Student debt is worse than a bubble

I've often thought about higher education today as a kind of economic bubble, though I've always wondered if that's quite right. It seems to have the traits of a bubble: It seems overpriced, herd behavior and social conformity appear operative, and there's good data suggesting it doesn't do what it claims to do. But "bubble" is a fancy word easily deployed to describe anything that looks or feels vaguely unsustainable, so its informal usage as a term probably tends to overstate the economic significance of many phenomena. Student debt kind of feels like a bubble, but it's probably not quite that bad, right?

Well, I've recently learned that "bubble" is not quite the right way to understand the higher education problem. It understates the severity of the problem.

One problem for applying the bubble term to higher education is that you can't sell off student debt. The boom-and-bust pattern of the bubble phenomenon, requires that at the unsustainable breaking point of the boom, people realize the thing is overvalued and ditch it. Once this starts, others start selling off as they fear a continued collapse of the price, and they will sell at a loss if necessary (in the bust period, taking a loss now is at least better than taking a loss tomorrow).

I got this from Marshall Steinbaum of the Roosevelt Institute, who recently made this point in a Vice interview. Vice paraphrasing Steinbaum:

"…student debt couldn't explode in a traditional "bubble" because it tends to be unsecured… That piece of paper might become worth way less than you paid for it, but ultimately you're stuck with it."

So it's not that "bubble" is a naïve and fear-mongering metaphor for the problem of higher education: It's an overly generous and insufficiently alarmist metaphor for the problem of higher education.

The second reason higher education is not a bubble is that there is no path to relief for student debt. You can't default or go bankrupt from student debt, so even if nobody can pay their student debts, there is no mechanism for this to translate into a market crisis. The basic problems can be every bit as severe as a bubble, but we may never learn this like we ultimately do with bubbles because, ultimately, everything is held together by the US government. Though the US government is not holding this all together by absorbing losses, it's holding it all together by law enforcement on college graduates.

"You're not going to see the entire market bottom out, I don't think, like we did in the foreclosure crisis, because you won't see everyone default and then the bank is left holding the bag… Here, everyone defaults and the government just takes their wages, tax refunds, or Social Security benefits."

Persis Yu, staff attorney at the National Consumer Law Center

There's a sad irony here. People want the US government to provide financial aid for higher education in order to spread opportunity among young people, but the US government's involvement is precisely one of the reasons why the student debt catastrophe is an inescapable nightmare for so many young people, rather than just a bubble that might have popped long ago.

The non-linear effect of ability on earnings in the computer age

A reader/watcher/listener has brought to my attention another paper, which shows that, for college-educated individuals, earnings are a non-linear function of cognitive ability or g — at least in the National Longitudinal Survey of Youth from 1979-1994. The paper is a 2003 article by Justin Tobias in the Oxford Bulletin of Economics and Statistics.

There may be other studies on this question, but a selling point of this article is that it tries to use the least restrictive assumptions possible. Namely, allowing for non-linearities. In the social sciences, there is a huge bias toward finding linear effects, because most of the workhorse models everyone learns in grad school are linear models. Non-linear models are trickier and harder to interpret and so they're just used much less, even in contexts where non-linearities are very plausible.

A common motif in "accelerationist" social/political theories is the exponential curve. Many of us have priors suggesting that, at least for most of the non-trivial tendenices characterizing modern polities, there are likely to be non-linear processes at work. If the contemporary social scientist using workhorse regression models is biased toward finding linear effects, accelerationists tend to go looking for non-linear processes at the individual, group, nation, or global level. So for those of us who think the accelerationist frame is the one best fit to parsing the politics of modernity, studies allowing for non-linearity can be especially revealing.

The first main finding of Tobias is visually summarized in the figure below. Tobias has more complicated arguments about the relationship between ability, education, and earnings, but we'll ignore those here. Considering college-educated individuals only, the graph below plots on the y-axis the percentage change in wages associated with a one-standard-deviation increase in ability, across a range of abilities. Note that whereas many graphs will show you how some change in X is associated with some change in Y, this plot is different: It shows the marginal effect of X on Y, but for different values of X.

Tobias 2003, pp. 13.

The implication of the above graph is pretty clear. It just means that the earnings gain from any unit increase in g is greater at higher levels of g. An easy way to summarize this is to say that the effect of X on Y is exponential or multiplicative. Note also there's nothing obvious about this effect; contrast this graph to the diminishing marginal utility of money. Gaining $1000 when you're a millionaire has less of an effect on your happiness than if you're at the median wealth level. But when it comes to earnings, gaining a little bit of extra ability when you're already able is worth even more than if you were starting at a low level of ability.

The paper has a lot of nuances, which I'm blithely steamrolling. My last paragraph is only true for the college educated, and there are a few other interesting wrinkles. But this is a blog, and so I mostly collect what is of interest to me personally. Thus I'll skip to the end of the paper, where Tobias estimates separate models for each year. The graph below shows the size of the wage gap between the college-educated and the non-college-educated, for three different ability types, in each year. The solid line is one standard deviation above the mean ability, the solid line with dots is mean ability, and the dotted line is one standard deviation below the mean ability.

Tobias 2003, pp. 23

An obvious implication is that the wage gap increases over this period, more or less for each ability level. But what's interesting is that the slope looks a bit steeper, and is less volatile, for high-ability than for average and low-ability. There is a lot of temporal volatility for the class of low-ability individuals. In fact, for low-ability individuals there is not even a consistent wage premium enjoyed by the college-educated until 1990.

Anyway, file under runaway intelligence takeoff...

Study finds the relationship between genes and earnings increased after 1980

Someone sent me a recent NBER working paper by Nicholas W. Papageorge and Kevin Thom on polygenic scores and educational attainment/earnings. Most pertinent to my theoretical interests is that the link between genes and income appears to increase over recent decades.

In my lectures on the politics of media (really about the politics of technology more generally), I dedicate a session to the topic of skill-biased technical change (SBTC). While the econometrics and specific interpretations are debated, there is a literature in Economics that suggests certain technological innovations (i.e. computing) increase the earnings of the highly skilled relative to the less skilled. I would sometimes wonder to what degree "skills," which sound like primarily acquired things, in fact reflect heritable traits. Or if one could separate these out...

Papageorge and Thom provide one of the first efforts to study this question explicitly. "This is the first study to estimate the returns to genetic factors associated with education using micro genetic data and disaggregated measures of earnings and job tasks across cohorts."

Here is their summary of the genetic effect, conditional on time period:

The returns to these genetic endowments appear to rise over time, coinciding with the rise in income inequality after 1980. Accounting for degree and years of schooling, a one standard deviation increase in the score is associated with a 4.5 percent increase in earnings after 1980. These results are consistent with recent literature on income inequality
showing not only an increase in the college premium, but also a rise in the residual wage variance within educational groups (Lemieux, 2006). We also find a positive association between the score and the kinds of non-routine job tasks that benefited from computerization and the development of more advanced information technologies (Autor, Levy, and Murnane, 2003). This provides suggestive evidence that the endowments linked to more educational attainment may allow individuals to either better adapt to new technologies, or specialize in
tasks that more strongly complement these new technologies.

Basically, they observe what you would expect to observe if the computerization that begins around 1980 allowed the escape and takeoff of "non-routine analytic" power or abstract intelligence by those most genetically blessed with it. Implicitly, individuals less genetically blessed with "non-routine analytic" powers begin to be left behind around 1980.

Their findings cannot explain the entire postwar dynamic of increasing inequality and relative stagnation of the lower classes, however, because the flatlining of median wages begins around 1973 if I recall correctly. The study seems somewhat coy about naming or even labeling the polygenic score; but my non-expert intuition is that it would have to be something quite akin to what is called the "g-factor" or general intelligence, right?

One limitation of the study is that they use a dummy variable for the period after 1980. I would be curious to see what happens if one re-runs their models with a continuous variable for year. My intuition is that individual-level economic outcomes are more skill-biased/g-loaded today than in the 1980s, but I'm not yet up on any studies this precise on that question in particular.

Left Singularity

…modern political history has a characteristic shape, which combines a duration of escalating ‘progress’ with a terminal, quasi-punctual interruption, or catastrophe – a restoration or ‘reboot’. Like mould in a Petri dish, progressive polities ‘develop’ explosively until all available resources have been consumed, but unlike slime colonies they exhibit a dynamism that is further exaggerated (from the exponential to the hyperbolic) by the fact that resource depletion accelerates the development trend.

Economic decay erodes productive potential and increases dependency, binding populations ever more desperately to the promise of political remedy. The progressive slope steepens towards the precipice of supreme radicality, or total absorption into the state…

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