Outsource jobs or insource workers? The choice is clear.

In the New York Times' Economix blog, Nancy Folbre worries about foreign competition hurting skilled Americans:

To import or to outsource? That is the question... 

The disruptive impacts of globalization initially hammered workers without much education...College students in the United States who major in science, technology, engineering and mathematics – often referred to as STEM fields – definitely face better prospects in the labor market than others do. But even they need to be aware of intensified competition down the road. 

Many software engineers and others in the information technology field feel particularly aggrieved about the effects of the H1-B visa program. The computer scientist Norman Matloff, who maintains a Web site on the topic, asserts that [the H1-B program] enables employers to hire younger and less expensive workers, leaving many highly skilled, older programmers in the lurch. 

Workers with H1-B visas have a strong incentive to remain with the employer for whom it was issued in order to obtain a green card, allowing permanent residence in the United States, in a timely fashion. As a result, they have relatively little bargaining power.
Professor Matloff, who favors extensive changes to the H1-B visa program, also warns of the growing impact of outsourcing on jobs for computer science majors.

Now, I share some of Norm Matloff's concerns about the H1-B program. The visas force high-skilled immigrants to stay with a single employer in order to get green cards, thus turning these immigrants into a sort of indentured servant class. This should end.

But concerns over the H1-B program should not translate into a general antipathy towards high-skilled immigration. In fact, concern over outsourcing should make us much more supportive of increased HSI.

Reason 1: Taxes. If an American company - say, Microsoft - hires a programmer in Bangalore, the programmer pays income taxes to the Indian government. But if the same Indian programmer moves to San Jose, he pays his taxes to the American government. That means more roads, schools, etc. for America.

Reason 2: Clustering. Skilled workers like engineers and programmers are more productive when they're around a lot of other engineers and programmers. They exchange ideas and cooperate and start businesses together and hire each other. This is why increasing the number of engineers and programmers does not have only the simple, supply-based effect on wages that Econ 101 suggests. Econ 101 says that if you increase the supply of engineers, the wages of engineers will go down. But in the 1970s and 80s, many thousands of engineers moved to Silicon Valley - and yet the wages of engineers in Silicon Valley went up, not down, because of the clustering effect. In fact, clustering is one big reason why, when you grab a programmer out of Bangalore and plunk him down in San Jose, his wages go way up. 

These are two reasons why it's better to insource high-skilled workers than to outsource their jobs. If I were a programmer, and I had to choose between competing with an Indian guy making $10,000/yr in Bangalore or the same exact guy making $100,000/yr in San Jose and paying taxes to support my kids' schools, you can bet I'd choose the latter!!

(And this is not even mentioning the beneficial effect of high-skilled immigrants on low-skilled American workers.)

Now, here's one possible reply: "Why choose between outsourcing and immigration? Why not just restrict both? Then native-born high-skilled workers will face very little competition from foreigners at all!"

This would be nice, but I don't think it would work. Because it's not true that America's workers would face no competition from their foreign counterparts in such a situation. This is because a lot of our software and technology companies' sales are in foreign markets; they are exports. We sell software and semiconductors and airplanes and business services to Germany and Japan and Australia and China and the UK. And foreign companies - Taiwanese companies, Indian companies, Korean companies - compete with American companies in every one of those markets, no matter how many immigrants America allows in or how much outsourcing we allow. That is competition that we cannot stop.

Right now, the global software industry and the global semiconductor industry are more-or-less dominated by American companies. But that could change. It changed for cars and TVs and batteries in the 70s and 80s. And if it does change for software and semiconductors and aerospace, and if our markets and immigration system are closed, then high-skilled native-born American workers - no matter how well educated and how well trained - will be in big trouble. Their employers, hobbled by high costs and less able to take advantage of clustering effects, will only be able to sell their wares to the domestic American market, which is less than a fifth of the world.

So I don't think we have the option of closing ourselves to immigration AND restricting outsourcing AND taxing imports. Not if we want to maintain anything like our current standard of living, I mean. Instead, we have to choose between outsourcing our high-skilled jobs or admitting more high-skilled immigrants. I think we should choose the latter.

Dinner at Zachary's

Moving out of Ann Arbor, first to Japan for a couple weeks and then on to New York, so blogging will be sparse in August.

Inflation hawks have predicted 10 out of the last zero inflations. Japan printed Space Battleship Yamato-loads of money and inflation went from negative to just over zero. More recently, the Fed's massive asset purchases and ardent promises to keep interest rates at zero until the heat death of the Universe have not pushed core inflation higher than the 2% target. Furthermore, slack demand from Europe and China do not bode well for a robust global recovery in the next few years. Additionally, markets expect very low inflation for a long time to come (and second-guessing the market is always a gamble). Also, the Fed seems to have a good number of influential members who think 5% inflation would be a calamity far worse than 8% unemployment. And finally, liquidity-trap New Keynesian models of the macroeconomy, which recognize the importance of the Zero Lower Bound of nominal interest rates, seem to me far more credible to me than the flexible-price models that predict higher inflation.

To sum up: Inflation looks incredibly unlikely to me at this point. So why on Earth did I just bet Brad DeLong dinner that the U.S. economy will experience substantial inflation at some point during the next three years?

Short answer: Dinner with Brad DeLong at Zachary's will be fun even if I lose.

Longer answer: I got 50-to-1 odds on the bet (if I win, Brad buys me dinner and pays me 49x the cost of dinner). That means that I am essentially betting that there is a 2% chance that something really funky happens to the global economy over the next three years. What kind of funky thing would cause inflation to go over 5% even as unemployment stayed over 6% (as per the terms of the bet)? Perhaps the Chinese state will collapse, disrupting supply chains and sending import costs soaring; or perhaps the Saudi state will collapse, sending oil prices soaring. And perhaps the Fed will be spooked so much by these events that it would allow 5% inflation as a hedge against global economic collapse.

Or, alternatively, perhaps the models (or mental model-sketches) used by John Cochrane, Steve Williamson, Jim Bullard, Charles Plosser, Narayana Kocherlakota, and other inflation hawks are much more right than I realize! I always talk about the basic ignorance of macroeconomists about how the economy works, so in a sense I'm putting my money where my mouth is.

Am I giving Brad a great deal by not demanding 100-to-1 or 200-to-1 odds? Probably, yes. In fact I was going to demand 100-to-1 but I felt it would be rude. But small probabilities are notoriously hard to estimate, or even guesstimate. So instead of expected utility maximization, I'm using a different decision theory rule, limiting my potential losses. The worst that can happen to me is that I will have to buy Brad dinner, during which he will probably laugh at me.

Also, note that if by some odd twist of fate Brad loses the bet, not only will it be good news for the economy (5% inflation would really help us out), but it will mean that the 49 Zachary dinner cost equivalents that he will be forced to pay me will not buy me 49 Zachary's dinners unless I eat way too much pizza...so he'll have the last laugh...

Update: As of 5/6/2013, I have hedged my bet with Brad by taking the opposite side of the exact same bet with Patrick Chovanec at 25-to-1 odds. Note that in theory this means that I could have executed an arbitrage, as long as the price of a pizza was guaranteed to be the same, and if a fractional pizza could be specified. In any case, I now get to have pizza with both Brad and Patrick, at a guaranteed total cost of (nearly) zero, with the possibility of earning 25 times the cost of a Zachary's pizza in the unlikely event that inflation comes in at >5% sometime in the next 2 years...

Inflation predictions are hard, especially about inflation

John Cochrane responds here to a Brad DeLong jibe about inflation predictions. To make a long story short, back in 2009 Cochrane predicted inflation, it hasn't happened yet, and DeLong made fun of Cochrane for that fact. Cochrane essentially has two responses:

1. The inflation prediction was (and is) a statement about risks, not a time-specific forecast.

2. The large federal debt and deficit mean that unless our economy grows rapidly, we must inflate, and hence a period of higher inflation is highly likely at some point.

Regarding Point 1, this is a very fair retort. Predictions are not necessarily forecasts. Saying "we are probably in a bubble that will burst sooner or later" is a different thing from saying "the bubble will burst at 10:36 A.M. next Thursday". I think people instinctively demand too many forecasts and not enough other kinds of predictions from macroeconomic models.

(Of course, when you make a vague prediction like "inflation is likely to come sooner or later," you don't get nearly as many smart-points for being right, because most things happen if you wait long enough. But if that sort of vague statement is the best we can do, it's the best we can do, and the poor forecasting power of existing macro models doesn't leave us with much.)

Regarding Point 2, it is not clear to me that this is true. Let me explain why:

A. If you have a high amount of debt but no deficit, you can pay it off without high inflation or rapid growth. It just takes a really long time. But it's perfectly possible.

B. A high deficit does not require either inflation or growth to go up in the future. Why? Because you can cut the deficit.

So, here's a perfectly possible scenario: We temporarily run a high deficit. Then we balance the budget after that. We have a bunch of debt. Growth remains slow and inflation remains low. Over time, we very slowly pay off the debt, without defaulting, growing faster, or causing higher inflation.

So I don't agree that our current debt and deficit, even in combination, imply that either inflation or fast growth is coming. We could just balance the budget.

Also, an unrelated point - which I am allowed to make, because This Is Blogs. Cochrane makes another statement that caught my eye:

Growth economics is unanimous: You get [fast] growth only from higher productivity, and from letting new innovative competitors dethrone established interests.

I'm not sure I agree with this (maybe I'm disagreeing with John, maybe with growth economics). The part about productivity is right. But the part about letting innovative competitors dethrone established interests? I'm skeptical. Look at Japan and Germany. Japan, I know, protects established corporate interests, to the point of tossing upstart entrepreneurs in jail. Germany I know less about, but I know that turnover among its big companies is quite low. But since 2000, both Germany and Japan have grown more strongly, in real per capita terms, than the U.S. And the U.S. grew rapidly in the 50s and 60s without nearly as much industrial disruption as we have today.

I don't think we know much for sure about the relationship between industrial disruption and productivity growth. It might be better sometimes and worse at other times.


Update: Brad DeLong responds, noting that markets never expected high inflation, in 2009 or since. someone who says inflation is a danger, DeLong claims, should provide a compelling reason why the universe of investors disagrees. He writes:

Thus, Noah, I really do not think it is "a fair retort" to say:

• Risks of high inflation in 2009 were really really big!

• TIPS in 2009 were really really undervalued!

• Investors who were then holding TIPS had huge expected excess returns--and a return distribution with a large negative beta!

• No, I have no explanation for why asset market prices then did not match fundamentals.

This is a point I've made before. And it's one reason why I don't agree with the predictions of Cochrane and other inflation-worriers. That said, I am not as strict as Brad about demanding that people provide compelling explanations for asset mispricings. Housing was mispriced in 2005 and 2006, and we don't really know why, though there are all kinds of explanations. Similarly, market opinions can shift quickly. So I stand by what I said; although I personally think a burst of high inflation is very unlikely over the next decade, if high inflation does come, Cochrane will look "smarter"...though the longer we have to wait for that inflation, the more likely it will be that Cochrane just got lucky.

Update 2: Paul Krugman discusses the distinction between conditional and unconditional predictions. But I think there are two other ways to categorize predictions that are relevant to this example. The first is definite vs. probabilistic predictions. A probabilistic prediction - "Based on events, I now think there's a 70% chance of high inflation within the next 3 years rather than a 30% chance" - is clearly weaker than a definite prediction, since probabilities can't be verified ex post in macro. But it's still a prediction. The other distinction is fixed-lag predictions vs. variable-lag predictions. A fixed-lag prediction is saying "I see you blowing air into that balloon, if you keep doing that it will pop within the next three seconds." A variable-lag prediction is "I see you blowing air into that balloon, if you keep doing that the likelihood of the balloon popping will continue to increase." The latter is weaker than the former because, again, likelihoods can't be verified ex post when there's no replication (as in macro). But it's still a prediction.

What I'm saying is that you can have a "pressure model" in your mind, where printing more and more money causes a higher and higher likelihood of high inflation, but the start of the inflation is still a random shock. This appears to be the sort of model that Cochrane has in mind, much like when Bob Shiller said "this is a housing bubble, the more prices go up, the higher the likelihood that they will experience a steep crash." It's harder to say whether the person making the prediction was smart or just lucky, even when the prediction comes to pass.

Judgment calls and philosophy of science

Lately I've done several philosophy-of-science posts on macro, complaining about what I call "judgment calls" (see here and here). I've been getting a lot of comments about philosophy of science, and I thought I'd take some time to step back and lay out how I think about the subject. So here's a post on Noah's General Philosophy of Science.

Preliminaries:

1. Language. I don't believe in demanding that terms be precisely defined, or in demanding that definitions be perfectly consistent. Reason: Defining terms precisely is incredibly hard. I find that philosophical conversations that focus too much on definitions and jargon quickly get lost in the weeds of "What do you really mean by X?". Sometimes you actually do need to drill down and figure out exactly how you're using a term, or how your usage is different from somebody else's, or how the usage depends on the situation. But I think that sort of intense focus on terminology should only be used sparingly, in times of great need.

2. Past Philosophers. I only care a little bit about what Popper, or Kuhn, or Feyerabend said. Not zero, because those guys were smart, and they read a lot of history and a lot of other people's ideas. But I only care a little, because I really want to figure these things out for myself instead of taking the word of an "expert", and I believe that I am intellectually equipped to do so (note: I am not trying to push the boundaries of philosophy-of-science scholarship here). So if I say something that conflicts with what Kuhn thought...well, as the Japanese say, sho ga nai ne! So to anyone who reads this and says "You are such an ignorant amateur, why don't you go read what some real experts say?!", I preemptively reply: "Why don't you try thinking for yourself instead of parroting an authority figure?" I see absolutely no reason not to reinvent the wheel occasionally. (If you're interested, my main sources of ideas were probably Charles Marcus, Robert Laughlin, Richard Feynman, Lee Smolin, Robert Waldmann, and Steven Smith, as well as some of those well-known philosophers. But some parts I think I just made up.)

3. Ontology. Ontology is the philosophy of what existence means. My ontology is basically what I think of as "pragmatist"...we believe things because it's useful to believe them. If you disbelieve in the existence of a wall, you're going to stub your toe and it's going to be unpleasant. Or maybe not...try disbelieving in the wall and let me know the result. I'll be over here with a beer, getting your experiment on video. That's basically my philosophy of what "existence" means. One result of this outlook is that I think of "detectability" (or "observability") is the same thing as "existence"...if you can't in some way, however indirectly, stub your toe on something, it might as well not exist. I don't know if that's what other people mean when they say "pragmatism", but see Point 1 about language.

4. Epistemology. Epistemology is the philosophy of how you can know things. When it comes to science, there are limitations on how much you can know. Tomorrow, things might all start falling up. You don't know that they won't! This is, I believe, called the "paradox of induction". "Laws" of the Universe might change tomorrow. If we're lucky, they won't change. So far, things still fall down. Whew! Also, tomorrow there might cease to be any sort of "laws" at all. See here and here for ideas about what might happen in that case. Suffice to say that it would be a weird, weird day.

OK, now that those are out of the way, on to My General Philosophy of Science:

5. The Goal of Science. The main goal of science, as I see it, is to increase humankind's power over the Universe. Where did I get this goal? Simple; I made it up...where else does anyone come up with goals? Anyway, combining this goal with Point 3 about ontology, I think the aim of science should be to give humankind the ability to accomplish pragmatic things, like predicting future phenomena, or making technology, etc. A secondary goal of science would be for its direct pleasure benefit for non-scientists - e.g., people can read about infinite inflationary cosmology and go "Wow, that is neat-o!" I do not view the pleasure of scientists themselves as a goal of science.

6. Scientific Models. George Box famously said that "All models are wrong, but some are useful." To me, this is like saying "No house is 100% big, but some are big." It's just a silly statement. No model describes all of reality. Most or all models fail to perfectly describe the set of phenomena that they purport to describe. And if some model - say, general-relativistic quantum mechanics - does perfectly describe its chosen set of phenomena,  - say, quantum mechanics, or general relativity - it would hardly be worth mentioning. Combining Points 3, 4, and 5, I think that "useful" and "right" are the same thing when it comes to scientific models. Perfect usefulness ("rightness"?) is only one measure-zero point on a multidimensional continuum of rightness/usefulness.

7. Techniques of Science. It seems to me that all scientific endeavors involve three basic processes: A) Logic, B) Evidence, and C) Judgment.

7a. Logic. Logic, to me, means following some sort of rules for your arguments. I basically think you should always use some sort of logic when you make your arguments, since it seems to help convince people of stuff in a repeated and consistent manner. Other methods of convincing - appeals to emotion, for instance, or tribal affiliation - do also seem to work sometimes, but more sporadically. So I think scientists should always use logic when they can. But logic is like a rule for constructing a chain...it doesn't tell you where to start the chain. You need some sort of premise or starting point.

7b. Evidence. Evidence, to me, means that how well a theory matches past or existing data is an indication of how right/useful the theory is. If science is going to work, then tomorrow is going to be have to be something like today. By Point 4, this means that there have to be some sort of "laws", over some sort of time horizon. Maybe the laws only hold for a short time, but they have to hold for longer than it takes you to figure them out. This means that "induction" is going to have to work to some degree. In other words, how well a model or theory describes data today must be some sort of indication of how well it describes data tomorrow, or else science is useless.

Now here we get to an interesting side question: In what way is a theory's descriptive power an indication of its prescriptive power? You can say "If a theory doesn't match the data, it's not useful/right." This, I think, is what people call "falsification". Alternatively, you can say the converse: "If a theory does match the data, it is useful/right." I don't know a name for that. Actually, I think both these statements are too extreme. Rigid insistence on pure falsification is not always a good idea, since the cutoff for saying a theory "matches data" is pretty arbitrary, like a confidence interval in statistics. And the converse of falsification - "It looks right so it must be right" - seems to lead to overfitting. Also, ranking theories based on how well they fit the data has its own pitfalls, since theories that make big mistakes can sometimes lead to future theories that make fewer mistakes than the best presently existing theories - as an example, Copernicus' initial heliocentric model was less good at predicting eclipses than Ptolemy's geocentric model with epicycles, but it led to the creation of Kepler's theory of elliptical orbits, which predicted both eclipses and planetary phases better than Ptolemy's model.

So in selecting your criteria for matching theories to evidence, you inevitably need to use some judgment.

7c. Judgment. Judgment, to me, basically means using your intuition or instinct to tell you things about the world. Some of this is always present in science, for the reason specified above (deciding how to match theory to evidence). Also, there's at least one other reason to use judgment, since formally there are infinite possible hypotheses to test, and infinite models that fit any set of phenomena. (This is pointed out by Robert Pirsig in Zen and the Art of Motorcycle Maintenance, which is mostly a first-hand account of psychological disorder, but is also a cool philosophy-of-science book.)

So you always need some judgment in science. There are many different ways to use judgment, and the the amount to which you use judgment in each of those ways can vary. A few examples include: What do you set as the null and alternative hypotheses? What kind of confidence intervals do you set for your regressions? Do you toss outliers, and if so, which? How do you penalize the addition of parameters to the model? And, most importantly, there is The Big Judgment Call: How do you use the match between theory and evidence to evaluate the usefulness of a model?

Basically, it seems to me that if you don't have logic, evidence, and judgment, you're not doing science.

8. Different "Scientific" Disciplines. There are many different disciplines that purport to inform us about the world - physics, history, biology, economics, etc. Some of these call themselves "sciences", some call themselves "social sciences" with an emphasis on the "social", and some don't call themselves "sciences". The ways in which these disciplines use evidence and judgment are different. Furthermore, the way that each discipline uses evidence and judgment may change in time (witness the changes in physics between Aristotle and Feynman!). These changes are probably evolutionary, based on trial-and-error - basically, kick your foot into a way of doing science, and see if you stub your toe or not. Aristotle's way of doing physics wasn't useful for producing models that allowed gunners to hit targets accurately from long distances; Newton and Galileo's was. Aristotle's way of doing physics mostly died out, Newton and Galileo's survived and evolved.

Evaluating the usefulness of an approach toward science involves a sort of meta-science, involving evidence about judgment, judgment about judgment, evidence about evidence, and judgment about evidence. Physics seems to have a lot more replicability than history, which is probably why physics has usually relied a lot more on evidence, and history a lot more on judgment. Nowadays, with the controversy over string theory, we see a big debate over whether physics should evolve toward a method in which evidence is less important. In history, with the efforts of people like Daron Acemoglu and Jared Diamond, we see people arguing that history can rely more on evidence than in the past. (Yes, I am using the terms "less evidence" and "more evidence" VERY loosely, see Point 1 about language.)

So each science has its own "scientific method", and this method evolves in time. We just have to figure out what seems to be working and what doesn't seem to be working, and adjust accordingly. Its not always obvious, and it's certainly not a rapid process. We can only hope that the marketplace of ideas promptly produces the best scientific method for each discipline given the available technologies of investigation. But it seems to me, witnessing the failures of science to blossom in the Roman Empire, Abbasid Caliphate, and Sung Dynasty, that opportunities to improve science are often missed.

9. A Few Unhelpful Ideas About Science. I'm not sure if anybody says exactly these things, but they loosely conform to ideas that some people seem to entertain (i.e. they are straw men), so I might as well list them...

"Since all disciplines use judgment in some way, all uses of judgment are equally appropriate." Actually, different uses of judgment sometimes seem to produce radically different results within a discipline, as with the oft-discussed shift in physics, chemistry, and biology in the 1600s and 1700s.

"Science does not always need evidence; sometimes, we can start from judgment and proceed by logic to a conclusion, and then accept that conclusion without checking it against evidence." This is like when the evil wizard tries to win by turning himself into a snake...it never works.

"We only make theories in order to check the internal consistency of our ideas." Who cares? Remember, scientists having fun is not a goal of science (according to my arbitrary value system). We do not pay you $200k/yr to play video games; why should we pay you $200k/yr to satisfy yourself of the internal consistency of your ideas?

"It takes a theory to beat a theory." This may be true sociologically, as a description of how science evolves in practice, but I don't think this ought to be the case. I am fine with doubt and ignorance. I think that bad ideas can block and delay the development of good ideas. And I think that a false sense of certainty can lead to mistakes (e.g. by policymakers).


And, finally, we come to the application of this General Philosophy of Science to macroeconomics. There is not much new in this section; it's a summary of things I've said before, and I just included it here so that you could see how I map from my philosophy of science to my tendency to complain about macro. Since it's such a rehash, this will be my last big complaint session about macro for quite some time.

10. Judgment Calls in Modern Macroeconomics.

I occasionally complain about certain uses of judgment in modern macro. These mostly revolve around one complaint: The macro field does not, in my opinion, use sufficiently stringent criteria for rejecting theories. Multiple theories are simultaneously judged "good" by explaining the same stylized facts (for example, producing simulated economic fluctuations in GDP that match the variance of observed GDP)...These mechanisms can't all be accounting for 100% of the same phenomenon at the same time! Also, macroeconomic models are rarely if ever tossed out because of the results of some statistical test (statistics being the only way we have of matching macro theories to data, since experiments are unavailable). Additionally, the microfoundations used in macro theories are not required to match the microfoundations observed by microeconomists. It thus seems to me that there is "too much" judgment involved in modern macro, and "not enough" evidence. Yes, "too much" and "not enough" are loose terms. 

I suspect that the reason for this, historically/evolutionarily speaking, is the poor quality of macro data. Macroeconomics has better data than history, but not a lot better! You're still dealing with time series that may or may not be ergodic (in other words, macroeconomic history may have just been "one damn thing after another", with no stable "shock process" or "adjustment process"). The time series may not be stationary (unit root tests have low power). Cross-country comparisons are notoriously difficult.

So if you require macroeconomics to hew to the same standards of empirical verification/falsification as, say, tax economics or financial economics, you will be left scratching your head most of the time and saying "Well, we just really don't know what the heck is going on!" So, historically, macroeconomists had to settle for less ambitious goals. They had to behave more like historians, writing "literary" tomes vaguely describing what they thought was going on. After World War 2, this changed, and macroeconomists started to describe their ideas in the language of mathematics. For a while, people thought macro could work a bit like physics, but the Lucas Critque and some notorious policy mistakes seem to have dashed that ambition. Now, macroeconomists seem to be back to "telling stories" (a phrase they themselves often use), though they've retained the language of math.

One common response is that macroeconomics produces a bunch of different models that tell a bunch of different stories, and that judgment should be used to select which stories apply at which times. And I am OK with that in principle! Maybe evidence, rather than judgment, can be used to tell whether, for example, the Diamond-Dybvig model of bank runs is about to come into effect (people like Markus Brunnermeier and Hyun Song Shin are trying to do things like that, which is just one reason why I am big fans of theirs). But the set of possible stories is essentially infinite; it is a certainty that some of these models are bad ones; that they are not as good as they could be, and that evidence could be used to show this and to construct better models. I feel - and this is just the sense I get from talking to people and going to talks and reading papers - that not very much model rejection is being done.

In other words, although I am not a strict "falsification-ist", I think that rejecting models is almost certain to be an essential feature of using evidence to select the best set of models to use in practice.

And what I suspect is that macroeconomics went so long without any hope of matching any data that it developed bad habits. Internal consistency and the collective intuition of macroeconomists were overemphasized, and what little data there was was often ignored. Theoretical tolerance became the norm, and models that were essentially never useful remained prominent in the toolkits of economists and policymakers alike. And the large reliance on judgment seems (unsurprisingly) to have allowed some political bias to seep into the profession.


So to sum up: I don't complain about macro methodology because I have a rigid idea of what "science" ought to be, and I demand that all disciplines either live up to the standards of physics or admit radical ignorance. I simply judge that macro has too much judgment in too many places, that there are popular models out there that could and should be rejected by what little evidence exists, and that many macroeconomists should admit more doubt about our understanding of the "business cycle".

Maybe I'm wrong, and if so, I'm prepared to revise my thinking...

The Lucas Critique and the hard-money consensus

The Lucas Critique is simple, and it is correct. If you have a model of the economy that works pretty well, and you try to use that model to predict the effects of a new policy, the policy may change people's behavior so that your model no longer works pretty well, thus leading (among other things) to the policy failing to have its intended effect.

The Lucas Critique was applied by Lucas to invalidate many of the "Phillips Curve" models of the 1970s. The idea was that if central banks cause inflation in an attempt to pump up growth, people will start expecting higher inflation in general, and the inflation-growth relationship that held in the past would change. That seems to have been borne out by the events of the 70s. And so people started to think that the Lucas Critique was of great practical importance.

The Lucas Critique still figures prominently in debates between macroeconomists today. Here is Charles Plosser, one of the founders of "Real Business Cycle" theory, criticizing the New Keynesian DSGE models that are popular nowadays:

In my view, the current rules of the game of New Keynesian DSGE models run afoul of the Lucas critique... 

I have always been uncomfortable with the New Keynesian model’s assumption that wage and price setters have market power but, at the same time, are unable or unwilling to change prices in response to anticipated and systematic shifts in monetary policy. This suggests that the deep structure of nominal frictions in New Keynesian DSGE models should do more than measure the length of time that firms and households wait for a chance to reset their prices and wages... 

When the real and nominal frictions of New Keynesian models do not reflect the incentives faced by economic actors in actual economies, these models violate the Lucas critique’s policy invariance dictum, and thus, the policy advice these models offer must be interpreted with caution... 

During the 1980s and 1990s, it was quite common to hear in workshops and seminars the criticism that a model didn’t satisfy the Lucas critique. I thought this was often a cheap shot because almost no model satisfactorily dealt with the issue. And during a period when the policy regime was apparently fairly stable — which many argued it mostly was during those years — the failure to satisfy the Lucas critique seemed somewhat less troublesome. However, in my view, throughout the crisis of the last few years and its aftermath, the Lucas critique has become decidedly more relevant.

First, let me say that I agree with Plosser: Calvo pricing, which is one feature of New Keynesian models about which Plosser is complaining, seems to me not to satisfy the Lucas Critique. (As Plosser points out elsewhere, it also looks to be simply false.) Models in which firms choose when to change their prices are much more desirable. Of course, people are working on these. They are really hard to do, since the decision to change prices can depend on all sorts of weird, hard-to-aggregate stuff, like coordination with other price-setters. This kind of realistic behavior is very hard to shoehorn into the kludgey modeling framework of DSGE, which is why the New Keynesians have been forced to adopt Calvo Pricing as a modeling convenience.

But I digress. What I really want to talk about is the question of how a model satisfies the Lucas Critique.

Plosser says that "almost no model [has] satisfactorily dealt with the [Lucas Critique]." This implies that some of them do. How do we identify the few that do?

In a science, the way you establish the correctness or incorrectness of a proposition is by looking at some sort of real-world evidence. What kind of real-world evidence would allow macroeconomists to know that one of their models is policy-invariant? Well, you could have a central bank try out some policies as an experiment, and see if the model's predictions held up. Or you could call up Blizzard Entertainment and get them to flood Diablo III with virtual gold, or something like that.

Alternatively, you could understand the behavior of individual consumers and firms really, really well, and then find some way to aggregate them that is robust in agent-based simulations. In other words, we could get real microfoundations. This is extremely hard to do, of course.

But this is not actually what macroeconomists do when the subject of the Lucas Critique is brought up. Instead of looking at evidence, what they do is make a judgment call. If all the pieces of a model sort of intuitively seem like things that wouldn't change under different policy regimes, then people nod their heads and say "OK, that seems like it satisfies the Lucas Critique", and they think no more of it. This basically happened with RBC models. "Technology shocks" sound like something that people don't control, and that therefore couldn't change if policy changed. And people assumed that the other features of the model (costless price changes, for example) would hold up under different policy regimes as well. So RBC was thought to have satisfied the Lucas Critique.

But if someone says "Hey, these shocks don't seem structural," or "Hey, I think agents in this model would change their actions in response to policy, don't you?", then there is no consensus, and a vocal group of dissenters continues to say that a model "fails the Lucas Critique". This is what has happened with New Keynesian models. To see how this works, check out this 2008 paper by Chari, Kehoe, and McGrattan of the Minneapolis Fed. They discuss the Smets-Wouters model, widely considered to be the "best" of the New Keynesian models:

The Smets-Wouters model has seven exogenous random variables. We divide these into two groups. The potentially structural shocks group includes shocks to total factor productivity, investment-specific technology, and monetary policy. The dubiously structural shocks group includes shocks to wage markups, price markups, exogenous spending, and risk premia.

How do the authors decide which shocks are "potentially structural"? They don't say. The "dubiously structural shocks" are labeled "dubious" because of a mix of evidence and reasonable-sounding thought experiments that show how these shocks change, or might change, in response to changing economic conditions.

But of the "potentially structural" shocks they say nothing. They simply give technology and policy shocks a free pass. These parts of the model are thus judged to "satisfy" the Lucas Critique because no macroeconomists - or, at least, none who matter! - happen to be concerned about whether they satisfy the Lucas Critique.

In other words, the decision of whether a model satisfies the Lucas Critique is made not by evidence, but by the consensus judgment of macroeconomists.

This is just one more judgment call in macro. Which means one more place where personal and political bias can creep in. In the comments on my earlier post, someone wrote: "I think of the Lucas Critique as a gun that only fires left." What that means is that in practice, the Lucas Critique is generally brought up as an objection to models in which the central bank can stabilize output. Or in other words, consensus has a well-known hard-money bias.

Update: Steve Williamson offers some thoughts, especially this:

[W]hat's the big deal? Noah seems endlessly perturbed that economics is not like the natural sciences. There are no litmus tests that allow us to throw out bad theories so we can be done with them. But that makes economics fun. We have to be creative about using the available empirical evidence to reinforce our arguments. We have to be much more creative on the theoretical side than is the case in the natural sciences. We get to have interesting fights in public. Who could ask for more?

File this under "things I cannot possibly argue with"...

Cyber Liability Insurance

As absolute dependence on computers and computer stored information grows there are new ways companies can be sued by third parties for damages.  When private information such as dates of birth, social security numbers, credit card numbers, etc are stolen off of your business' computer systems, it is called a data breach, and they are very costly to manage.  The insurance industry has calculated this cost to be about $200 per individual whose information was taken.  Also, if a malicious virus is distributed from your computer to a customer’s computer system and causes damage, you could be held responsible for cost to repair their system.  Your basic general liability policies are not designed to pay for such claims so many times when businesses look to their business policies the coverage is not there to help with these expenses.  Because of that, insurance companies have developed a new product called cyber liability.  It is designed to step up and pay for the third party damages caused by your data breaches and damage by viruses to other’s computer system.

So what kinds of business should be looking into this new cyber liability products?  Any business with a computer, especially one that stores or interacts with any private information or distribute emails to others should look into this product.  Restaurants and retail stores that take credit cards, professional business that store dates of birth, driver’s license numbers and social security numbers, even doctors’ offices are at risk for the types of claims mentioned above.  Unfortunately, even if a business has the best firewalls and antivirus software, they still are at risk of data breaches and malicious viruses.  Cyber liability is something designed to help protect your business assets if an unpreventable claim strikes.

Something Big happened in the early 70s

Dissertation successfully defended (it won't be official until I complete the byzantine formatting requirements, but that will happen in a couple days), so back to blogging slash getting ready to teach finance to MBAs...

Reading Paul Krugman this week, I found that a question that has often nagged me is nagging me more than usual. Take a look at these two graphs that Krugman put up:

Yes, I know that trends in squiggly lines can be illusions. They might not represent anything structural. "The trend is your friend until the bend at the end," as they say. But WOW, doesn't it look like there was some sort of trend break in the early 1970s? Also, that Krugman graph shows labor productivity, but if we look at Total Factor Productivity, here is what we see:

Now, this is not exactly couched in the careful, formal language of macroeconometricians, but it seems like Something Big happened in the early 1970s.

What was it??

First, there are a few candidate "trend break events" that seem dubious to me. The first is a general technological slowdown, a la Tyler Cowen. It does not seem very likely that the "low hanging fruit" of science would suddenly run out within a two-year period. Also, the "neoliberal"/Reagan policy revolution seems an unlikely candidate, since that happened after 1980. Finally, the entry of women into the workforce was slow and steady.

The decline of unions has been postulated as a cause. And there does look to be an especially steep dropoff in membership in the late 70s:

But this is a bit problematic too, because A) the trend break seems to come a few years later than the earlier ones, and B) it's not clear that union-bashing policies were in place in the early 70s. I guess I wouldn't rule this one out, but my intuition is that it was a result, not a cause, of the Something Big.

This leaves me with two candidate explanations for the possible early-70s trend break. These are the end of Bretton Woods in 1971, and the Oil Crisis in 1973.

The end of Bretton Woods seems like a big deal. It ushered in the era of floating exchange rates and ended the de facto gold standard that had prevailed since WW2. Why would this have held down wages in the U.S.? Well, it might have allowed the start of globalization, which began to add labor-rich, capital-poor countries to the rich-country trading system, thus holding down wages via factor price equalization. The catch-up of Europe and Japan in the 70s and 80s, and then of China et al. in the 2000s, might have held down U.S. wages as these countries' catch-up productivity gains outpaced their wages. Alternatively, exchange rate risk must have spiked after the end of Bretton Woods; this could have reduced investment as a percent of GDP, raising the return on capital relative to labor, while simultaneously decreasing nondurables TFP via endogenous growth effects. I'm not quite sure if either of these mechanisms holds up under close scrutiny, however.

The Oil Crisis of '73 seems like a big deal. It represented the start of an era of highly variable energy prices. Since energy is an input for basically everything, lots of people have speculated that higher (and more variable) energy costs have caused a general productivity stagnation. Peter Thiel puts the argument thus:

[W]e've had basically no progress on energy.  And if you think about where oil prices were in 1973, it was $2 or so a barrel, it is now at north of $100 a barrel, and so you've had sort of a catastrophic failure of energy innovation.  And it's basically been offset by computer innovation.  I think that's sort of the simplified account of what's happened in the last 40 years.

This more limited version of the "Great Stagnation" hypothesis seems much more likely to me than the more general "end of science" version.

So there are two possible culprits for the Something Big that happened in the early 70s. Of course, even if one of these is the villain, it's possible that it's only a proximate cause for something more fundamental. The end of Bretton Woods may have been an inevitable, necessary response to increasing globalization that was made possible by falling transport costs, maybe due to the invention of containerized shipping (and, later, the internet). The rise in oil costs may have been due to increasing extraction costs and decreasing discoveries, and floating exchange rates might themselves have played some sort of role. The question of which "shocks" are truly fundamental, or truly "structural", is a bit like the question of whether guns kill people or people kill people.

But the important point is, something happened in the early 70s that seems to have been a trend break in how our macroeconomy works. We should be examining economic theory and econometric evidence, in ways far more careful and thorough than this blog post (or any blog post), for clues as to the processes that caused the trend break. If necessary, we may have to modify our theories...but isn't that always the case?

Update: It looks like Daniel Kuehn did a very similar blog post about a month ago, and came to some of the same conclusions...

Microfoundations would be nice if we had them

Matt writes:

But while reduction is something scientists inquire into, it's never been the case that reduction is the sine qua non of useful scientific inquiry. To the best of my understanding, Newton's account of gravity didn't have any microfoundations at all. It was a mathematical formalization of observations about how the world functions... 

And crucially the key test a purported effort to provide microfoundations for gravity must pass is that it has to explain gravity well. The trend in economics since the Lucas Critique seems to be the reverse. If a theory lacks adequate microfoundations, it's rejected out of hand while you get a lot of wriggle room in terms of accounting for the data properly... 

People who seem very bothered by the fact that the observed reality of nominal wage stickiness is not well-microfounded don't appear to have the same difficulty relying on the observed reality of gravity. This is particularly strange since microeconomics itself is not particularly microfounded in psychology or neurology... 

The right answer is that productive inquiry can happen at many different levels of understanding and the appropriate test of a theory is whether it gives some kind of useful account of the thing it's supposed to explain.

I have a few responses to this, which don't fit into a coherent whole, so I'll just make a list:

1. Gravity is different from macroeconomics in several ways. One way is that we don't actually know if there is any phenomenon "underlying" gravity. Gravity might not be explainable in terms of any broader, more general phenomenon. But we know for a fact that macroeconomics is the result of a whole bunch of little economic decisions by individuals and companies. This, not the Lucas Critique, is the real reason we look for microfoundations for macroeconomics. Now, maybe some macroeconomic phenomena don't have microfoundations; maybe they are emergent. But we don't know that yet.

2. Another way in which gravity is different than macroeconomics is that you can test gravity in a lab. With macro, our empirical data come from economic time series, which are basically a very poor window into the phenomena we're trying to understand. Time-series econometrics, currently our only tool for matching macro theories to data, is pretty inadequate. Microeconomics, however, often can be tested in a lab, or with reasonably abundant "natural experiments" that don't exist in macro. So finding microfoundations for macroeconomics would allow us to be more scientific about the whole thing.

3. Matt says "the appropriate test of a theory is whether it gives some kind of useful account of the thing it's supposed to explain". This is a paraphrase of Milton Friedman's quote that "theory is to be judged by its predictive power for the class of phenomena which it is intended to 'explain.'" But "useful account" and "predictive power" are not precisely defined terms. For example, people once believed that psychological stress caused ulcers. This explanation had good predictive power - people who were under a lot of stress started having ulcer pain. And it was a useful account, since you could reduce pain by reducing stress. But eventually we found out that bacteria play a big role in ulcer formation, almost certainly bigger than the role played by stress. In this case, a decent non-microfounded theory was vastly improved by a successful search for microfoundations.

4. Having said all this, I agree with Matt that there is too little respect for data in macro today (probably because if there were more respect for data, macroeconomists would have to say "We don't really know whats going on" much of the time). And I agree that productive theoretical analysis can in principle be conducted at any level of complexity. If this idea is used to argue that we shouldn't require macro theories to be microfounded, then I agree. If this idea is used to argue that looking for microfoundations in macro is a waste of time and effort, then I disagree. Having microfoundations is better than not having them. 

5. Once again, let me reiterate that the big problem with "microfounded" macro, as I see it, is that the "microfoundations" are bad: not credible, and generally not consistent with anything microeconomists have actually found. Bad microfoundations are worse than none at all.


Update: Here's Peter Dorman with some specifics about bad microfoundations. Actually, having read a bazillion experimental papers, I am of the opinion that some neoclassical micro matches reality pretty well and some doesn't. See here for a partial survey.


Update 2: Here's Tyler Cowen with some thoughts on microfoundations and wage flexibility. He also makes the excellent point that "microfoundations" is not synonymous with a certain policy attitude; in other words, we shouldn't make the mistake of thinking "microfoundations = hard money" and then conclude "Let's ditch microfoundations"...

Steve Williamson explains modern macro

...Well, not actually all of modern macro. Just the way that modern macroeconomists define the "business cycle". But since the "business cycle" is the phenomenon that macroeconomists want to explain, this is really key, and it's something that doesn't get talked about a lot. So go read Steve Williamson; the post is really quite excellent, you will learn more about modern macro from reading it than from days of reading a graduate textbook. After you are done reading, then come back and continue this post.

What is a "business cycle"? It's not immediately obvious what it is. We have this idea that sometimes the economy does well, and sometimes it doesn't do well. Sometimes jobs are hard to find and don't seem to pay very well, sometimes jobs are easy to find and just throw perks at you. Sometimes lots of new buildings are going up, sometimes not many are. Etc.

Unlike seasons, these business "cycles" seem not to all last the same amount of time, or come at regular intervals. So maybe the "cycle" is really just randomness. Sometimes something happens to make the economy go well, sometimes something happens to make the economy not go well.

BUT, here's the thing...the economy seems to do steadily better and better over time. Only rarely do things get so bad that we actually produce less stuff than the year before. So people often think of the economy as containing a "trend" - some underlying force making us do better and better - and a "cycle" - some random thing that makes the economy do even better than the trend, or even worse, for a short while before going away.

The Hodrick-Prescott "Filter" (or H-P Filter) does not clean your water supply. It is a method for turning a time-series - say, GDP - into a "cycle", by subtracting out the "trend". If you are a "business cycle theorist", what you do for a living is basically this:

Step 1: Subtract out a "trend"; what remains is the "cycle".

Step 2: Make a theory to explain the "cycle" that you obtained in Step 1.

The H-P Filter is just a method for doing Step 1. You take a jagged time-series and you smooth it out, and you call the smoothed-out series the "trend". That's it. Whatever is left you call the "cycle", and you make theories to try to explain that "cycle".

But how much do you smooth? That's a really key question! If you smooth a lot, the "trend" becomes log-linear, meaning that any departure of GDP from a smooth exponential growth path - the kind of growth path of the population of bacteria in a fresh new petri dish - is called a "cycle". But if you don't smooth very much, then almost every bend and dip in GDP is a change in the "trend", and there's almost no "cycle" at all. In other words, YOU, the macroeconomist, get to choose how big of a "cycle" you are trying to explain. The size of the "cycle" is a free parameter.

Now, let's think about those explanations. One such explanation is the original RBC ("real business cycle") model, invented by the same Prescott who invented the Hodrick-Prescott Filter. This model won Prescott a Nobel Prize in 2004. I've criticized the RBC model, but let's forget about that criticism for now. How did Prescott show that his model explained the business cycle? What he did was this: First, he chose some values for the parameters in the RBC model that seemed reasonable to him ("calibration"). Then, he simulated an economy with the RBC model, and measured the size of the simulated fluctuations that it produced. Finally, he compared the size of those fluctuations with the size of the "cycle" that he got out of an H-P Filter, and decided that the two were pretty close in size. Thus, he concluded, the "cycles" of economic activity that we see in the real world could be generated by the RBC model, and hence the RBC model was a good one.

Now, I've criticized this method of validating models (which is called "moment matching"). But let's put aside that criticism for now, and think about the H-P Filter. Remember that we get to choose how much to smooth the time-series. The less we smooth, the smaller the "business cycle" becomes. So, up to a point, by choosing how much to smooth, we can choose to make the business cycle as big or as small as we like!

So what Prescott did was:

A) Chose how big of a "business cycle" he wanted to model,

B) Built a model of business cycles that produced fluctuations of about the size he chose in step A, and

C) Claimed to have explained the business cycle.

Now this may sound like a big fat hoax, but it's not quite. The amount of smoothing in the H-P filter is a free parameter, but if you choose it too big or too small, people will be skeptical. After all, we have other measures of recessions, like the "NBER recessions". If you smooth so much or so little that your "cycles" don't coincide at least roughly with those recessions, people won't buy your theory. They will say "Aww come on, really?" And in fact, some people responded that way to the RBC theory when it came out. But a critical mass of people gave it credence, which is why it became the basis of most subsequent models of the business cycle, and won a Nobel Prize.

What I want to point out here is how many judgment calls there are in modern macro. There is the judgment call of how big you think the "business cycle" is compared to the "trend". There is the judgment call of the parameters you think are reasonable to stick into your model. And there is the judgment call of whether you think the simulated fluctuations produced by your model are "close enough" in size to the real fluctuations. Actually, there are more judgment calls I haven't even talked about, such as the judgment call of whether a "shock" (a random thing that causes "cycles") is "structural" or not (see here if you want to learn more about that).

Now, some modern macroeconomists will tell you that all these judgment calls are fine. A theorist's conclusions, they will tell you, follow from their assumptions. Judgment calls are just assumptions.The job of a theorist, they will tell you, is to make a theory that is internally consistent. The purpose of the mathematics is to show that the conclusions - the policy recommendations, the forecasts - flow logically from the assumptions, or judgment calls. As for which judgment calls are appropriate, well, that is what academics spend their time arguing about, using their common sense to guide them.

Doesn't it seem to you that this way of doing "science" is a little too vulnerable to cultural/political biases among the body of practicing macroeconomists? It seems that way to me. But maybe I am wrong.

"This time it's different": China edition

I confess: I take a guilty pleasure in reading articles that claim "This time it's different". Back in 2004-5, when I started reading financial and economics news, all of those yarns were about how U.S. housing prices had reached a permanently high plateau, or how the financial system had evolved to be able to spread risks to the people most willing to bear them, etc. etc. It was fun. Everyone seemed to know it was BS. We just sat there imagining who would possibly believe it.

Anyway, the fun is back. As China slips into a slowdown, a few people are piping up to claim that no, this time it's different, China has it all figured out, their culture is different, their government is better, and so forth. But so far, I haven't seen anyone do this routine as beautifully as James White, analyst at Colonial First State. In an article flagged by Izabella Kaminska of FT Alphaville, he writes:

China’s rise confounds economic history, but not necessarily economic theory...[its] foundations seem brittle to western investors used to judging the health of an economy through the returns on capital. But the Chinese are comfortable with low capital returns if the pay-off is a stronger economy. This has been the case... 

The Chinese don’t play chess. They play wei qi [also known as Go]...The Chinese government views the economy as though it’s wei qi. Each piece has its own role in the economy, but each is no more important than another. 

This is an important observation. In the developed world...Falling or negative returns on capital are a sure sign of economic weakness reflecting the end of a period of over-investment...In China, capital is just one piece on the board where the aim is to raise living standards of all households...The government’s role is paramount. Despite claims of dramatic imbalances (investment spending has made up to 45% of GDP in recent years, compared to below 15% in some developed economies), investment is driving sustainably higher economic growth... 

At a macro-level, the higher allocation of capital in China has led to falling profit growth and lower returns for capital...Since 2004...China['s stock market] is up just 46%...2011 has been punctuated with stories of large capital losses across the economy... 

By not using capital returns as a scorecard for economic progress, China improves the allocation of capital in its economy and raises living standards. Effectively, China takes a broader perspective to the value of capital in an economy... 

First, and most obviously, the government has the ability to fund losses on individual capital projects through the accumulated financial reserves, totalling at least $3.2 trillion. Second, and most importantly, the Chinese government, as ultimate capital allocator, can recoup returns from projects by capturing the positive externalities from projects in the form of higher tax revenues created by higher levels of activity... 

China’s economic performance in the last 20 years has been remarkable; very strong growth and low inflation...The government, as the largest capital allocator, can both manage losses from individual projects and capture the benefits of loss-making projects through its taxcollecting authorities. 

I love this. It has all the classic hallmarks of a "This time it's different" piece. First - and this is my personal favorite - we have the cultural analogy. Chinese people don't play chess, they play Go! And by realizing this we can understand why low capital returns are irrelevant! It's all about the Eastern Mystique...this time it's different because these people are different.

(Of course, an astute reader might point out that Japanese people also play Go, and until the last couple of decades absolutely dominated international Go competitions. That didn't prevent the Japanese economy from tanking, and it didn't change the fact that wasteful investment was a central feature of said tanking.)

Next we have the faith in the government. China's government, we are told, is a benevolent sort of oligarchy, whose "aim is to raise living standards of all households". Never mind the massive corruption and state-corporate collusion. Never mind the negative real rates of return on Chinese household deposits. Never mind China's slow consumption growth and low share of household consumption in GDP. Never mind the Latin America-like levels of inequality. China's government, unlike our Western variety, is all for The People, despite the curious fact that The People there have less say in the government's operations. (Note how this also plays to Orientalist stereotypes: the Chinese as a Confucianist hive mind.)

But the Chinese government isn't just benevolent, it's omnipotent! The government can bail out loss-making capital projects with its massive stock of foreign asset reserves. Never mind the fact that the government acquired those reserves from private banks by swapping government liabilities for foreign assets. 

Finally we have the blatant trend extrapolation. China has grown strongly for the last 20 years; hence it will continue to grow strongly. It's growth is "sustainably higher" than that of other countries. If you bet against China in 2001, you were a sucker; hence, if you bet against China now, you are a sucker.

So because China's government cares more about the general populace than about the profits of capitalists, which somehow has to do with the fact that they play Go instead of chess, it will use its foreign asset reserves to bail out loss-making projects that produce positive externalities that raise GDP growth overall. Hence, "This time it's different", and we should view the 20-year trend of Chinese growth as something structural instead of the kind of transitory catch-up phase observed in every other country in history, including Japan, South Korea, and Taiwan. Therefore, the market's expectation that growth will slow - reflected in the only-46% rise in Chinese stocks since 2004 - is seriously wrong.

Got it.

Anyway, fun-poking aside, I have always been struck by the sheer volume of words expended on the question of whether newly industrializing economies will break the Solow Model or not. Really, it's very hard to see how you can break the Solow Model - keep accumulating capital, and your growth will be fast but steadily decreasing as you converge to the rich-world average. Sure, you can fall short of the Solow Model and get stuck in a "middle income trap". Sure, there are questions as to how fast technology can be transferred from richer countries, or whether investment has a maximum "speed" beyond which it becomes more wasteful, or what kind of institutions are optimal. But the idea that growth must slow - gently or otherwise - as a country gets richer should, in my opinion, be the jumping-off point for any predictions about development. 

It's very weird that after all these years, we're still trying to extrapolate countries' futures based on what kind of board games they play.

Inflation for the People

For the past few weeks I've been getting acquainted with the popular wing of the "Austrian economics" movement. First I discovered the Bizarro Economics World of online forums, then I got re-acquainted with Zero Hedge, which seems to have taken a more and more inflationista/goldbuggy/Austrian tone in recent years.  Then a bunch of Texan friends started posting "End the Fed!" memes to my Facebook feed. So I went on Facebook and I asked: "Why do people want to end the Fed?" In response, a friend sent me this video:

In the video, a guy loses his house, and his American Dream is crushed. He is then taken back in time by a guy with a horribly fake African-American accent, to witness the source of his problems. As it turns out, everything is the fault of bankers, who steal people's money through fractional reserve banking. Eventually, banking power is concentrated in the hands of a shadowy cabal headed by the Rothschilds, to whom even J.P. Morgan must kowtow. Thomas Jefferson and Andrew Jackson temporarily hold off the evil bankers here in America, ushering in a huge boom "with real money, backed with real gold." But eventually the bankers get in, and end up forming the Fed, which proceeds to steal people's hard-earned money even more via inflation and collaboration with the IRS. 

Anyway, for now I'll ignore the oddity of anti-semitic video makers latching onto an economic philosophy (Austrianism) invented by a Jewish guy and a movement (the Ron Paul movement) inspired by another Jewish guy. Anti-semitism has always been a bit weird like that. I'll also put aside the thing about fractional reserve banking (a subject for another post). Instead, I'd like to talk about inflation.

The classic simple example of inflation is this: The Fed (or the banking system, giant Rothschild robots, whatever) doubles the money supply. So money becomes only half as valuable as before. Therefore, the price of everything doubles. So:

Price of a gallon of milk:  $4 --> $8

Price of a gallon of gasoline:  $4 --> $8

Price of a new house: $200k --> $400k

..and so on. BUT, inflation also doubles your salary, in exactly the same way:

Salary: $40k --> $80k

However, (unless you own a special kind of bond called TIPS), inflation does not change the size of your bank account:

Your savings: $80k --> $80k

So while prices and your salary double, the number of dollars in your savings account stays the same. This makes you poorer, because you can't buy as much stuff with your savings:

Your savings: 20,000 gallons of gasoline --> 10,000 gallons of gasoline

So inflation steals your money, right? Well, sure. BUT, wait a second. What if you have a mortgage? Suppose you already bought a house, but you haven't paid off your mortgage yet. You have debt! What happens to this debt when inflation happens? Does it go up? Nope! Just like your bank account, it stays the same:

Your mortgage debt: $160k --> $160k

Now remember, your salary went up when inflation happened. So now, it takes you much less work to pay off your mortgage:

Your mortgage debt: 4 years of your salary --> 2 years of your salary

Inflation stole money from you by shrinking your bank account, but it put money in your pocket by shrinking your mortgage debt!! Notice that the way I have the numbers here, your net worth went up, because your mortgage debt was bigger than your bank account. 

So if your net worth goes up and the purchasing power of your income stays the same, as in this simple example, inflation makes you richer. Inflation hurts people who have more savings than debt, and helps people who have more debt than savings.

Who has more savings than debt? Old people and rich people. Who has more debt than savings? Young workers who are paying off mortgages. In other words, the video has it exactly backwards - inflation will not take your house away from you, inflation will prevent your house from being taken away from you. It will save your American Dream. If you don't believe me, go back and look at my example again. It works.

And that hyperinflation that people on Zero Hedge are always screaming about? Well, first of all, it's not coming. But if it did happen, it would mean that your mortgage would be paid up instantly. Really. If our money turned into Monopoly money, you could just pay off your mortgage with a wheelbarrow full of Monopoly money. Perfectly legal!

(Now, you may say "Inflation is still bad, because it punishes saving and rewards reckless borrowing." Well, you're right. That's a danger that the Fed thinks about when they are trying to decide whether to print money in an attempt to boost GDP growth.)

Anyway, what's interesting is that inflation was not always seen as the enemy of the common people. When Thomas Jefferson railed against banks, he worried about deflation as much as inflation. And in the 1800s, the Populist Movement - basically, a bunch of small farmers in the South and West - fought for inflation! At that time, farmers owed a bunch of money to big banks on the East Coast (including J.P. Morgan, who was presumably busy kowtowing to his secret immortal Rothschild masters). Without inflation, they would have to sell their farms to the banks and go be factory workers. So they fought for the United States to go off the gold standard and go on the silver standard - debasing the currency in order to reduce their debts! 

Actually, the big banks defeated the Populist farmers. The gold standard was maintained, inflation was prevented, and a lot of people lost their farms. So maybe the robotic Rothschild octopi had the last laugh after all. But they didn't do it through inflation! Quite the opposite. Time was, inflation and currency debasement were seen as the savior of the common man.

Just remember: Inflation hurts people with savings and helps people with debt. An awful lot of Americans these days fall into that second category. Before you go embracing the hard-money, Austrian, gold-standard stuff, think about which category includes you!

(Then again, why should you believe me? After all, I am a Jew. I could be working for...THEM...dum dum dummmmmm...)

How Zero Hedge makes your money vanish

In 2001, Brad Barber and Terrance Odean published a very famous finance paper called "Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment". The upshot is this:

Theoretical models predict that overconfident investors trade excessively...Psychological research demonstrates that, in areas such as finance, men are more overconfident than women. Thus, theory predicts that men will trade more excessively than women. Using account data for over 35,000 households...We document that men trade 45 percent more than women. Trading reduces men’s net returns by 2.65 percentage points a year[.]

It has been known for quite some time that individual investors - this means you, sitting at your computer clicking away on e*Trade - almost all underperform the market. In other words, you suck at investing. In 1999, Odean found that a lot of this poor performance comes from the fact that individual investors trade too much. In other words, one reason you suck at investing is because you have an itchy trigger finger. And this 2001 Barber and Odean paper found that men have itchier trigger fingers than women.

Which brings me to the website Zero Hedge.

Zero Hedge is a financial news website. The writers all write under the pseudonym of "Tyler Durden", Brad Pitt's character from Fight Club. Each post comes with a little black and white icon of Brad Pitt's head. On Zero Hedge you can read news, rumors, facts, figures, off-the-cuff analysis, and political screeds (usually anti-Obama, anti-government, and pro-hard money). On the sidebars, you can click on ads for online brokerages, gold collectibles, and The Economist. 

The site is a big fat hoax. And if you read it for anything other than amusement, you're almost certainly a big fat sucker.

That's a bold claim! Why do I make this claim? Well, in one sense, all financial news is a hoax. Financial news, by definition, is public information - if you've read it, you can bet that thousands of other people have too. That means that if the market is anywhere close to being efficient, any information in any article you read will already have been incorporated into the price of financial assets. Reading or watching public information should not, in theory, give you any "alpha".

But OK fine, suppose the market is not efficient. Suppose there are some smart people who can interpret the public news really well, and some suckers who take home the wrong message. And also suppose that the suckers stupidly believe that they are the ones taking home the right message, and that the other folks are suckers. In that case, the smart people can make money by reading Zero Hedge, interpreting it correctly, and taking money from the suckers who either didn't read Zero Hedge or who took home the wrong message...right?

Wrong. Because there's another problem here. If the writers of Zero Hedge really knew some information that could allow them to beat the market, why in God's name would they tell it to you? If they had half a brain, they'd just keep the info to themselves, trade on it, and make a profit! Maybe then, after they had made their profit, they'd release the news to the public (and collect ad revenue), but by then the news would be worthless. Financial news sites, you should realize, are not in the business of giving you insider tips out of the goodness of their hearts.

So the only way you can make money by reading Zero Hedge is if you're not only smarter than a bunch of suckers who look and act a lot like you, but if you're smarter than Zero Hedge's writers too. Not gonna happen. The fact that financial news is big business fits perfectly with the mass suckering of America's individual investors documented by Barber & Odean (2001). Financial "news" is noise.

As you might expect, it's not hard to look back at Zero Hedge's predictions and see that a large number of them are junk. For example, here's a bunch of posts from 2009 predicting imminent hyperinflation. Hope you didn't make any trades based on that bit of wisdom! (Note for the sake of fairness: Yes, in terms of giving "hot tips", there are a lot worse sites than Zero Hedge...see Update 3 below.)

So how does Zero Hedge get away with this hoax? Barber & Odean (2001) give a big hint. Tyler Durden, whose name and image grace every Zero Hedge Post, is a symbol of masculinity. More specifically, he is a nerd's imagined vision of what a really masculine nerd would be like. In Fight Club, Durden says: "All the ways you wish you could be, that's me. I look like you wanna look, I f*** like you wanna f***, I am smart, capable, and most importantly, I am free in all the ways that you are not."

In other words, you are a young smart (i.e. nerdy) guy sitting at your computer with rivers of testosterone coursing through your veins. And now here comes Tyler Durden, your generation's Platonic ideal of pure masculinity, telling you that Real Men Take Risks. At the top of the site, there is a Tyler Durden quote: "On a long enough timeline the survival rate for everyone drops to zero." In other words, gamble. Bet that you're the smart guy and not the sucker. Because hey, you're going to die anyway, so there's no use hedging your bets. Zero hedge, right? (Or you can read a sister site called "Testosterone Pit".  Not kidding!)

In other words, "Tyler Durden" knows what Barber & Odean (2001) knew. Men take risks that, on average lose them money. Zero Hedge is a brilliant behavioral-finance technology that uses the predictable regularities of human psychology to extract money from testosterone-addled dupes. The people who run the site are far from idiots; they are geniuses. In fact, I wouldn't be surprised if "Tyler Durden" were actually a bunch of behavioral finance grad students, snickering behind their hands at everyone who takes their site seriously.

Now, dear reader, maybe you are a fan of Zero Hedge. Maybe you have been getting angrier and angrier as you read this post, and maybe even now you have a comment box open and are typing the first words of an angry diatribe: "You're an idiot and you teach at a shit school which does not acknowledge fact or logic which is keystone in finance and econ!!!" (an actual Tweet I received the other day from a self-described drunk undergrad.)

And maybe you're right. Maybe I am an idiot who is blinded to the obvious wisdom dispensed by Zero Hedge, wisdom that would make me rich if I was just smart enough to read the site and man enough to make some big bets. Maybe. But before you hit the "send" button on that comment, take a glance in the mirror. You're young, right? You're a man, right? You trade pretty frequently, right? And you're not a rich, successful hedge fund manager or investment bank trader, are you?

Here's a radical thought: Maybe you're the sucker.


Update: I showed this post to a few friends in the finance industry, and while all of them agreed, one came up with by far the best one-line response: "Retail investor is retail."

Update 2: John Aziz has a rebuttal over at Zero Hedge, in which he says: 1. The rise in the price of gold shows that hedging against hyperinflation has been a good bet, 2. The name "Zero Hedge" actually doesn't mean you shouldn't hedge, it means you should hedge against a total crash of the financial system, and 3. Zero Hedge has done a good job of exposing corruption in the financial system. I don't think I need to respond to point (1). Point (2) is interesting, I hadn't thought of that, but I'm not particularly convinced it's true. And Point (3) is true; good news is a good thing good to read, and journalism that exposes corruption is valuable. But reading news is different than trading on it, which is what I'm talking about in this post.

Update 3: Some people have pointed out that in terms of offering trading tips, there are a lot worse offenders than Zero Hedge. That's certainly true - The Motley Fool and StockTwits come to mind, not to mention Jim Cramer or any show on CNBC. Compared to those sites, Zero Hedge has a lot more news and less tips. I just picked on Zero Hedge because the testosterone thing gave me a perfect opportunity to whip out Barber & Odean. And also because it's become a haven for goldbug/"Austrian"/"hyperinflation-is-coming"/"the Fed is a UN conspiracy to destroy the white race" kind of BS.