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.