The straw that broke the camel’s back

Oct 18 JDN 2459141

You’ve probably heard the saying before: “It was the straw that broke the camel’s back.” Something has been building up for a long time, with no apparent effect; then suddenly it crosses some kind of threshold and the effect becomes enormous.

Some real-world systems do behave like this: Avalanches, for instance. There is a very sharp critical threshold at which snow suddenly becomes unstable and triggers an avalanche.

This is how weight works in many video games, and it seems ridiculous: In Skyrim, for instance, one 1-pound cheese wheel can mean the difference between being able to function normally and being unable to move. Fear not, however: You can simply eat that cheese wheel and then be on your way.

But most real-world systems aren’t like this. In particular, camels are not. Yes, zero pieces of straw will not break a camel’s back, and some quantity of straw will. No, there is not a well-defined threshold at which adding just one piece of straw will kill the camel. This is one of those times where formal mathematical modeling can help us to see things that we otherwise couldn’t.

If this seems too frivolous, consider that this model need not be about camels: It could be about the weight a bridge can hold, or the amount of pollution a region can sustain, or the amount of psychological stress a person can bear. I think applying it to psychological stress is particularly appropriate at the moment: COVID-19 has suddenly thrust us all above our usual level of stress, and it’s important to understand where our limits lie.

A really strict formal model useful for engineering purposes would be a stress-strain curve, showing the relationship between stress (the amount of force applied) and strain (the amount of deformation of the object). But for this purpose there are basically two regimes to consider:

Below some weight y (the yield strength)the camel’s back will compress under the weight, but once the weight is removed it will return to normal. A healthy camel can carry up to y in straw essentially indefinitely.

Above that point, additional weight will begin to strain the camel’s back. But this damage will not all occur at once; a larger amount of weight for a shorter time will have the same effect as a smaller amount of weight for a longer time.

The total strain on the camel will thus look something like this, for exposure time t: (w-y)t

There is a total amount of strain that the camel can take without breaking its back. This has units of momentum, so I’m going to use p.

What is the amount of straw that breaks the camel’s back? Well, that depends on how long it is there!

w = p/t + y

This implies that even an arbitrarily large weight is survivable, if experienced for a sufficiently small amount of time. This may seem counter-intuitive, but it’s actually quite realistic: I’m not aware of any tests on camels, but human beings have been able to survive impacts of 40 g for a few milliseconds.

If you are hoping to carry a certain load of straw by camel over a certain distance, and need to know how many camels to use (or how many trips to take), you would figure out how long it takes to cover that distance, then use that as your time parameter to figure out the maximum weight a camel could carry for that long.

So what would happen if you actually added one piece of straw at a time to a camel’s back? That depends on how fast you add them and how long you leave them there!

Sheepskin effect doesn’t prove much

Sep 20 JDN 2459113

The sheepskin effect is the observation that the increase in income from graduating from college after four years, relative going through college for three years, is much higher than the increase in income from simply going through college for three years instead of two.

It has been suggested that this provides strong evidence that education is primarily due to signaling, and doesn’t provide any actual value. In this post I’m going to show why this view is mistaken. The sheepskin effect in fact tells us very little about the true value of college. (Noah Smith actually made a pretty decent argument that it provides evidence against signaling!)

To see this, consider two very simple models.

In both models, we’ll assume that markets are competitive but productivity is not directly observable, so employers sort you based on your education level and then pay a wage equal to the average productivity of people at your education level, compensated for the cost of getting that education.

Model 1:

In this model, people all start with the same productivity, and are randomly assigned by their life circumstances to go to either 0, 1, 2, 3, or 4 years of college. College itself has no long-term cost.

The first year of college you learn a lot, the next couple of years you don’t learn much because you’re trying to find your way, and then in the last year of college you learn a lot of specialized skills that directly increase your productivity.

So this is your productivity after x years of college:

Years of collegeProductivity
010
117
222
325
431

We assumed that you’d get paid your productivity, so these are also your wages.

The increase in income each year goes from +7, to +5, to +3, then jumps up to +6. So if you compare the 4-year-minus-3-year gap (+6) with the 3-year-minus-2-year gap (+3), you get a sheepskin effect.

Model 2:

In this model, college is useless and provides no actual benefits. People vary in their intrinsic productivity, which is also directly correlated with the difficulty of making it through college.

In particular, there are five types of people:

TypeProductivityCost per year of college
0108
1116
2144
3193
4310

The wages for different levels of college education are as follows:

Years of collegeWage
010
117
222
325
431

Notice that these are exactly the same wages as in scenario 1. This is of course entirely intentional. In a moment I’ll show why this is a Nash equilibrium.

Consider the choice of how many years of college to attend. You know your type, so you know the cost of college to you. You want to maximize your net benefit, which is the wage you’ll get minus the total cost of going to college.

Let’s assume that if a given year of college isn’t worth it, you won’t try to continue past it and see if more would be.

For a type-0 person, they could get 10 by not going to college at all, or 17-(1)(8) = 9 by going for 1 year, so they stop.

For a type-1 person, they could get 10 by not going to college at all, or 17-(1)(6) = 11 by going for 1 year, or 22-(2)(6) = 10 by going for 2 years, so they stop.

Filling out all the possibilities yields this table:

Years \ Type01234
01010101010
1911131417
2
10141622
3

131925
4


1930

I’d actually like to point out that it was much harder to find numbers that allowed me to make the sheepskin effect work in the second model, where education was all signaling. In the model where education provides genuine benefit, all I need to do is posit that the last year of college is particularly valuable (perhaps because high-level specialized courses are more beneficial to productivity). I could pretty much vary that parameter however I wanted, and get whatever magnitude of sheepskin effect I chose.

For the signaling model, I had to carefully calibrate the parameters so that the costs and benefits lined up just right to make sure that each type chose exactly the amount of college I wanted them to choose while still getting the desired sheepskin effect. It took me about two hours of very frustrating fiddling just to get numbers that worked. And that’s with the assumption that someone who finds 2 years of college not worth it won’t consider trying for 4 years of college (which, given the numbers above, they actually might want to), as well as the assumption that when type-3 individuals are indifferent between staying and dropping out they drop out.

And yet the sheepskin effect is supposed to be evidence that the world works like the signaling model?

I’m sure a more sophisticated model could make the signaling explanation a little more robust. The biggest limitation of these models is that once you observe someone’s education level, you immediately know their true productivity, whether it came from college or not. Realistically we should be allowing for unobserved variation that can’t be sorted out by years of college.

Maybe it seems implausible that the last year of college is actually more beneficial to your productivity than the previous years. This is probably the intuition behind the idea that sheepskin effects are evidence of signaling rather than genuine learning.

So how about this model?

Model 3:

As in the second model, there are four types of people, types 0, 1, 2, 3, and 4. They all start with the same level of productivity, and they have the same cost of going to college; but they get different benefits from going to college.

The problem is, people don’t start out knowing what type they are. Nor can they observe their productivity directly. All they can do is observe their experience of going to college and then try to figure out what type they must be.

Type 0s don’t benefit from college at all, and they know they are type 0; so they don’t go to college.

Type 1s benefit a tiny amount from college (+1 productivity per year), but don’t realize they are type 1s until after one year of college.

Type 2s benefit a little from college (+2 productivity per year), but don’t realize they are type 2s until after two years of college.

Type 3s benefit a moderate amount from college (+3 productivity per year), but don’t realize they are type 3s until after three years of college.

Type 4s benefit a great deal from college (+5 productivity per year), but don’t realize they are type 4s until after three years of college.

What then will happen? Type 0s will not go to college. Type 1s will go one year and then drop out. Type 2s will go two years and then drop out. Type 3s will go three years and then drop out. And type 4s will actually graduate.

That results in the following before-and-after productivity:

TypeProductivity before collegeYears of collegeProductivity after college
010010
110111
210214
310319
410430

If each person is paid a wage equal to their productivity, there will be a huge sheepskin effect; wages only go up +1 for 1 year, +3 for 2 years, +5 for 3 years, but then they jump up to +11 for graduation. It appears that the benefit of that last year of college is more than the other three combined. But in fact it’s not; for any given individual, the benefits of college are the same each year. It’s just that college is more beneficial to the people who decided to stay longer.

And I could of course change that assumption too, making the early years more beneficial, or varying the distribution of types, or adding more uncertainty—and so on. But it’s really not hard at all to make a model where college is beneficial and you observe a large sheepskin effect.

In reality, I am confident that some of the observed benefit of college is due to sorting—not the same thing as signaling—rather than the direct benefits of education. The earnings advantage of going to a top-tier school may be as much about the selection of students as they are the actual quality of the education, since once you control for measures of student ability like GPA and test scores those benefits drop dramatically.

Moreover, I agree that it’s worth looking at this: Insofar as college is about sorting or signaling, it’s wasteful from a societal perspective, and we should be trying to find more efficient sorting mechanisms.

But I highly doubt that all the benefits of college are due to sorting or signaling; there definitely are a lot of important things that people learn in college, not just conventional academic knowledge like how to do calculus, but also broader skills like how to manage time, how to work in groups, and how to present ideas to others. Colleges also cultivate friendships and provide opportunities for networking and exposure to a diverse community. Judging by voting patterns, I’m going to go out on a limb and say that college also makes you a better citizen, which would be well worth it by itself.

The truth is, we don’t know exactly why college is beneficial. We certainly know that it is beneficial: Unemployment rates and median earnings are directly sorted by education level. Yes, even PhDs in philosophy and sociology have lower unemployment and higher incomes (on average) than the general population. (And of course PhDs in economics do better still.)

What good are macroeconomic models? How could they be better?

Dec 11, JDN 2457734

One thing that I don’t think most people know, but which immediately obvious to any student of economics at the college level or above, is that there is a veritable cornucopia of different macroeconomic models. There are growth models (the Solow model, the Harrod-Domar model, the Ramsey model), monetary policy models (IS-LM, aggregate demand-aggregate supply), trade models (the Mundell-Fleming model, the Heckscher-Ohlin model), large-scale computational models (dynamic stochastic general equilibrium, agent-based computational economics), and I could go on.

This immediately raises the question: What are all these models for? What good are they?

A cynical view might be that they aren’t useful at all, that this is all false mathematical precision which makes economics persuasive without making it accurate or useful. And with such a proliferation of models and contradictory conclusions, I can see why such a view would be tempting.

But many of these models are useful, at least in certain circumstances. They aren’t completely arbitrary. Indeed, one of the litmus tests of the last decade has been how well the models held up against the events of the Great Recession and following Second Depression. The Keynesian and cognitive/behavioral models did rather well, albeit with significant gaps and flaws. The Monetarist, Real Business Cycle, and most other neoclassical models failed miserably, as did Austrian and Marxist notions so fluid and ill-defined that I’m not sure they deserve to even be called “models”. So there is at least some empirical basis for deciding what assumptions we should be willing to use in our models. Yet even if we restrict ourselves to Keynesian and cognitive/behavioral models, there are still a great many to choose from, which often yield inconsistent results.

So let’s compare with a science that is uncontroversially successful: Physics. How do mathematical models in physics compare with mathematical models in economics?

Well, there are still a lot of models, first of all. There’s the Bohr model, the Schrodinger equation, the Dirac equation, Newtonian mechanics, Lagrangian mechanics, Bohmian mechanics, Maxwell’s equations, Faraday’s law, Coulomb’s law, the Einstein field equations, the Minkowsky metric, the Schwarzschild metric, the Rindler metric, Feynman-Wheeler theory, the Navier-Stokes equations, and so on. So a cornucopia of models is not inherently a bad thing.

Yet, there is something about physics models that makes them more reliable than economics models.

Partly it is that the systems physicists study are literally two dozen orders of magnitude or more smaller and simpler than the systems economists study. Their task is inherently easier than ours.

But it’s not just that; their models aren’t just simpler—actually they often aren’t. The Navier-Stokes equations are a lot more complicated than the Solow model. They’re also clearly a lot more accurate.

The feature that models in physics seem to have that models in economics do not is something we might call nesting, or maybe consistency. Models in physics don’t come out of nowhere; you can’t just make up your own new model based on whatever assumptions you like and then start using it—which you very much can do in economics. Models in physics are required to fit consistently with one another, and usually inside one another, in the following sense:

The Dirac equation strictly generalizes the Schrodinger equation, which strictly generalizes the Bohr model. Bohmian mechanics is consistent with quantum mechanics, which strictly generalizes Lagrangian mechanics, which generalizes Newtonian mechanics. The Einstein field equations are consistent with Maxwell’s equations and strictly generalize the Minkowsky, Schwarzschild, and Rindler metrics. Maxwell’s equations strictly generalize Faraday’s law and Coulomb’s law.
In other words, there are a small number of canonical models—the Dirac equation, Maxwell’s equations and the Einstein field equation, essentially—inside which all other models are nested. The simpler models like Coulomb’s law and Newtonian mechanics are not contradictory with these canonical models; they are contained within them, subject to certain constraints (such as macroscopic systems far below the speed of light).

This is something I wish more people understood (I blame Kuhn for confusing everyone about what paradigm shifts really entail); Einstein did not overturn Newton’s laws, he extended them to domains where they previously had failed to apply.

This is why it is sensible to say that certain theories in physics are true; they are the canonical models that underlie all known phenomena. Other models can be useful, but not because we are relativists about truth or anything like that; Newtonian physics is a very good approximation of the Einstein field equations at the scale of many phenomena we care about, and is also much more mathematically tractable. If we ever find ourselves in situations where Newton’s equations no longer apply—near a black hole, traveling near the speed of light—then we know we can fall back on the more complex canonical model; but when the simpler model works, there’s no reason not to use it.

There are still very serious gaps in the knowledge of physics; in particular, there is a fundamental gulf between quantum mechanics and the Einstein field equations that has been unresolved for decades. A solution to this “quantum gravity problem” would be essentially a guaranteed Nobel Prize. So even a canonical model can be flawed, and can be extended or improved upon; the result is then a new canonical model which we now regard as our best approximation to truth.

Yet the contrast with economics is still quite clear. We don’t have one or two or even ten canonical models to refer back to. We can’t say that the Solow model is an approximation of some greater canonical model that works for these purposes—because we don’t have that greater canonical model. We can’t say that agent-based computational economics is approximately right, because we have nothing to approximate it to.

I went into economics thinking that neoclassical economics needed a new paradigm. I have now realized something much more alarming: Neoclassical economics doesn’t really have a paradigm. Or if it does, it’s a very informal paradigm, one that is expressed by the arbitrary judgments of journal editors, not one that can be written down as a series of equations. We assume perfect rationality, except when we don’t. We assume constant returns to scale, except when that doesn’t work. We assume perfect competition, except when that doesn’t get the results we wanted. The agents in our models are infinite identical psychopaths, and they are exactly as rational as needed for the conclusion I want.

This is quite likely why there is so much disagreement within economics. When you can permute the parameters however you like with no regard to a canonical model, you can more or less draw whatever conclusion you want, especially if you aren’t tightly bound to empirical evidence. I know a great many economists who are sure that raising minimum wage results in large disemployment effects, because the models they believe in say that it must, even though the empirical evidence has been quite clear that these effects are small if they are present at all. If we had a canonical model of employment that we could calibrate to the empirical evidence, that couldn’t happen anymore; there would be a coefficient I could point to that would refute their argument. But when every new paper comes with a new model, there’s no way to do that; one set of assumptions is as good as another.

Indeed, as I mentioned in an earlier post, a remarkable number of economists seem to embrace this relativism. “There is no true model.” they say; “We do what is useful.” Recently I encountered a book by the eminent economist Deirdre McCloskey which, though I confess I haven’t read it in its entirety, appears to be trying to argue that economics is just a meaningless language game that doesn’t have or need to have any connection with actual reality. (If any of you have read it and think I’m misunderstanding it, please explain. As it is I haven’t bought it for a reason any economist should respect: I am disinclined to incentivize such writing.)

Creating such a canonical model would no doubt be extremely difficult. Indeed, it is a task that would require the combined efforts of hundreds of researchers and could take generations to achieve. The true equations that underlie the economy could be totally intractable even for our best computers. But quantum mechanics wasn’t built in a day, either. The key challenge here lies in convincing economists that this is something worth doing—that if we really want to be taken seriously as scientists we need to start acting like them. Scientists believe in truth, and they are trying to find it out. While not immune to tribalism or ideology or other human limitations, they resist them as fiercely as possible, always turning back to the evidence above all else. And in their combined strivings, they attempt to build a grand edifice, a universal theory to stand the test of time—a canonical model.