Category: Education

Bryan Caplan has a very good post on the human capital and signalling models of education. The key point is this, under the human capital model someone who forgets knowledge is no better than someone who failed to learn the same knowledge. Under the signaling model, however, failing and forgetting are very different. Bryan illustrates:

If I’d failed Spanish, I couldn’t have gone to a good college, wouldn’t have gotten into Princeton’s Ph.D. program, and probably wouldn’t be a professor.  But since I’ve merely forgotten my Spanish, I’m sitting in my professorial office, loving life.

Let’s turn the mike over to Alex, our Alex, the Alex, etc., the one who writes for MarginalRevolution:

The rags to riches to rags story of a poor, unemployed fellow who wins the lottery, blows the cash, and ends up just as poor and unemployed as he began is a common trope.  (Here is a classic in the genre). In a paper just published in the Review of Economics and Statistics (gated, free version here), Hankins, Hoekstra and Skiba argue that the rags to riches to rags story has a systematic component.

The authors link records of lottery winners to bankruptcy records. The use of the lottery is a great randomization device, although obviously it restricts the sample to people who play the lottery.

The central finding is this: people who win large amounts are just as likely to end up bankrupt as people who win small amounts…

Here is more, hat tip goes to Slocum.

Addendum: Floccina writes in the comments: “Former professional athletes are also an interesting case.”  Are the economic variables really driving the dysfunctional social norms, or vice versa, or most likely quite a bit of both?

Writing at New Geography Daniel Jelski offers a critique of (some of) Launching the Innovation Renaissance. We are in basic agreement about the laws of future employment:

Law #1: People will get jobs doing things that computers can’t do. Law #2: A global market place will result in lower pay and fewer opportunities for many careers. (But also in cheaper and better products and a higher standard of living for American consumers.) Law #3: Professional people will more likely be freelancers and less likely to have a steady job.

[But]…Laws #1 & 2 predict that there will likely be fewer STEM jobs in the future – they are both easily computerized and tradable. People will always be employed in STEM disciplines, many of them highly paid, but they’ll be paid for smarts rather than education. The disciplines will be much more competitive, with older and less talented workers left on the sidelines. Tom Friedman and Alex Tabarrok, reflecting conventional wisdom,  are mistaken in maintaining that increasing STEM education is a key to future economic competitiveness.

Jelski instead recommends English lit and psychology, at least if you are young and hot!  The logic–computers don’t write well and people don’t want to have sex with or be counseled by computers (yet!),–seems strong but wage rates and unemployment levels don’t support the argument. Jelski is correct about demand but forgets to take into account supply. Thus, the way to go is to be a hot engineer who can write well and get along with other people. (Jelski also forgets that my argument for STEM was in large part about the spillover effects).

I am in strong agreement with Jelski, however, that education is only the first step to success. Education is a tool; to truly succeed one must have skills developed with grit and applied with passion.

Good piece in the New York Times making three points about foreign students in U.S. universities 1) State budgets for education have been slashed, 2) foreign students are way up and because they are paying much higher tuition than in-state students they are supporting education for citizens, 3) selling education services is one way our trade deficit with China is balanced.

This is the University of Washington’s new math: 18 percent of its freshmen come from abroad, most from China. Each pays tuition of $28,059, about three times as much as students from Washington State. And that, according to the dean of admissions, is how low-income Washingtonians — more than a quarter of the class — get a free ride.

Not everyone is happy, however. Here is one (ironic?) complaint:

“Morally, I feel the university should accept in-state students first, then other American students, then international students,” said Farheen Siddiqui, a freshman from Renton, Wash., just south of Seattle.

The puzzle is why firms pay huge sums to big name consulting firms, when their advice comes from kids fresh out of college, who spend only a few months studying an industry they previous knew nothing about. How could such quickly-created advise from ignorant college students be worth the millions paid? Why don’t firms just ask their own internal recent college grads?

Some say that consulting firms use their access to collect data on best practices, data that other firms are eager to pay for. But while this probably contributes, I find it hard to see as the main effect.

My guess is that most intellectuals underestimate just how dysfunctional most firms are. Firms often have big obvious misallocations of resources, where lots of folks in the firm know about the problems and workable solutions. The main issue is that many highest status folks in the firm resist such changes, as they correctly see that their status will be lowered if they embrace such solutions.

The CEO often understands what needs to be done, but does not have the resources to fight this blocking coalition. But if a prestigious outside consulting firm weighs in, that can turn the status tide. Coalitions can often successfully block a CEO initiative, and yet not resist the further support of a prestigious outside consultant.

To serve this function, management consulting firms need to have the strongest prestige money can buy. They also need to be able to quickly walk around a firm, hear the different arguments, and judge where the weight of reason lies. And they need to be relatively immune from accusations of bias – that their advice follows from interests, affiliations, or commitments.

All three of these functions seem to be achieved at a low cost by hiring good-looking kids from our most prestigious schools. These are the cheapest folks you can buy with our most prestigious affiliations, they are smart enough to judge where reason lies, and they have few prior affiliations to taint them with bias. They can not only “borrow your watch to tell you the time,” but can also cow you into submission in accepting that time.

Yes the information contained in consulting advice can be obtained elsewhere at a lower cost. Firms could hire most any smart independent folks, or set up a prediction market. But alas those sources don’t have the raw strength of status to cow opponents into submission, opponents who in practice can block changes no matter what a CEO declares.

So mine is a signaling and status story (surprise surprise). The weight of status often decides outcomes, no matter what the CEOs commands, and so CEOs often need to bring out status ringers, to cow opponents into submission.

Here is a bit more.

The age structure of achievement is being ratcheted upward, due to specialization and the growth of knowledge.  Mathematicians used to prove theorems at age 20, now it happens at age 30, because there is so much to learn along the way.  If you are a smart 22-year-old, just out of Harvard, you probably cannot walk into a widget factory and quickly design a better machine.  (Note that in “immature” economic sectors, such as social networks circa 2006, young people can and do make immediate significant contributions and indeed they dominated the sector.)  Yet you and your parents expect you to earn a high income — now — and to affiliate with other smart, highly educated people, maybe even marry one of them.  It won’t work to move to Dayton and spend four years studying widget machines.

You will seek out jobs which reward a high “G factor,” or high general intelligence.  That means finance, law, and consulting.  You are productive fairly quickly, you make good contacts with other smart people, and you can demonstrate that you are smart, for future employment prospects.

The rest of the world is increasingly specialized, so the returns to your general intelligence, as a complementary factor, are growing too, in spite of your lack of widget knowledge.  “Hey you, think about what you are doing!  Are you sure?  How about this?” often sounds bogus to outsiders but every now and then it pays off and generates a high expected marginal product.

Both supply and demand sustain this Smithian equilibrium.

There are other factors of relevance, as explained over a very good session last night; the people there comprised about half of my Twitter feed.

Acemoglu and Autor present a few non-controversial stylized facts about labor markets, including falling wages of low-skill workers, flattening of the wage premium for workers with less education than college completion, non-monotone shifts in inequality, polarization of employment in advanced economies, and skill-replacing technologies (and don’t forget the new Brynjolfsson and MacAfee book; it is important).

The simplest model is that, because of information technology, employers demand more skills.  The job market responds accordingly, and eventually the education system responds too.  The major shifts are driven by changing productivities of human capital, and that is one reason why the human capital model of labor markets has proven so robust.  It accounts (mostly) for the big changes in labor market returns.

What would a signalling model predict as the results of skill-biased technical change?  I am never sure.  Those models are tricky with comparative statics predictions for at least three reasons:

1. Multiple equilibria are common and arguably essential,

2. It is assumed that employers cannot in the short run (medium run?) observe the marginal products of workers, and

3. The (supposed) relevant factor for employers, the degree, is past history and, if not quite carved in stone, credentialed retraining remains the exception in many market segments.   It hardly drives wage outcomes or observed changes in wages.

The simplest (non-signaling) model is that wages follow MP, albeit with some lag, and adjusting for a suitably sophisticated notion of marginal revenue product, including morale effects on other workers.

Again, how should skill-based technical change matter in a signaling model?  In the model, no employer observes (across what time horizon?) that the MPs of some workers have gone up and that other workers’ MPs have gone down.  Yet it seems that changing MPs matter at margins.  And if employers can sniff out changing MPs, this implies they can sniff out large MP differences more generally, which limits the scope of educational signaling.

It is a strong result these days that occupation and also job tasks predict earnings better than before (see pp.26-27 in the first link), including relative to level of education.  That also seems to run counter to what signaling theories predict.  Most likely we are now better at measuring the quality of workers and their educational signals don’t matter as much as they used to.  The higher returns to post-secondary education, which account for most of the recent growth in the returns to college degrees (p.145 and thereabouts), are skill-based and they are tightly connected to occupation and job tasks.

These are all reasons why the signaling model for education is not growing in popularity, namely that  it does not speak well to current comparative statics and to the current big stories in labor markets.

It is an embarrassing question for signaling models to ask: with what lag do employers get a good estimate of a worker’s marginal product?  If you say “it takes 37 years” it is hard to account for all the recent changes in wage rates in response to technology, as discussed above.

Alternatively, let’s say the lag is two years.  There are several RCT estimates of the return to education, based on earnings profiles measured over twenty or thirty year periods.  The estimated returns to education are high, and if those returns were just signaling-based you would expect the IV-elevated individuals to show up as underskilled and for the credentials-based wage gains to fall away with a few years’ time.  That doesn’t happen (if you are wondering, the IV-elevated individuals are those who for essentially random reasons end up getting more education, or an instrumental variable proxies as such, without the elevation being correlated with their underlying quality as workers,).

In other words, the signaling model is caught between two core results — high long-term measured returns to the education of IV-elevated individuals, and technology drives wage changes in the medium-term.  It is hard for a signaling model to explain both of those changes at the same time.

There is a way to nest signaling models within human capital models, rather than viewing them as competing hypotheses.  Using matching theories, let’s say employers learn the quality of workers they have, but find it hard to estimate the quality of workers they don’t have.  IV-elevated workers can’t fool the market/the employer for very long, and so their high pecuniary returns from education really do measure productivity gains.  Nonetheless there can be undervalued “diamonds in the rough.”  Think of them as geniuses, or at least good workers, who hate getting the education.

From the point of view of these students (or dropouts, as the case may be), the signaling model will appear to be true.  They will resent the education and they won’t need the education.  If it is costly enough to sample worker quality from the “outsiders bin,” it will remain an equilibrium that a degree is required to get the job, at least provided workers of this kind are not too numerous.  If there were “lots and lots” of such workers, more employers would scrounge around in the outsider’s bin.  In other words, the anecdotal evidence for signaling fits into a broader model precisely because such cases aren’t too common.

From Rich Miller and Jennifer Ryan:

At MIT, King, 63, and then-professor Ben S. Bernanke, 58, had adjoining offices in 1983, spending the early days of their academic careers in an environment where economics was viewed as a tool to set policy. Earlier, Bernanke and European Central Bank President Mario Draghi, 64, earned their doctorates from the university in the late 1970s, Draghi with a thesis entitled “Essays on Economic Theory and Applications.”

Fischer, 68, advised Bernanke’s thesis on “Long-Term Commitments, Dynamic Optimization and the Business Cycle,” and taught Draghi. Greek Prime Minister and former ECB vice president Lucas Papademos and Olivier Blanchard, now chief economist for the International Monetary Fund in Washington, earned their doctorates from MIT at about the same time.

Other monetary policy makers who have passed through MIT’s doors include Athanasios Orphanides, head of the Central Bank of Cyprus, Duvvuri Subbarao, governor of the Reserve Bank of India and Charles Bean, King’s deputy in the U.K.

Central banking is filled with former attendees of the Cambridge, Massachusetts, university not just because it was and is one of the world’s top schools for economics.

Arnold Kling comments.