Most trade barriers are not tariffs

Jul 8 JDN 2458309

When we talk about “protectionism” or “trade barriers”, what usually comes to mind is tariffs: taxes imposed on imports or exports. But especially now that international trade organizations have successfully reduced tariffs around the world, most trade barriers are not of this form at all.

Especially in highly-developed countries, but really almost everywhere, the most common trade barriers are what is simply but inelegantly called non-tariff barriers to trade: this includes licenses, quotas, subsidies, bailout guarantees, labeling requirements, and even some environmental regulations.

Non-tariff barriers are much more complicated to deal with, for at least three reasons.

First, with the exception of quotas and subsidies, non-tariff barriers are not easily quantifiable. We can easily put a number on the value of a tariff (though its impact is somewhat subtler than that), but this is not so easy for the effect of a bailout guarantee or a labeling requirement.

Second, non-tariff barriers are often much harder to detect. It’s obvious enough that imposing a tax on imported steel will reduce our imports of steel; but it requires a deeper understanding of the trade system to understand why bailing out domestic banks would distort financial flows, interest rates and exchange rates (even though the impact of this may actually be larger—the effect on global trade of US bank bailouts was between $35 billion and $110 billion).

Third, some trade barriers are either justifiable or simply inevitable. Simply having customs screening at the border is a non-tariff barrier, but it is widely regarded as a justifiable security measure (and I agree, by the way, even though I am generally in favor of much more open borders). Requiring strict labor and environmental standards on the production of products both domestic and imported is highly beneficial, but also imposes a trade barrier. In a broader sense, differences in language and culture could even be regarded as trade barriers (they certainly increase the real cost of trade), but it’s not clear that we could eliminate such things even if we wanted to.

This requires us to look very closely at almost every major government policy, to see how it might be distorting world trade. Some policies won’t meaningfully distort trade at all; these are not trade barriers. Others will distort trade, but are beneficial enough in other ways that they are still worth it; these are justifiable trade barriers. Still others will distort trade so much that they cannot be justified despite their other benefits. Finally, some policies will be put in place more or less explicitly to distort trade, usually in the form of protectionism to prop up domestic industries.

Protectionist policies are of course the first things to get rid of. Honestly, it baffles me that people even want to impose them in the first place. For some reason they think of exports as the benefit and imports as the cost, when it’s really the other way around; when we impose protectionism, we go out of our way to make it harder to get cars and iPhones so that we can stop other countries from taking our green paper. This seems to be tied to the fact that people think of jobs as something desirable, when really it’s wealth that’s desirable, and jobs are just one way of getting wealth—in some sense the most expensive way. Our macroeconomic policy obsesses over inflation, which is almost literally meaningless (as long as it is not too unpredictable, really nothing would change if inflation were raised from 2% to 4% or even 10%) and unemployment, which is at best an imperfect indicator of what we really should care about, namely the welfare of our people. A world of full employment with poverty wages is much worse than a world of high unemployment where a basic income provides for everyone’s needs. It is true that in our current system, unemployment is closely tied to a lot of very bad outcomes—but I maintain that this is largely because unemployment entails losing your income and your healthcare.

Some regulations that appear benign may actually be harmful because of their effects on trade. Yet I should also point out that it’s possible to go too far the other direction, and start tearing down all regulations in the name of reducing trade barriers. We particularly seem to do this in the financial industry, where “deregulation” seems to be on everyone’s lips until it causes a crisis, then we impose some regulations that fix the worst problems, things look good for awhile—and then we go back around and everyone starts talking about “deregulation” again. Meanwhile, the same people who talk about “freedom” as an excuse for removing financial safeguards are the ones who lock up children at the border. I think this is something that needs to be reframed: Which regulations are you removing? Just what, exactly, are you making legal that wasn’t before? Legalizing murder would be “deregulation”.

Trade policy, therefore, is a very delicate balance, between removing distortions and protecting legitimate public interests, between the needs of your own country and the world as a whole. This is why we need this whole apparatus of international trade institutions; it’s not a simple matter.

But I will say this: It would probably help if people educated themselves a bit more about how trade actually works before voting in politicians who promise to “save their jobs” from foreign competition.

“DSGE or GTFO”: Macroeconomics took a wrong turn somewhere

Dec 31, JDN 2458119

The state of macro is good,” wrote Oliver Blanchard—in August 2008. This is rather like the turkey who is so pleased with how the farmer has been feeding him lately, the day before Thanksgiving.

It’s not easy to say exactly where macroeconomics went wrong, but I think Paul Romer is right when he makes the analogy between DSGE (dynamic stochastic general equilbrium) models and string theory. They are mathematically complex and difficult to understand, and people can make their careers by being the only ones who grasp them; therefore they must be right! Nevermind if they have no empirical support whatsoever.

To be fair, DSGE models are at least a little better than string theory; they can at least be fit to real-world data, which is better than string theory can say. But being fit to data and actually predicting data are fundamentally different things, and DSGE models typically forecast no better than far simpler models without their bold assumptions. You don’t need to assume all this stuff about a “representative agent” maximizing a well-defined utility function, or an Euler equation (that doesn’t even fit the data), or this ever-proliferating list of “random shocks” that end up taking up all the degrees of freedom your model was supposed to explain. Just regressing the variables on a few years of previous values of each other (a “vector autoregression” or VAR) generally gives you an equally-good forecast. The fact that these models can be made to fit the data well if you add enough degrees of freedom doesn’t actually make them good models. As Von Neumann warned us, with enough free parameters, you can fit an elephant.

But really what bothers me is not the DSGE but the GTFO (“get the [expletive] out”); it’s not that DSGE models are used, but that it’s almost impossible to get published as a macroeconomic theorist using anything else. Defenders of DSGE typically don’t even argue anymore that it is good; they argue that there are no credible alternatives. They characterize their opponents as “dilettantes” who aren’t opposing DSGE because we disagree with it; no, it must be because we don’t understand it. (Also, regarding that post, I’d just like to note that I now officially satisfy the Athreya Axiom of Absolute Arrogance: I have passed my qualifying exams in a top-50 economics PhD program. Yet my enmity toward DSGE has, if anything, only intensified.)

Of course, that argument only makes sense if you haven’t been actively suppressing all attempts to formulate an alternative, which is precisely what DSGE macroeconomists have been doing for the last two or three decades. And yet despite this suppression, there are alternatives emerging, particularly from the empirical side. There are now empirical approaches to macroeconomics that don’t use DSGE models. Regression discontinuity methods and other “natural experiment” designs—not to mention actual experiments—are quickly rising in popularity as economists realize that these methods allow us to actually empirically test our models instead of just adding more and more mathematical complexity to them.

But there still seems to be a lingering attitude that there is no other way to do macro theory. This is very frustrating for me personally, because deep down I think what I would like to do as a career is macro theory: By temperament I have always viewed the world through a very abstract, theoretical lens, and the issues I care most about—particularly inequality, development, and unemployment—are all fundamentally “macro” issues. I left physics when I realized I would be expected to do string theory. I don’t want to leave economics now that I’m expected to do DSGE. But I also definitely don’t want to do DSGE.

Fortunately with economics I have a backup plan: I can always be an “applied micreconomist” (rather the opposite of a theoretical macroeconomist I suppose), directly attached to the data in the form of empirical analyses or even direct, randomized controlled experiments. And there certainly is plenty of work to be done along the lines of Akerlof and Roth and Shiller and Kahneman and Thaler in cognitive and behavioral economics, which is also generally considered applied micro. I was never going to be an experimental physicist, but I can be an experimental economist. And I do get to use at least some theory: In particular, there’s an awful lot of game theory in experimental economics these days. Some of the most exciting stuff is actually in showing how human beings don’t behave the way classical game theory predicts (particularly in the Ultimatum Game and the Prisoner’s Dilemma), and trying to extend game theory into something that would fit our actual behavior. Cognitive science suggests that the result is going to end up looking quite different from game theory as we know it, and with my cognitive science background I may be particularly well-positioned to lead that charge.

Still, I don’t think I’ll be entirely satisfied if I can’t somehow bring my career back around to macroeconomic issues, and particularly the great elephant in the room of all economics, which is inequality. Underlying everything from Marxism to Trumpism, from the surging rents in Silicon Valley and the crushing poverty of Burkina Faso, to the Great Recession itself, is inequality. It is, in my view, the central question of economics: Who gets what, and why?

That is a fundamentally macro question, but you can’t even talk about that issue in DSGE as we know it; a “representative agent” inherently smooths over all inequality in the economy as though total GDP were all that mattered. A fundamentally new approach to macroeconomics is needed. Hopefully I can be part of that, but from my current position I don’t feel much empowered to fight this status quo. Maybe I need to spend at least a few more years doing something else, making a name for myself, and then I’ll be able to come back to this fight with a stronger position.

In the meantime, I guess there’s plenty of work to be done on cognitive biases and deviations from game theory.

What we lose by aggregating

Jun 25, JDN 2457930

One of the central premises of current neoclassical macroeconomics is the representative agent: Rather than trying to keep track of all the thousands of firms, millions of people, and billions of goods and in a national economy, we aggregate everything up into a single worker/consumer and a single firm producing and consuming a single commodity.

This sometimes goes under the baffling misnomer of microfoundations, which would seem to suggest that it carries detailed information about the microeconomic behavior underlying it; in fact what this means is that the large-scale behavior is determined by some sort of (perfectly) rational optimization process as if there were just one person running the entire economy optimally.

First of all, let me say that some degree of aggregation is obviously necessary. Literally keeping track of every single transaction by every single person in an entire economy would require absurd amounts of data and calculation. We might have enough computing power to theoretically try this nowadays, but then again we might not—and in any case such a model would very rapidly lose sight of the forest for the trees.

But it is also clearly possible to aggregate too much, and most economists don’t seem to appreciate this. They cite a couple of famous theorems (like the Gorman Aggregation Theorem) involving perfectly-competitive firms and perfectly-rational identical consumers that offer a thin veneer of justification for aggregating everything into one, and then go on with their work as if this meant everything were fine.

What’s wrong with such an approach?

Well, first of all, a representative agent model can’t talk about inequality at all. It’s not even that a representative agent model says inequality is good, or not a problem; it lacks the capacity to even formulate the concept. Trying to talk about income or wealth inequality in a representative agent model would be like trying to decide whether your left hand is richer than your right hand.

It’s also nearly impossible to talk about poverty in a representative agent model; the best you can do is talk about a country’s overall level of development, and assume (not without reason) that a country with a per-capita GDP of $1,000 probably has a lot more poverty than a country with a per-capita GDP of $50,000. But two countries with the same per-capita GDP can have very different poverty rates—and indeed, the cynic in me wonders if the reason we’re reluctant to use inequality-adjusted measures of development is precisely that many American economists fear where this might put the US in the rankings. The Human Development Index was a step in the right direction because it includes things other than money (and as a result Saudi Arabia looks much worse and Cuba much better), but it still aggregates and averages everything, so as long as your rich people are doing well enough they can compensate for how badly your poor people are doing.

Nor can you talk about oligopoly in a representative agent model, as there is always only one firm, which for some reason chooses to act as if it were facing competition instead of rationally behaving as a monopoly. (This is not quite as nonsensical as it sounds, as the aggregation actually does kind of work if there truly are so many firms that they are all forced down to zero profit by fierce competition—but then again, what market is actually like that?) There is no market share, no market power; all are at the mercy of the One True Price.

You can still talk about externalities, sort of; but in order to do so you have to set up this weird doublethink phenomenon where the representative consumer keeps polluting their backyard and then can’t figure out why their backyard is so darn polluted. (I suppose humans do seem to behave like that sometimes; but wait, I thought you believed people were rational?) I think this probably confuses many an undergrad, in fact; the models we teach them about externalities generally use this baffling assumption that people consider one set of costs when making their decisions and then bear a different set of costs from the outcome. If you can conceptualize the idea that we’re aggregating across people and thinking “as if” there were a representative agent, you can ultimately make sense of this; but I think a lot of students get really confused by it.

Indeed, what can you talk about with a representative agent model? Economic growth and business cycles. That’s… about it. These are not minor issues, of course; indeed, as Robert Lucas famously said:

The consequences for human welfare involved in questions like these [on economic growth] are simply staggering: once one starts to think about them, it is hard to think about anything else.

I certainly do think that studying economic growth and business cycles should be among the top priorities of macroeconomics. But then, I also think that poverty and inequality should be among the top priorities, and they haven’t been—perhaps because the obsession with representative agent models make that basically impossible.

I want to be constructive here; I appreciate that aggregating makes things much easier. So what could we do to include some heterogeneity without too much cost in complexity?

Here’s one: How about we have p firms, making q types of goods, sold to n consumers? If you want you can start by setting all these numbers equal to 2; simply going from 1 to 2 has an enormous effect, as it allows you to at least say something about inequality. Getting them as high as 100 or even 1000 still shouldn’t be a problem for computing the model on an ordinary laptop. (There are “econophysicists” who like to use these sorts of agent-based models, but so far very few economists take them seriously. Partly that is justified by their lack of foundational knowledge in economics—the arrogance of physicists taking on a new field is legendary—but partly it is also interdepartmental turf war, as economists don’t like the idea of physicists treading on their sacred ground.) One thing that really baffles me about this is that economists routinely use computers to solve models that can’t be calculated by hand, but it never seems to occur to them that they could have started at the beginning planning to make the model solvable only by computer, and that would spare them from making the sort of heroic assumptions they are accustomed to making—assumptions that only made sense when they were used to make a model solvable that otherwise wouldn’t be.

You could also assign a probability distribution over incomes; that can get messy quickly, but we actually are fortunate that the constant relative risk aversion utility function and the Pareto distribution over incomes seem to fit the data quite well—as the product of those two things is integrable by hand. As long as you can model how your policy affects this distribution without making that integral impossible (which is surprisingly tricky), you can aggregate over utility instead of over income, which is a lot more reasonable as a measure of welfare.

And really I’m only scratching the surface here. There are a vast array of possible new approaches that would allow us to extend macroeconomic models to cover heterogeneity; the real problem is an apparent lack of will in the community to make such an attempt. Most economists still seem very happy with representative agent models, and reluctant to consider anything else—often arguing, in fact, that anything else would make the model less microfounded when plainly the opposite is the case.

 

Games as economic simulations—and education tools

Mar 5, JDN 2457818 [Sun]

Moore’s Law is a truly astonishing phenomenon. Now as we are well into the 21st century (I’ve lived more of my life in the 21st century than the 20th now!) it may finally be slowing down a little bit, but it has had quite a run, and even this could be a temporary slowdown due to economic conditions or the lull before a new paradigm (quantum computing?) matures. Since at least 1975, the computing power of an individual processor has doubled approximately every year and a half; that means it has doubled over 25 times—or in other words that it has increased by a factor of over 30 million. I now have in my pocket a smartphone with several thousand times the processing speed of the guidance computer of the Saturn V that landed on the Moon.

This meteoric increase in computing power has had an enormous impact on the way science is done, including economics. Simple theoretical models that could be solved by hand are now being replaced by enormous simulation models that have to be processed by computers. It is now commonplace to devise models with systems of dozens of nonlinear equations that are literally impossible to solve analytically, and just solve them iteratively with computer software.

But one application of this technology that I believe is currently underutilized is video games.

As a culture, we still have the impression that video games are for children; even games like Dragon Age and Grand Theft Auto that are explicitly for adults (and really quite inappropriate for children!) are viewed as in some sense “childish”—that no serious adult would be involved with such frivolities. The same cultural critics who treat Shakespeare’s vagina jokes as the highest form of art are liable to dismiss the poignant critique of war in Call of Duty: Black Ops or the reflections on cultural diversity in Skyrim as mere puerility.

But video games are an art form with a fundamentally greater potential than any other. Now that graphics are almost photorealistic, there is really nothing you can do in a play or a film that you can’t do in a video game—and there is so, so much more that you can only do in a game.
In what other medium can we witness the spontaneous emergence and costly aftermath of a war? Yet EVE Online has this sort of event every year or so—just today there was a surprise attack involving hundreds of players that destroyed thousands of hours’—and dollars’—worth of starships, something that has more or less become an annual tradition. A few years ago there was a massive three-faction war that destroyed over $300,000 in ships and has now been commemorated as “the Bloodbath of B-R5RB”.
Indeed, the immersion and interactivity of games present an opportunity to do nothing less than experimental macroeconomics. For generations it has been impossible, or at least absurdly unethical, to ever experimentally manipulate an entire macroeconomy. But in a video game like EVE Online or Second Life, we can now do so easily, cheaply, and with little or no long-term harm to the participants—and we can literally control everything in the experiment. Forget the natural resource constraints and currency exchange rates—we can change the laws of physics if we want. (Indeed, EVE‘s whole trade network is built around FTL jump points, and in Second Life it’s a basic part of the interface that everyone can fly like Superman.)

This provides untold potential for economic research. With sufficient funding, we could build a game that would allow us to directly test hypotheses about the most fundamental questions of economics: How do governments emerge and maintain security? How is the rule of law sustained, and when can it be broken? What controls the value of money and the rate of inflation? What is the fundamental cause of unemployment, and how can it be corrected? What influences the rate of technological development? How can we maximize the rate of economic growth? What effect does redistribution of wealth have on employment and output? I envision a future where we can directly simulate these questions with thousands of eager participants, varying the subtlest of parameters and carrying out events over any timescale we like from seconds to centuries.

Nor is the potential of games in economics limited to research; it also has enormous untapped potential in education. I’ve already seen in my classes how tabletop-style games with poker chips can teach a concept better in a few minutes than hours of writing algebra derivations on the board; but custom-built video games could be made that would teach economics far better still, and to a much wider audience. In a well-designed game, people could really feel the effects of free trade or protectionism, not just on themselves as individuals but on entire nations that they control—watch their GDP numbers go down as they scramble to produce in autarky what they could have bought for half the price if not for the tariffs. They could see, in real time, how in the absence of environmental regulations and Pigovian taxes the actions of millions of individuals could despoil our planet for everyone.

Of course, games are fundamentally works of fiction, subject to the Fictional Evidence Fallacy and only as reliable as their authors make them. But so it is with all forms of art. I have no illusions about the fact that we will never get the majority of the population to regularly read peer-reviewed empirical papers. But perhaps if we are clever enough in the games we offer them to play, we can still convey some of the knowledge that those papers contain. We could also update and expand the games as new information comes in. Instead of complaining that our students are spending time playing games on their phones and tablets, we could actually make education into games that are as interesting and entertaining as the ones they would have been playing. We could work with the technology instead of against it. And in a world where more people have access to a smartphone than to a toilet, we could finally bring high-quality education to the underdeveloped world quickly and cheaply.

Rapid growth in computing power has given us a gift of great potential. But soon our capacity will widen even further. Even if Moore’s Law slows down, computing power will continue to increase for awhile yet. Soon enough, virtual reality will finally take off and we’ll have even greater depth of immersion available. The future is bright—if we can avoid this corporatist cyberpunk dystopia we seem to be hurtling toward, of course.

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.

Sometimes people have to lose their jobs. This isn’t a bad thing.

Oct 8, JDN 2457670

Eleizer Yudkowsky (founder of the excellent blog forum Less Wrong) has a term he likes to use to distinguish his economic policy views from either liberal, conservative, or even libertarian: “econoliterate”, meaning the sort of economic policy ideas one comes up with when one actually knows a good deal about economics.

In general I think Yudkowsky overestimates this effect; I’ve known some very knowledgeable economists who disagree quite strongly over economic policy, and often following the conventional political lines of liberal versus conservative: Liberal economists want more progressive taxation and more Keynesian monetary and fiscal policy, while conservative economists want to reduce taxes on capital and remove regulations. Theoretically you can want all these things—as Miles Kimball does—but it’s rare. Conservative economists hate minimum wage, and lean on the theory that says it should be harmful to employment; liberal economists are ambivalent about minimum wage, and lean on the empirical data that shows it has almost no effect on employment. Which is more reliable? The empirical data, obviously—and until more economists start thinking that way, economics is never truly going to be a science as it should be.

But there are a few issues where Yudkowsky’s “econoliterate” concept really does seem to make sense, where there is one view held by most people, and another held by economists, regardless of who is liberal or conservative. One such example is free trade, which almost all economists believe in. A recent poll of prominent economists by the University of Chicago found literally zero who agreed with protectionist tariffs.

Another example is my topic for today: People losing their jobs.

Not unemployment, which both economists and almost everyone else agree is bad; but people losing their jobs. The general consensus among the public seems to be that people losing jobs is always bad, while economists generally consider it a sign of an economy that is run smoothly and efficiently.

To be clear, of course losing your job is bad for you; I don’t mean to imply that if you lose your job you shouldn’t be sad or frustrated or anxious about that, particularly not in our current system. Rather, I mean to say that policy which tries to keep people in their jobs is almost always a bad idea.

I think the problem is that most people don’t quite grasp that losing your job and not having a job are not the same thing. People not having jobs who want to have jobs—unemployment—is a bad thing. But losing your job doesn’t mean you have to stay unemployed; it could simply mean you get a new job. And indeed, that is what it should mean, if the economy is running properly.

Check out this graph, from FRED:

hires_separations

The red line shows hires—people getting jobs. The blue line shows separations—people losing jobs or leaving jobs. During a recession (the most recent two are shown on this graph), people don’t actually leave their jobs faster than usual; if anything, slightly less. Instead what happens is that hiring rates drop dramatically. When the economy is doing well (as it is right now, more or less), both hires and separations are at very high rates.

Why is this? Well, think about what a job is, really: It’s something that needs done, that no one wants to do for free, so someone pays someone else to do it. Once that thing gets done, what should happen? The job should end. It’s done. The purpose of the job was not to provide for your standard of living; it was to achieve the task at hand. Once it doesn’t need done, why keep doing it?

We tend to lose sight of this, for a couple of reasons. First, we don’t have a basic income, and our social welfare system is very minimal; so a job usually is the only way people have to provide for their standard of living, and they come to think of this as the purpose of the job. Second, many jobs don’t really “get done” in any clear sense; individual tasks are completed, but new ones always arise. After every email sent is another received; after every patient treated is another who falls ill.

But even that is really only true in the short run. In the long run, almost all jobs do actually get done, in the sense that no one has to do them anymore. The job of cleaning up after horses is done (with rare exceptions). The job of manufacturing vacuum tubes for computers is done. Indeed, the job of being a computer—that used to be a profession, young women toiling away with slide rules—is very much done. There are no court jesters anymore, no town criers, and very few artisans (and even then, they’re really more like hobbyists). There are more writers now than ever, and occasional stenographers, but there are no scribes—no one powerful but illiterate pays others just to write things down, because no one powerful is illiterate (and even few who are not powerful, and fewer all the time).

When a job “gets done” in this long-run sense, we usually say that it is obsolete, and again think of this as somehow a bad thing, like we are somehow losing the ability to do something. No, we are gaining the ability to do something better. Jobs don’t become obsolete because we can’t do them anymore; they become obsolete because we don’t need to do them anymore. Instead of computers being a profession that toils with slide rules, they are thinking machines that fit in our pockets; and there are plenty of jobs now for software engineers, web developers, network administrators, hardware designers, and so on as a result.

Soon, there will be no coal miners, and very few oil drillers—or at least I hope so, for the sake of our planet’s climate. There will be far fewer auto workers (robots have already done most of that already), but far more construction workers who install rail lines. There will be more nuclear engineers, more photovoltaic researchers, even more miners and roofers, because we need to mine uranium and install solar panels on rooftops.

Yet even by saying that I am falling into the trap: I am making it sound like the benefit of new technology is that it opens up more new jobs. Typically it does do that, but that isn’t what it’s for. The purpose of technology is to get things done.

Remember my parable of the dishwasher. The goal of our economy is not to make people work; it is to provide people with goods and services. If we could invent a machine today that would do the job of everyone in the world and thereby put us all out of work, most people think that would be terrible—but in fact it would be wonderful.

Or at least it could be, if we did it right. See, the problem right now is that while poor people think that the purpose of a job is to provide for their needs, rich people think that the purpose of poor people is to do jobs. If there are no jobs to be done, why bother with them? At that point, they’re just in the way! (Think I’m exaggerating? Why else would anyone put a work requirement on TANF and SNAP? To do that, you must literally think that poor people do not deserve to eat or have homes if they aren’t, right now, working for an employer. You can couch that in cold economic jargon as “maximizing work incentives”, but that’s what you’re doing—you’re threatening people with starvation if they can’t or won’t find jobs.)

What would happen if we tried to stop people from losing their jobs? Typically, inefficiency. When you aren’t allowed to lay people off when they are no longer doing useful work, we end up in a situation where a large segment of the population is being paid but isn’t doing useful work—and unlike the situation with a basic income, those people would lose their income, at least temporarily, if they quit and tried to do something more useful. There is still considerable uncertainty within the empirical literature on just how much “employment protection” (laws that make it hard to lay people off) actually creates inefficiency and reduces productivity and employment, so it could be that this effect is small—but even so, likewise it does not seem to have the desired effect of reducing unemployment either. It may be like minimum wage, where the effect just isn’t all that large. But it’s probably not saving people from being unemployed; it may simply be shifting the distribution of unemployment so that people with protected jobs are almost never unemployed and people without it are unemployed much more frequently. (This doesn’t have to be based in law, either; while it is made by custom rather than law, it’s quite clear that tenure for university professors makes tenured professors vastly more secure, but at the cost of making employment tenuous and underpaid for adjuncts.)

There are other policies we could make that are better than employment protection, active labor market policies like those in Denmark that would make it easier to find a good job. Yet even then, we’re assuming that everyone needs jobs–and increasingly, that just isn’t true.

So, when we invent a new technology that replaces workers, workers are laid off from their jobs—and that is as it should be. What happens next is what we do wrong, and it’s not even anybody in particular; this is something our whole society does wrong: All those displaced workers get nothing. The extra profit from the more efficient production goes entirely to the shareholders of the corporation—and those shareholders are almost entirely members of the top 0.01%. So the poor get poorer and the rich get richer.

The real problem here is not that people lose their jobs; it’s that capital ownership is distributed so unequally. And boy, is it ever! Here are some graphs I made of the distribution of net wealth in the US, using from the US Census.

Here are the quintiles of the population as a whole:

net_wealth_us

And here are the medians by race:

net_wealth_race

Medians by age:

net_wealth_age

Medians by education:

net_wealth_education

And, perhaps most instructively, here are the quintiles of people who own their homes versus renting (The rent is too damn high!)

net_wealth_rent

All that is just within the US, and already they are ranging from the mean net wealth of the lowest quintile of people under 35 (-$45,000, yes negative—student loans) to the mean net wealth of the highest quintile of people with graduate degrees ($3.8 million). All but the top quintile of renters are poorer than all but the bottom quintile of homeowners. And the median Black or Hispanic person has less than one-tenth the wealth of the median White or Asian person.

If we look worldwide, wealth inequality is even starker. Based on UN University figures, 40% of world wealth is owned by the top 1%; 70% by the top 5%; and 80% by the top 10%. There is less total wealth in the bottom 80% than in the 80-90% decile alone. According to Oxfam, the richest 85 individuals own as much net wealth as the poorest 3.7 billion. They are the 0.000,001%.

If we had an equal distribution of capital ownership, people would be happy when their jobs became obsolete, because it would free them up to do other things (either new jobs, or simply leisure time), while not decreasing their income—because they would be the shareholders receiving those extra profits from higher efficiency. People would be excited to hear about new technologies that might displace their work, especially if those technologies would displace the tedious and difficult parts and leave the creative and fun parts. Losing your job could be the best thing that ever happened to you.

The business cycle would still be a problem; we have good reason not to let recessions happen. But stopping the churn of hiring and firing wouldn’t actually make our society better off; it would keep people in jobs where they don’t belong and prevent us from using our time and labor for its best use.

Perhaps the reason most people don’t even think of this solution is precisely because of the extreme inequality of capital distribution—and the fact that it has more or less always been this way since the dawn of civilization. It doesn’t seem to even occur to most people that capital income is a thing that exists, because they are so far removed from actually having any amount of capital sufficient to generate meaningful income. Perhaps when a robot takes their job, on some level they imagine that the robot is getting paid, when of course it’s the shareholders of the corporations that made the robot and the corporations that are using the robot in place of workers. Or perhaps they imagine that those shareholders actually did so much hard work they deserve to get paid that money for all the hours they spent.

Because pay is for work, isn’t it? The reason you get money is because you’ve earned it by your hard work?

No. This is a lie, told to you by the rich and powerful in order to control you. They know full well that income doesn’t just come from wages—most of their income doesn’t come from wages! Yet this is even built into our language; we say “net worth” and “earnings” rather than “net wealth” and “income”. (Parade magazine has a regular segment called “What People Earn”; it should be called “What People Receive”.) Money is not your just reward for your hard work—at least, not always.

The reason you get money is that this is a useful means of allocating resources in our society. (Remember, money was created by governments for the purpose of facilitating economic transactions. It is not something that occurs in nature.) Wages are one way to do that, but they are far from the only way; they are not even the only way currently in use. As technology advances, we should expect a larger proportion of our income to go to capital—but what we’ve been doing wrong is setting it up so that only a handful of people actually own any capital.

Fix that, and maybe people will finally be able to see that losing your job isn’t such a bad thing; it could even be satisfying, the fulfillment of finally getting something done.

The high cost of frictional unemployment

Sep 3, JDN 2457635

I had wanted to open this post with an estimate of the number of people in the world, or at least in the US, who are currently between jobs. It turns out that such estimates are essentially nonexistent. The Bureau of Labor Statistics maintains a detailed database of US unemployment; they don’t estimate this number. We have this concept in macroeconomics of frictional unemployment, the unemployment that results from people switching jobs; but nobody seems to have any idea how common it is.

I often hear a ballpark figure of about 4-5%, which is related to a notion that “full employment” should really be about 4-5% unemployment because otherwise we’ll trigger horrible inflation or something. There is almost no evidence for this. In fact, the US unemployment rate has gotten as low as 2.5%, and before that was stable around 3%. This was during the 1950s, the era of the highest income tax rates ever imposed in the United States, a top marginal rate of 92%. Coincidence? Maybe. Obviously there were a lot of other things going on at the time. But it sure does hurt the argument that high income taxes “kill jobs”, don’t you think?

Indeed, it may well be that the rate of frictional unemployment varies all the time, depending on all sorts of different factors. But here’s what we do know: Frictional unemployment is a serious problem, and yet most macroeconomists basically ignore it.

Talk to most macroeconomists about “unemployment”, and they will assume you mean either cyclical unemployment (the unemployment that results from recessions and bad fiscal and monetary policy responses to them), or structural unemployment (the unemployment that results from systematic mismatches between worker skills and business needs). If you specifically mention frictional unemployment, the response is usually that it’s no big deal and there’s nothing we can do about it anyway.

Yet at least when we aren’t in a recession, frictional employment very likely accounts for the majority of unemployment, and thus probably the majority of misery created by unemployment. (Not necessarily, since it probably doesn’t account for much long-term unemployment, which is by far the worst.) And it is quite clear to me that there are things we can do about it—they just might be difficult and/or expensive.

Most of you have probably changed jobs at least once. Many of you have, like me, moved far away to a new place for school or work. Think about how difficult that was. There is the monetary cost, first of all; you need to pay for the travel of course, and then usually leases and paychecks don’t line up properly for a month or two (for some baffling and aggravating reason, UCI won’t actually pay me my paychecks until November, despite demanding rent starting the last week of July!). But even beyond that, you are torn from your social network and forced to build a new one. You have to adapt to living in a new place which may have differences in culture and climate. Bureaucracy often makes it difficult to change over documentation of such as your ID and your driver’s license.

And that’s assuming that you already found a job before you moved, which isn’t always an option. Many people move to new places and start searching for jobs when they arrive, which adds an extra layer of risk and difficulty above and beyond the transition itself.

With all this in mind, the wonder is that anyone is willing to move at all! And this is probably a large part of why people are so averse to losing their jobs even when it is clearly necessary; the frictional unemployment carries enormous real costs. (That and loss aversion, of course.)

What could we do, as a matter of policy, to make such transitions easier?

Well, one thing we could do is expand unemployment insurance, which reduces the cost of losing your job (which, despite the best efforts of Republicans in Congress, we ultimately did do in the Second Depression). We could expand unemployment insurance to cover voluntary quits. Right now, quitting voluntarily makes you forgo all unemployment benefits, which employers pay for in the form of insurance premiums; so an employer is much better off making your life miserable until you quit than they are laying you off. They could also fire you for cause, if they can find a cause (and usually there’s something they could trump up enough to get rid of you, especially if you’re not prepared for the protracted legal battle of a wrongful termination lawsuit). The reasoning of our current system appears to be something like this: Only lazy people ever quit jobs, and why should we protect lazy people? This is utter nonsense and it needs to go. Many states already have no-fault divorce and no-fault auto collision insurance; it’s time for no-fault employment termination.

We could establish a basic income of course; then when you lose your job your income would go down, but to a higher floor where you know you can meet certain basic needs. We could provide subsidized personal loans, similar to the current student loan system, that allow people to bear income gaps without losing their homes or paying exorbitant interest rates on credit cards.

We could use active labor market programs to match people with jobs, or train them with the skills needed for emerging job markets. Denmark has extensive active labor market programs (they call it “flexicurity”), and Denmark’s unemployment rate was 2.4% before the Great Recession, hit a peak of 6.2%, and has now recovered to 4.2%. What Denmark calls a bad year, the US calls a good year—and Greece fantasizes about as something they hope one day to achieve. #ScandinaviaIsBetter once again, and Norway fits this pattern also, though to be fair Sweden’s unemployment rate is basically comparable to the US or even slightly worse (though it’s still nothing like Greece).

Maybe it’s actually all right that we don’t have estimates of the frictional unemployment rate, because the goal really isn’t to reduce the number of people who are unemployed; it’s to reduce the harm caused by unemployment. Most of these interventions would very likely increase the rate frictional unemployment, as people who always wanted to try to find better jobs but could never afford to would now be able to—but they would dramatically reduce the harm caused by that unemployment.

This is a more general principle, actually; it’s why we should basically stop taking seriously this argument that social welfare benefits destroy work incentives. That may well be true; so what? Maximizing work incentives was never supposed to be a goal of public policy, as far as I can tell. Maximizing human welfare is the goal, and the only way a welfare program could reduce work incentives is by making life better for people who aren’t currently working, and thereby reducing the utility gap between working and not working. If your claim is that the social welfare program (and its associated funding mechanism, i.e. taxes, debt, or inflation) would make life sufficiently worse for everyone else that it’s not worth it, then say that (and for some programs that might actually be true). But in and of itself, making life better for people who don’t work is a benefit to society. Your supposed downside is in fact an upside. If there’s a downside, it must be found elsewhere.

Indeed, I think it’s worth pointing out that slavery maximizes work incentives. If you beat or kill people who don’t work, sure enough, everyone works! But that is not even an efficient economy, much less a just society. To be clear, I don’t think most people who say they want to maximize work incentives would actually support slavery, but that is the logical extent of the assertion. (Also, many Libertarians, often the first to make such arguments, do have a really bizarre attitude toward slavery; taxation is slavery, regulation is slavery, conscription is slavery—the last not quite as ridiculous—but actual forced labor… well, that really isn’t so bad, especially if the contract is “voluntary”. Fortunately some Libertarians are not so foolish.) If your primary goal is to make people work as much as possible, slavery would be a highly effective way to achieve that goal. And that really is the direction you’re heading when you say we shouldn’t do anything to help starving children lest their mothers have insufficient incentive to work.

More people not working could have a downside, if it resulted in less overall production of goods. But even in the US, one of the most efficient labor markets in the world, the system of job matching is still so ludicrously inefficient that people have to send out dozens if not hundreds of applications to jobs they barely even want, and there are still 1.4 times as many job seekers as there are openings (at the trough of the Great Recession, the ratio was 6.6 to 1). There’s clearly a lot of space here to improve the matching efficiency, and simply giving people more time to search could make a big difference there. Total output might decrease for a little while during the first set of transitions, but afterward people would be doing jobs they want, jobs they care about, jobs they’re good at—and people are vastly more productive under those circumstances. It’s quite likely that total employment would decrease, but productivity would increase so much that total output increased.

Above all, people would be happier, and that should have been our goal all along.