They are easy to spot. A certain glaze over the pupils; quick, frequent glances this way and that; puzzled pauses before adjusting course and setting out again in another direction: first year university and college students look so terribly lost during the first few days of school because, in fact, they are.
And quite understandably so: finding the right place to be at the right time is no small matter in a sea of thousands.
But surely the really difficult thing to figure out is not where you should be, but rather what you should be? Engineer or electrician? Anthropologist or accountant? Lab technician or teacher? Make a wrong turn in these hallways and you will pay for years.
But so will the rest of us. The economy needs only a certain number of actors and teachers, and right now probably a lot more electricians. If the post-secondary system gets this mix wrong, the consequences are unnecessarily high levels of structural unemployment, and lower economic growth.
But ultimately it is students themselves who collectively decide how many stage-managers, human rights activists, and biochemists will appear on the labour market in four or five years time.
Can we really trust eighteen years-olds to make these decisions?
Well it turns out that yes we can, or at least with some qualifications.
In a just released study, researchers from the University of Toronto found that students chose their careers with a clear-headed understanding of their potential earnings.
The higher the expected earnings in a field of study, the greater the enrollment.
Morley Gunderson and Harry Krashinsky found that this cold calculus influenced not just the decisions of those who graduated with degrees in Business, but also the scientists and engineers, those in the health sciences, as well as those choosing education.
Even those who studied in the Fine Arts and Humanities were sensitive to price signals: the lower the wages earned by previous cohorts of graduates, the less likely students were to make this their field of study.
But there is one big exception: students choosing the social sciences—that collective which includes the anthropologists, the sociologists, the women studies majors, the criminologists, the political scientists, and yes even the economists—were more likely to enroll, the lower their expected future earnings.
The researchers—good economists that they are—were stumped, writing: “We do not have an easy explanation for this anomaly and it may be simply just that — an anomaly.”
But the effect is strong—among the most sensitive they find—and since it refers to a significant proportion of the student population it is hard to dismiss for the purposes of public policy.
After all, in spite of this finding they draw the implication that to some important degree the market works: a higher wage in a particular occupation sends a signal of a relative shortage and encourages more people to get the necessary qualifications.
Yet, employers in certain parts of the country are repeatedly calling for more workers with the right skills, and the current national unemployment rate of 7.3% likely reflects a significant structural component due to skill mismatches.
Gunderson and Krashinsky may still be right that the market works, but perhaps not as quickly as it could.
Often the blame for this is placed at the doorstep of the universities. Indeed, the Ontario government is currently in the midst of a consultation process on the future of the province’s post-secondary system, which will surely lead some to call for admissions decisions that are more sensitive to labour market needs, and perhaps even a downsizing of the social sciences.
But in a rush to fix things, there is a risk of making matters worse.
It has long been known that periods of excess demand for certain skills are followed by unemployment and falling wages. This happens because incoming cohorts of students making career choices, university administrators setting admissions limits, governments determining immigration policies, and employers who drive the demand for labour, form their expectations of future wages according to past experience.
Indeed, this is exactly how Professors Gunderson and Krashinsky measure earnings prospects, by looking backward. The earnings students can expect to make in five or ten years are assumed to be what previous graduates are making.
A pattern of boom and bust is particularly evident in the market for engineers: witness the run up of wages in IT during the 1990s, the call to open the doors to more computer scientists, and the bust, stagnant earnings and unemployment that followed in the early 2000s.
Just maybe the social scientists are the one’s playing the game to most advantage.
Probably the most fundamental appeal that should be made to public policy is the need for more detailed information. This particular study was based upon a Statistics Canada survey that offers a great template, but as it stands is too small to examine the actual disciplines students are studying, never mind offering information for specific universities. Prospective students and post-secondary administrators would benefit most from this kind of detail before any fundamental reforms are made, and with it market signals may be even more clear.
5 thoughts on “How the invisible hand points students to a job”
Hi Miles! It couldn’t just be simultaneous equations estimation stuff, could it? When we find higher earnings go with higher enrolment we are estimating the supply curve, and when we find lower earnings gos with higher enrolment we are estimating the demand curve?
(I haven’t read the paper, and I’m no good at econometrics.)
Thanks Nick, this is a reasonable thought but the authors are more careful about this than my summary of their paper suggests. They estimate the returns to education using demand shocks from technical change as an instrument (though they also provide least squares estimates, the IV is the basis for the analysis and discussion). I avoid going into the technical side of the paper, but I don’t think we can criticize them on this point.
There have been more college students in the US looking for jobs on Wall Street in the last decade because of the potential for higher earnings. I even know some bright people with medical degrees who decided to take their skills to Wall Street. Wall Street has also attracted many highly trained scientists who have used their math skills to develop quantitative products and tools. This may not continue in the future, and it may not have been the best use of our brightest students.
I don’t believe the story about structural unemployment and the poor fit between the skills available and available jobs. We had very low unemployment before the recession, and the skills of the workforce, and the mix of available jobs, has not shifted enough in the last decade to explain high unemployment.
More students have been attracted to the social students and the humanities in our colleges because these are subjects that they like and many do not have the skills and talents to pursue more technical programs. The better students from these programs have usually been able to find good jobs with employers who valued their talents and who were willing to train them in job specific skills. One of our problems is that many employers are less willing to invest in training programs.
Thank you Norman. The Wall Street example is a good one. However, I should clarify one point. I agree with you about structural unemployment in the US context. I realize there is a certain debate right now about how much structural unemployment there is, and this is very important for public policy. If current levels of unemployment are mostly due to a mismatch between the characteristics and skills of workers and the demands and requirements of employers then the case for more stimulus through monetary and fiscal policy is weakened, and indeed people could even be making the argument that policies designed to financially support the unemployed are part of the problem. This, for example, is the case that Casey Mulligan seems to be making in a post on the NY Times blog today at http://economix.blogs.nytimes.com/2012/09/05/social-insurance-and-layoffs/ .
But I should make clear that when I made this statement in my post I was referring to the Canadian context. Currently the unemployment rate in Canada is 7.3%, and even as low as 6% or so if it is to be compared with the US (see https://milescorak.com/2012/05/04/the-gap-between-us-and-canadian-unemployment-rates-is-bigger-than-it-appears/). The other feature of the Canadian labour market is a very sharp regional divide, with excess demand of labour in some western parts of the country but excess supply in the east. I hesitate to put a number on it, but some part of this 7% is structural. I don’t feel this applies to the United States.
The last point you make is also very important. Many economic studies of the return to education deal with an “average” return, but the value of education varies a good deal from individual to individual, and we should recognize that in the context of good information individuals are in the best position to make decisions for themselves. My own feeling, and the point of the post, is that we could be doing something to improve the information upon which these decisions are made.
One of the reasons employers are hesitant to invest in training is that many of the required skills are “general”, in the sense that they are of value to other employers. So in a competitive market employers are less likely to pay for skills and risk seeing that investment disappear if the employee leaves. Employers are more likely to pay for “specific” skills that are of value only in their particular workplace.
Thanks for the clarifications and the added value from your response. Keep up the good work.