Results for “education signaling”

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Let’s say you signal your way into a first job, then learn a lot from holding that perch, and enjoy a persistently higher income for the rest of your life.  Is that a return to signaling or a return to learning?  Or both?

Maybe it matters that “the signaling came first.”  Well, try this thought experiment.

Let’s say you have to learn to read and write to signal effectively.  Can we run a causal analysis on “learning how to read and write”?  Take away that learning and you take away the return to signaling.  Should we thus conclude that the return to signaling is zero, once we take learning into account?  After all, the learning came first.  No, not really.

The trick is this: when there are non-additive, value-enhancing relationships across inputs, single-cause causal experiments can serve up misleading results.  In fact, by cherry-picking your counterfactual you can get the return to signaling, or to human capital, to be much higher or lower.  Usually one is working in a model where the implicit marginal causal returns to learning, IQ, signaling, and so on sum up to much more than 100%, at least if you measure them in this “naive” fashion.  If you think of a career in narrative terms, IQ, learning, and signaling are boosting each others’ value with positive and often non-linear feedback.  And insofar as these labor market processes have “gatekeepers,” it is easy for the marginal product of any one of these to run very high, again if you set up the right thought experiment.

Along related lines, many people use hypothetical examples to back out the return to signaling, learning, IQ, or whatever.  “Let’s say they make you drop out of Harvard and finish at Podunk U.”  “Let’s say you forge a degree.”  “Let’s say you are suddenly a genius but living in the backwoods.”  And so on.  These are fun to talk and think about, but like the above constructions they will give you a wide range of answers for marginal returns, again depending which counterfactual you choose.  A separate point is that many of these are non-representative examples, or they involve out of equilibrium behavior.

I call the methods discussed in the above few paragraphs the single-cause causal measures, because we are trying to estimate the causal impact of but a single cause in a broader non-additive, multi-causal process.

There is another way to analyze the return to signaling, and that is to leave historical causal chains intact and ask what if a degree is removed.  Let’s say I’ve held a job for ten years and my team is very productive.  But the boss can’t figure out who is the real contributor.  I get an especially large share of the pay because, from my undergraduate basket weaving major, the boss figures I am smarter than those team members who did not finish college at all.  If I didn’t have the degree, I would receive $1000  less.  So that year the return to signaling is $1000.  I call this the modal measure.  It is modal rather than causal because we take my degree away in an imaginary sense, without taking away my job (which perhaps I would not have, earlier on, received without the degree).

There are also the measures (not easy to do) based in notions from bargaining theory.  Consider IQ, learning, and signaling as coming together to form “coalitions.”  One-by-one, remove different marginal elements of the coalition in thought experiments, estimate the various marginal products, and then average up those marginal products as suggested by various bargaining axioms.  You could call those the multi-cause causal measures.  They are more theoretically correct than the single-cause causal measures, but difficult to do and also less fun to talk about.

Yet another method is to pick out a single counterfactual on the basis of which policy change is being proposed.  I’ll call these the policy measures.  Let’s say the proposal is to subsidize student transfer from community colleges to four-year institutions.  You can then ask causal questions about the group likely to be affected by this.  (It is possible to estimate the private return to education for this kind of policy, but hard to break that down into signaling and learning components.)  In any case the answers to these questions will not resolve broader debates about the relative importance of signaling, learning, IQ, and so on and how we should understand education more generally.

Usually when people argue about the return to signaling, they are conflating the single-cause causal measures, the modal measures, the bargaining theory measures, and the policy measures.  The single-cause causal measures are actually the least justified of this lot, but they exercise the most powerful sway over most of our imaginations.

The single-cause causal measures are especially influential in the blogosphere, where they make for snappy posts with vivid narrative examples and counterexamples.  But they are misleading, so do not be led astray by them.

On Twitter Bryan asks me:

Would you state your human capital/ability bias/signaling point estimates using my typology?

He refers to this blog post of his, though he does not clearly define the denominator there: is it percentage of what you spend on education or explaining what percentage of the variation in lifetime earnings?  I’ll choose the latter and also I’ll focus on signaling rather than trying to separate out which parts of human capital are from birth and which are later learned.  My calculations are thus:

1. I don’t wish to count “credentialed” occupations, where you need a degree and/or license, but you are reaping rents due to monopoly privilege.  It’s neither human capital nor signaling (it’s not about intrinsic talent), though you could argue it is a kind of human capital in rent-seeking.  In any case, let’s focus on private labor markets without such barriers.

2. Most capital and resource income is due to factors better explained by human capital theories or due to inheritance.  That is more than a third of earnings right there.  Note that the higher is inequality, the less the signaling model will end up explaining.  That is one reason why the signaling model has become less relevant.

3. Depending on job and sector, what you’ve signaled, as opposed to what you know, explains a big chunk of wages in the first three to five years of employment.  Within five years (often less), most individuals are earning based on what they can do, setting aside credentialism as discussed under #1.  Here is my earlier post on speed of employer learning.

Keep in mind that everyone’s wages change quite a bit over their lifetime and that is mostly not due to retraining (i.e., changes in the educational signal) in the formal sense, as most people stop formal retraining after some point.  The changes are due to employer estimates of skill, modified by bargaining power.  In this sense all theories are predominantly human capital theories, whether they admit it or not.

To be generous, let’s give Bryan the full first five years of income based on signaling alone, out of a forty year career.  And let’s say that on average wages rise at the rate of time discount (not true as of late, but a simplifying assumption and I think Bryan believes in a claim like this anyway.)

How much of income is explained by signaling?  I’m coming up with “1/8 of 2/3,” the latter fraction referring generously to labor’s share in national income.  That will fall clearly under ten percent, but recall I’ve inserted some generous assumptions here.

Bryan wants to call me “a signaling denialist,” yet I see signaling as still very important for understanding some aspects of the labor market.  But it’s far from the main story for the labor market as a whole, especially as you move into the out years.

That all said, this “decomposition” approach may obscure more than it illuminates.  Let’s consider two parables.

First, imagine a setting where you need the signal to be in the game at all, but after that your ingenuity and your personal connections explain all of the subsequent variation in income.  Depending what margin you choose, the contribution of signaling to later income can be seen as either zero percent or one hundred percent.  Signaling won’t explain any of the variation of income across people with the same signal, yet people will compete intensely to get the signal in the first place.

Second, in a basic signaling model there are two groups and one dimension of signaling.  That’s too simple.  A signaling model implies that a worker is paid some kind of average product throughout many years, but of course the reference class for defining this average product is changing all the time and is not, over time, based on the original reference class of contemporaneous graduating peers.  For the purposes of calculating your wage based on a signal, is your relevant peer group a) all those people who got out of bed this morning, b) all those people in the Yale class of 2012, or c) all those who have been mid-level managers at IBM for twenty years?  This will change as your life passes.

So there’s usually a signaling model nested within a human capital model, with the human capital model determining the broader parameters of pay, especially changes in pay.  The employer’s (reasonably good but not perfect) estimate of your marginal product determines which peer group you get put into, if you choose to invest in additional signals (or not).  The epiphenomena are those of a signaling model, but the peer group reshufflings over time are ruled by something else.  Everything will look like signaling but again over time signaling won’t explain much about the variation or evolution in wages.

Seeing the relevance of those “indeterminacy” and “nested” perspectives is more important than whatever decomposition you might cite to answer Bryan’s query.

It’s a well-known fact — well-known around GMU that is — that GMU graduates earn higher average salaries than do UVA grads (direct link here), that is for four year undergrads in their first year of employment.

It’s not just that UVa is in decline, or that some of them end up richer later in life.  Or others may use their wealthier parents to live in Williamsburg, Brooklyn and avoid direct employment.  A major reason for the wage discrepancy is simply that a disproportionate chunk of GMU students are likely to get jobs in the relatively high-paying Washington, D.C. area.

OK, so how does this relate to the broader ongoing debate over the signaling theory of education and wages?

It is widely accepted that UVa is a more exclusive school than GMU by the usual standards.  Yet here we see labor markets “seeing through” those credentials, and paying more to the GMU graduates.  In other words, labor markets are seeing that GMU students are, on average, “less exclusive by origin but will have a higher marginal product very quickly.”

The signaling model, in its simplest, most stripped down form, assumes that employers cannot judge the marginal products of individual new hires but instead pay them according to their credentials.  Yet here we have a case where employers seem quite willing to make a judgment about marginal product and indeed that is a judgment which contradicts data on exclusivity of academic origins.  Once you postulate that employers are willing to make estimates of individual marginal products which differ from the rankings that might be given by “raw ability,” the signaling model is  less applicable.  I don’t want to claim that the wages converge exactly on marginal products, but the credentials clearly are just one factor of many.  Employer judgments of expected marginal products are not dominated by credentials, and you can imagine that after having a worker for a year or two the credentials are even less important as a means of judging prospective marginal product.

Another way to put this point is that the speed of employer learning is in fact fairly rapid, and some of it happens before the job even starts.

Bryan Caplan doubts that on-line education will lead to many bankruptcies in higher education.  To provide a contrasting point of view, I see the landscape as follows:

1. The absolute wages of college graduates have been falling for over a decade, even though the relative premium over “no college degree” is robust.  Still, absolute wages do determine the long-term viability of any revenue model.  And note that a pretty big chunk of the relative college wage premium is captured through post-secondary education only.

2. The “debt bubble” behind a lot of recent higher education expansion won’t be repeated anytime soon.

3. A large number of institutions in the top one hundred will move to a hybrid on-line model for a third or so of their classes and they will do so gradually, without seriously disrupting norms of conformity or eliminating campus life.  In fact this will become the new conformity and furthermore through time-shifting it may increase the quantity and joy of drunken parties and campus orgies.  Eventually these on-line classes will be sold for credit to outside students.  Some top schools will sell credits in this manner, even if the more exclusive Harvard and Princeton do not.  Many lesser schools will lose a third or so of their current tuition revenue stream.  Note that the prices for these on-line credits, even if hybrid, will likely be much lower, plus lesser schools lose revenue to the schools better at designing on-line content.

4. Some state governors will try to put out a supposedly semi-passable degree from their state schools for 10k a year, with some on-line components of course.  That will put price and revenue pressure on many other schools.

So let’s say you are Trinity International University, in Deerfield, Illinois, 1,265 students, nominal tuition about 26k.  I had never heard of that place before doing a quick search through U.S. News rankings.  Still, it is rated in the second tier.  Will it survive?  Maybe their Evangelical orientation will push them through.  Maybe it will sink to 500 students.

How about Lynn University, in Florida, also second tier, nominal tuition listed as 32k?  1,619 students, but how many by say 2032?

I don’t think bankruptcy, literally interpreted, is the likely legal outcome (for one thing, these schools probably don’t have enough debt for bankruptcy law to be relevant).  Still, I think it is quite possible that one hundred or more schools in the U.S. News rankings will find their enrollments or at least their tuition revenue streams cut in half or more within twenty years.  They will be shells of their former selves, though on-line education might not even be their major economic challenge.  It will be one of three or four major whammies facing them.  Higher education as a general practice of course still will thrive, as will community colleges.

One key question is whether on-line education will encourage consolidation or not.  Under one vision, on-line offerings shore up the smaller schools, because you can go to them for the atmosphere while taking German III purely on-line.  (Even then, they survive but the revenue stream takes a huge whack.)  Under another vision, on-line — for most students — works best in hybrid form, mixed with various face-to-face forms, and the larger schools will have a much easier time getting this off the ground in a workable manner.

Two additional comments on Bryan’s post.  First, he thinks that for on-line education “…the dollars of venture capital raised are laughable.”  Yet keep in mind that the major players are or can be backed by the endowments of the top universities.  In any case, why raise extra money before you are able to spend it?  If these on-line efforts get any traction at all, the funding and lines of credit will be there.

Second, advocates of the relevance of the signaling model should be relatively optimistic about on-line education.  Because it is hard to pay attention in the on-line schoolhouse, it provides an especially potent signal!  And you always face the temptation to upgrade your signal by subbing in some Top School on-line credits for some of your Podunk University credits.  (Sooner or later Podunk will have to accept such credits.)  Social pressures for conformity will encourage rather than stop that trend.  On the other hand, if you subscribe to a learning model for higher education, there are some very legitimate questions as to how well the on-line product can teach you what you need to know, at least for people with some fairly wide variety of learning styles.

Conformity pressures and signaling may militate against the “stay at home all day” forms of on-line education, but not against on-line education more generally, in fact quite the contrary.  In my view Bryan is underestimating the economic problems to be faced by a wide range of colleges and universities, and putting up a not very plausible model of non-conformist on-line ed as the major threat.

Addendum: Matt Yglesias comments.

Via Reihan, this is an excellent blog post.  Rather than excerpt, let me reproduce the whole thing:

By now, you may be getting sick of reading articles and blog posts about the crisis in higher education. This post is different. It proposes an explanation of why students have been willing to pay more and more for undergraduate and professional degrees at the same time that these degrees are becoming both less scarce and more dumbed down. And that explanation rests on a simple and plausible economic hypothesis.

First, let me dispose of the idea that “college (and business school) is all about signaling.” The explanation I present allows signaling to represent a major part of the value of higher education, but it says that the historical increase in willingness to pay for education is not caused by an increase in its signaling value. (And the evidence for signaling or screening education premia, as opposed to human capital accumulation, is pretty thin anyway.) I’m certain signaling plays a role in creating value for certain degrees from certain institutions for certain people in certain situations. That it dominates the value proposition for college seems like a stretch.

My hypothesis is that it is precisely the dumbing down of U.S. education over the last decades that explains the increase in willingness to pay for education. The mechanism is diminishing marginal returns to education.

Typical graduate business school education has indeed become less rigorous over time, as has typical college education. But typical high school education has declined in quality just as much. As a result, the human capital difference between a college and high-school graduate has increased, because the first increments of education are more valuable on the job market than the later ones. It used to be that everybody could read and understand something like Orwell’s Animal Farm, but the typical college graduates could also understand Milton or Spencer. Now, nobody grasps Milton but only the college grads can process Animal Farm, and for employers the See Spot Run–>Animal Farm jump is more valuable than the Animal Farm–>Milton jump.

So the value of a college education has increased even as its rigor has declined, because willingness to pay for quality is really willingness to pay for incremental quality. This principle holds true in many markets. For example, a roof with mean time to failure of 5 years is a lot more valuable than one with a MTF of 2 years, but a 25-year MTF isn’t that much better than a 22-year MTF for most owners. A fuel economy increase from 12 to 15 miles per gallon is a bigger deal than an increase from 27 to 30 MPG.

Empirical points in favor of this diminishing marginal returns/reduced overall rigor hypothesis:

1. Rigor appears to be declining over time at all levels of American education.

2. Rate of return evidence classically suggests that the big marginal gains to education come from lower levels of education.

3. The median wages of college graduates have been flat, but the median wages of high-school-only graduates have gone down even more.

4. The MBA market has continued to support higher tuitions and enrollment despite the secular trend in rigor.

5. Employers increasingly favor those with more education even as they complain more about the quality of the graduates they hire.

Additional implications:

1. The incremental human capital gained from attending a (truly) better school rather than a typical school is increasing, since the additional learning is more basic (and hence more valuable) than it used to be.

2. Five and six-year undergraduate-to-masters programs should grow to accommodate those who would benefit from additional human capital.

3. More-rigorous high schools will attract larger premia (in either tuition, ability to be selective, or, for public schools, their impact on local property values), because at lower overall levels of rigor the increment of human capital is worth more.

Extensions of the logic to signaling considerations:

1. If you accept that the marginal ability and effort necessary to acquire education increases in the level of education (the flip side of the assumption about diminishing marginal payoff), then the signaling value of the typical degree is actually declining. The innate ability difference between the college and high-school-only graduate shrinks as both curricula are made less rigorous.

2. Signaling by the quality of the institution attended and the difficulty of the major subject studied is becoming more important; a very selective (or hard to complete) school or major adds back some of the lost signaling power of the typical degree.

3. We should see college degrees becoming more important in occupations that wouldn’t seem to “require” them under the old model of college, such as service staff in food service and hospitality jobs.