ontario Graduates’ Survey for the class of 2012.  This document is a major source of information regarding employment and income for the province’s university graduates.  And despite the chipperness of the news release (“the best path to a job is still a university degree”), it actually tells a pretty awful story when you do things like, you know, place it in historical context, and adjust the results to account for inflation.

On the employment side, there’s very little to tell here.  Graduates got hit with a baseball bat at the start of the recession, and despite modest improvements in the overall economy, their employment rates have yet to resume anything like their former heights.

Figure 1: Employment Rates at 6-Months and 2-Years After Graduation, by Year of Graduating Class, Ontario














Now those numbers aren’t good, but they basically still say that the overwhelming majority of graduates get some kind of job after graduation.  The numbers vary by program, of course: in health professions, employment rates at both 6-months and 2-years out are close to 100%; in most other fields (Engineering, Humanities, Computer Science), it’s in the high 80s after six months – it’s lowest in the Physical Sciences (85%) and Agriculture/Biological Sciences (82%).

But changes in employment rates are mild compared to what’s been happening with income.  Six months after graduation, the graduating class of 2012 had average income 7% below the class of 2005 (the last class to have been entirely surveyed before the 2008 recession).  Two years after graduation, it had incomes 14% below the 2005 class.

Figure 2: Average Income of Graduates at 6-Months and 2-Years Out, by Graduating Class, in Real 2013/4* Dollars, Ontario














*For comparability, the 6-month figures are converted into real Jan 2013 dollars in order to match the timing of the survey; similarly, the 2-year figures are converted into June 2014 dollars.

This is not simply the case of incomes stagnating after the recession: incomes have continued to deteriorate long after a return to economic growth.  And it’s not restricted to just a few fields of study, either.  Of the 25 fields of study this survey tracks, only one (Computer Science) has seen recent graduates’ incomes rise in real terms since 2005.  Elsewhere, it’s absolute carnage: education graduates’ incomes are down 20%; Humanities and Physical Sciences down 19%; Agriculture/Biology down 18% (proving once again that, in Canada, the “S” in “STEM” doesn’t really belong, labour market-wise).  Even Engineers have seen a real pay cut (albeit by only a modest 3%).

Figure 3: Change in Real Income of Graduates, Class of 2012 vs. Class of 2005, by Time Graduation for Selected Fields of Study














Now, we need to be careful about interpreting this.  Certainly, part of this is about the recession having hit Ontario particularly harshly – other provinces may not see the same pattern.  And in some fields of study – Education for instance – there are demographic factors at work, too (fewer kids, less need of teachers, etc.).  And it’s worth remembering that there has been a huge increase in the number of graduates since 2005, as the double cohort – and later, larger cohorts – moved through the system.  This, as I noted back here, was always likely to affect graduate incomes, because it increased competition for graduate jobs (conceivably, it’s also a product of the new, wider intake, which resulted in a small drop in average academic ability).

But whatever the explanation, this is the story universities need to care about.  Forget tuition or student debt, neither of which is rising in any significant way.  Worry about employment rates.  Worry about income.  The number one reason students go to university, and the number one reason governments fund universities to the extent they do, is because, traditionally, universities have been the best path to career success.  Staying silent about long-term trends, as COU did in yesterday’s release, isn’t helpful, especially if it contributes to a persistent head-in-the-sand unwillingness to proactively tackle the problem.  If the positive career narrative disappears, the whole sector is in deep, deep trouble.

April 13

Five Questions for Ken Coates

So, Ken Coates of the University of Saskatchewan published a paper the week before last arguing that there were too many university students and not enough trades students, so we should reduce university enrolments by a third and what the hell is wrong with kids today anyway?  Despite being not much more than a warmed-over version of the paper he co-authored with Rick Miner in IRPP a couple of years ago, it got some attention because it played directly into both the elitist view of universities (all these students devalue the degree!) and the weird view some in Canada have that the only problem with the labour market is that workers are too stupid to see the opportunities in front of them.

The paper is a hot mess of unfounded assertions and questionable logic which raises at least 5 questions (I’d guess readers can come up with a few more of their own) which I think the author needs to answer before the paper can be taken seriously.

1.Why does Coates keep saying today’s young people feel too “entitled”? What does he mean by this disparaging term?  What evidence is there to suggest this generation display a greater sense of entitlement than any previous generation?  Or is this just an arrogant way of saying youth don’t do what Coates thinks they should do?  (Also: does Coates spend a lot of time yelling at kids to get off his lawn?)

2. Why does Coates repeatedly denigrate the idea that “the labour market should be directed by the uninformed educational choices of 17-19 year-olds”?  Has it not ever been thus?  Was there some golden age in Canadian history when the state or business made career decisions on young peoples’ behalf and where economic outcomes were demonstrably better?  Can Coates name a democratic nation where 17-19 year-olds don’t make their own educational choices?  

3. Why, if as Coates claims, no one can know the future of the labour market, is he so damn sure we need more college/trades graduates?  Coates: “it is extremely difficult to anticipate downstream market demand for employees”.  Coates: “Governments have a poor track record when it comes to picking winners in the economy”.  Well, if that’s true, isn’t this entire paper – which based on the idea that we know that more college/trades education and less university education is a good idea – an enormous waste of time?  (Or, more simply, “wrong”?)

4. What evidence does Coates have for saying Canadians are defaulting “to the traditional view that a university degree is the best avenue to prosperity” and “turning their children’s dreams against blue-collar work”?  Here’s a quick summary of educational attainment for Canadian males, aged 30 or under, who did their post-secondary education in Canada:

 Figure 1: Highest Level of Educational Attainment, Males Aged 30 and Under, Canada, 2010


Got that?  Among males under 30, there are almost as many apprentice and trades certificate holders as there are bachelor’s holders.  Throw the colleges in and it’s more than two to one.  Another way to look at the data is to compare the number of males with Bachelor’s degrees with those working in those “in-demand” area Coates is continually babbling about – construction trades, mechanics, precision production, transportation, and all Engineering sub-fields who have less than a bachelor’s degree.  Here are the numbers:

Figure 2: Bachelor’s Degree Holders vs. Workers in Five Key Trades, Males under 30, Canada, 2010


 In short: Averse to blue-collar work?  Not even vaguely true.


5.   Why does Coates think blue-collar work is so hot anyway?  The problem with blue collar work – apart from the fact that it’s seriously gender-biased – is that it’s cyclical.  A lot of people in Canada – including Coates, apparently – forget that because the current commodities cycle has been going on so long, but when commodities prices fall blue collar outcomes are pretty terrible.  Back in the mid-90s, when oil was cheap, we only had about a quarter as many apprentices as we do now.  In the 1980s, unemployment rates for trades grads was over 15%.  How good do you think blue collar will look if oil is permanently back down to $50 and China’s growth rate heads down to 3%?

Coates does have a point in that universities need to do more to make their graduates employable, and he’s also right that more post-secondary learning needs to be experiential in nature.  But to go from there and say that we need fewer university graduates is just a baseless assertion.  He can and should do better.

February 04

The “Skills for Jobs Blueprint”

I don’t pay as much attention as I should on this blog to matters British Columbian, mostly because I don’t get out there often enough.  But the province’s “Skills for Jobs  Blueprint” cries out for some critical treatment, because frankly it’s not all that smart.

Turn back the clock a bit: in April 2014, the BC government rolled-out a series of policies that were collectively branded as the “Skills for Jobs Blueprint”.  Much of it consisted of relatively sensible changes to trades training in view of the upcoming Liquid Natural Gas (LNG) mega-project.  However, included in this package was some other stuff that sounded like it had been dreamt up on the back of a cocktail napkin.  These included: more generous student aid to students enrolled in disciplines related to “high-demand” occupations, and requiring institutions to spend at least 25% of their budgets on disciplines related to “high-demand” occupations (to be phased in by 2017-18).

The student aid pledge was just silly: if these are truly high-demand occupations, they’ll pay more, and students will have less problem re-paying loans.  Why would you give more money to these people? The requirement for institutional spending had the potential to be ridiculous, but wasn’t necessarily so.  Whatever purists might think, public authorities spend money on higher education mainly to improve the local economy; and besides, depending on how broadly “high-demand” occupations were described, they might already be spending 25%.  There was the possibility, in other words, that it would require no change at all on institutions’ part.  But that would depend crucially on how BC defined “high-demand”.

This is where it gets maddening.  When the government finally released its definition of high-demand, it had nothing to do with a skills gap, and was not in any way based on analyses of supply and demand.  Instead, it was simply the 60 occupations with the most job openings.  Or, put differently: according to the government of BC, the highest-demand occupations are simply the 60 largest occupations.  Oy.

Now, it’s hard to tell whether institutions actually line up 25% of their spending on priority disciplines related to the “big 60”, since BC doesn’t work on any kind of funding formula.  However, it is possible to reverse engineer this kind of thing by looking at enrolment patterns, and assuming that spending weights are similar to what one would see in other provinces (read: Ontario and Quebec), as we demonstrated back here.  Which is what my colleague Jackie Lambert did.

The results were instructive.  Quite clearly, all colleges meet the test.  Among universities, it’s slightly more complicated.  If you simply take all enrolments in the academic programs most directly related to 59 of the 60 “most desired” occupations, and weight them in the ON/QC style, you find that province-wide, these programs already make up 32% of expenditures, and all universities except Emily Carr would meet the 25% cut.  However, the 60th occupation with the most “demand” is university professors (yes, really), which technically can be filled by doctoral students from any program.  Throw those in and you end up with almost 47% of all dollars being spent on “priority” areas.

Ideally, this result would mean the province could just declare victory (“Look!  25%! We showed them!”) and go home.  But these days, government can’t just be seen to be ordering institutions about; they have to actually be ordering them about.  So my guess is BC will avoid declaring victory, and instead use the ambiguity created by the lack of a funding formula to jerk institutions around a bit “(Spend here!  Don’t spend there!”), just to show everyone who’s boss.

Plus ça change…

September 08

Some Scary Graduate Income Numbers

Last week, the Council of Ontario Universities put out a media release with the headline “Ontario University Graduates are Getting Jobs”, and trumpeted the results of the annual provincial graduates survey, which showed that 93% of undergraduates had jobs two years after graduation, and their income was $49,398.  Hooray!

But the problem – apart from the fact that it’s not actually 93% of all graduates with jobs, but rather 93% of all graduates who are in the labour market (i.e. excluding those still in school) – is that the COU release neither talks about what’s going on at the field of study level, nor places the data in any kind of historical context.  Being a nerd, I collect these things when they come out each year and put the results in a little excel sheet.  Let’s just say that when you do compare these results to earlier years, things look considerably less rosy.

Let’s start with the employment numbers, which look like this:

Figure 1: Employment Rate of Ontario Graduates 2 Years Out, Classes of 1999 to 2011














Keep your eye on the class of 2005 – this was the last group to be measured 2 years out before the recession began (i.e. in 2007).  They had overall employment rates of about 97%, meaning that today’s numbers actually represent a 4-point drop from there.  If you really wanted to be mean about it, you could equally say that graduate unemployment in 2013 has doubled since 2007.  But look also at what’s happened to the Arts disciplines: in the first four years of the slowdown, their employment rates fell about two percentage points more than the average (though, since the class of ’09, their employment levels-out).

Still, one might think: employment rates in the 90s – not so bad, given the scale of the recession.  And maybe that’s true.  But take a look at the numbers on income:

Figure 2: Average Income (in $2013) 2 Years After Graduation, Ontario Graduating Classes from 2003-2011, Selected Disciplines














Figure 2 is unequivocally bad news.  The average in every single discipline is below where it was for the class of 2005.  Across all disciplines, the average is down 13%.  Engineering and Computer Science are down the least, and have made some modest real gains in the last couple of years; for everyone else, the decline is in double-digits.  Business: down 11%.  Humanities: down 20%.  Physical Sciences: down 22% (more evidence that generalizations about STEM disciplines are nonsense).

Now, at this point some of you may be saying: “hey, wait a minute – didn’t you say last year that incomes 2 years out were looking about the same as they did for the class of 2005?”  Well, yes – but you may also recall that a couple of days later I called it back because Statscan did a whoopsie and said: “you know that data we said was two years after graduation?  Actually it’s three years out”.

Basically, the Ontario data is telling us that 2 years out ain’t what it used to be, and the Statscan data is telling us is that three years out is the new two; simply, it now takes 36 months for graduates to reach the point they used to reach in 24.  That’s not a disaster by any means, but it does show that – in Ontario at least – recent graduates are having a tougher time in the recession.

Tomorrow: more lessons in graduate employment data interpretation.

Page 1 of 41234