Libertarians: Only now, at the end, do you understand...

Charles Koch reveals the true power of the Dark Side...

Given my history of critiquing libertarianism, it would hardly be surprising if I felt a flash of gleeful schadenfreude to see the dismay with which so many movement libertarians are reacting to the Koch takeover of the Cato Institute. But I don't. I just feel sad. Here are a bunch of smart people who truly, honestly believe in their worldview - a worldview that shares some key elements with my own - discovering for the first time that they are in fact merely a proxy army for people who don't take them or their worldview seriously at all.

To those of us outside the movement, the fact that libertarians are a proxy army has always been painfully obvious. The key piece of evidence was always the set of issues that libertarians chose to emphasize. Most Americans share the belief that civil liberties are good, war is to be avoided, and high taxes are bad. But the fact that our country's libertarian movement spent so much time fighting high taxes and so little time fighting the encroaching authoritarianism of conservative presidential administrations was a clear sign that some priorities were seriously out of place. Should we really be more afraid of turning into Sweden than turning into Singapore? The contrast between libertarians' continual jeremiads against taxes and their muted, intermittent criticism of things like warrantless wiretaps, executive detention, and torture was a huge tip-off that the movement was really just some kind of intellectual front for America's right wing.

The thing is, the soldiers in this proxy army didn't seem to realize they were a proxy army. They appeared, and appear, to truly believe in their synthetic ideology; they seemed deeply convinced of the Rand/Nozick idea that taxes and environmental regulation represented a more dire threat to human freedom than the authoritarianism that had been the bane of earlier freedom advocates since Enlightenment. 

Now, however, they are beginning to understand:

[T]he Kochtopus...threatens to swallow up libertarian scholarship in order to regurgitate it as fodder for the social activist tail that seeks to wag the GOP dog in the 2012 elections.Readers should not expect many free market think tanks to speak out against the Koch assault. Too many of them benefit financially from the pocket money doled out by Charles and David Koch through their various well-funded foundations. That pocket money comes at a significant cost. I can assure you that there is no such thing as a free Koch luncheon.

How much did libertarians blind themselves to the true motivations of the people who were throwing money at them? We may never know. But it's certain that the blinders are off now. People working at more explicitly Koch-funded think tanks (such as the Mercatus Center, headed by the econ blogosphere's own Tyler Cowen) must be experiencing some serious cognitive dissonance right about now.

Because the superpower bankrolling America's libertarian movement is simply our version of the right-wing oligarch-and-racist coalition that crops up in every nation from time to time. The American conservative movement wants a strong executive who wields many of the powers of a tyrant of old, in order to "protect" us (from "terrorists," subversive elements at home, or outsiders in general). It wants a justice system oriented toward protecting tradition, group rights, and the social order (like Japan's system). It wants to restrict immigration by nonwhite racial groups. It wants heavy government regulation of personal morality. And it occasionally wants wars of choice.

In other words, it is deeply anti-liberty. 

Some libertarians are belatedly recognizing this:

What does Cato say that no other think tank says? Militarism is… the worst foreign policy for a free market. The War on Drugs is not only unnecessary in a free market, but ending it would be a straightforward implementation of free market principles. And the freedom to buy and sell is a sick joke without robust civil liberties for all. Conversely, most people want their civil liberties partly so that they can earn a living and enjoy economic opportunities. That is what Cato is about. That is also apparently why the Kochs are trying to destroy it.

If I were a meaner-spirited type of person, I would say that this realization is too little, too late - that libertarians spent decades being apologists, water-carriers, and useful idiots for authoritarians, and only now that their masters are reeling in the leash do they suddenly want out. But instead I say: Better late than never. You guys made mistakes before, but now you see the truth. First, realize that the conservative puppeteering of the libertarian movement is not an extremely recent phenomenon, but was always present at some level. And then start thinking about what kind of political agenda and rhetorical emphasis will actually promote liberty in America. 

Freed from the conservative yoke, libertarians will have huge potential to do a lot of real good for this fundamentally libertarian country.

Why macro is hard (Taylor/Krugman edition)

In the latest volley in the Stimulus Wars (giggle), John Taylor explains why he doesn't think the ARRA, commonly called "the stimulus" or "Obama's stimulus", increased GDP:

For the parts of the packages which include temporary tax rebates or temporary tax cuts I find no significant consumption effect using regression analysis and controlling for other factors that affect consumption. If you look at a chart of the tax rebates in 2008, for example, the evidence is striking: There was a big increase in personal disposable income at the time of the rebate, but no similar change in consumption... 

In the case of the 2009 stimulus package, there was also an attempt to increase significantly government purchases of goods and services. But the evidence is that this attempt largely failed. A special satellite account produced by the Bureau of Economic Analysis shows that federal infrastructure investment—at the peak quarter—increased by only .05 percent of GDP as a result of the stimulus and federal government consumption by only .14 percent... 

While state and local governments received substantial grants under the 2009 stimulus, a statistical analysis by John Cogan and me shows that they did not use these grants to increase their purchases of goods and services as many had predicted. Instead they reduced net borrowing and increased transfer payments.

It's important to realize, as Mark Thoma explains, that Taylor is not saying that stimulus can't work theoretically, but that the specific "stimulus" enacted in the ARRA didn't work.

Tayor makes three separate arguments here. The first is that the tax rebates in the ARRA failed to increase household consumption. This doesn't seem like an incredibly controversial argument, since A) there exist decent ways to forecast what consumption would have been in the absence of the ARRA, and B) Keynesian theories predict that transfer payments make for much worse stimulus than government expenditure. But note that one of those things that forecasts consumption is wealth; when the ARRA was passed, household wealth was plummeting due to the collapse in housing prices, and thus it is possible that the ARRA stopped consumption from collapsing.

Taylor's second argument is that the ARRA failed to increase federal government purchases - i.e., "true" federal stimulus - by much. This definitely matches the evidence gathered by Paul Krugman in a recent series of posts (see here, here, here, and here) showing that total U.S. government expenditure fell during the recent recession. So, not much argument here. 

Finally, Taylor claims that the ARRA did not do much to change government purchases at the state level. This is very hard to prove, since we don't have a good model of state government behavior. Paul Krugman claims that without the ARRA, state expenditure would have fallen even more, because states would have run up against borrowing constraints. Taylor obviously doesn't think that would have happened.

Who's right? It's hard to know, and this really illustrates the main difficulty in doing macroeconomics: lack of a good "counterfactual." History only happens once; it's just damn hard to tell how things would have changed if people had made different decisions, just by looking at what ended up actually happening.

The only reliable way to get a "counterfactual" - and, thus, the only way to tell if a policy worked - is to have a good theoretical model of how policies work. But one of the things required for a model to be "good" is for it to be tested against data. And all too often, the only data we have to test our macro theories is...you guessed it...one single run of history. And that run of history is pretty short - less than a century of quarterly aggregate data on a small handful of variables.

Now, if we could believe in cross-country comparisons, then we could basically have several simultaneous "runs" of history to compare, and this would help us be more certain about our models. But cross-country comparisons are notoriously hard to do, and countries' economies are also dependent on each other to some degree.

This is why, in my opinion, the only way to build really believable and reliable models is to use microfoundations (or, if the macroeconomy is big enough to display emergent properties, to use agent-based modeling). Without those techniques, macro is doomed to be a lot more like history than science. 

Which is not to say macro is doomed! History is not a useless way of understanding the world. Observing and recording events and then making reasonable speculations as to their causes - e.g., saying "I think states would have run up against borrowing constraints without ARRA" - is not a useless endeavor by any means. For thousands of years of human civilization - up until Francis Bacon & co. - that was the only way we had of understanding the world around us, and it did lead to a slow steady increase in human knowledge. It's just that history is a less powerful way of understanding the world than science. If we could make macro a science, that would be awesome.

In the meantime, regarding the current debate, you may ask: Who do I believe, Taylor or Krugman? I guess I mostly side with Taylor here. Krugman is probably right that if there had been no ARRA, states would have spent even less as they bumped up against borrowing constraints. But the states still would have saved a portion of the ARRA money (or passed it on to households as tax rebates or transfers), substantially reducing the ARRA's fiscal multiplier. I believe that direct federal government purchases - increased infrastructure investment - makes for by far the best stimulus. The fact that there was almost none of this in the ARRA was a major, major policy failure. I think that that failure overshadows the small benefit that the ARRA might have had in supporting state purchases. And I think that that is a point on which Krugman and other stimulus advocates would broadly agree.

Why bother with microfoundations?

Simon Wren-Lewis, Paul Krugman, and Robert Waldmann have posts up discussing whether macroeconomists always need to use "microfoundations" - in other words, whether macro models should always start with some sort of individual optimization. I'll get to their ideas in a minute, but first, some general thoughts.

Why are microfoundations useful? The usual answer is that "microfoundations make models immune to the Lucas Critique." The idea is that the rules of individual behavior don't change when policy changes, so basing our models purely on the rules of individual behavior will allow us to predict the effects of government policies. Actually, I'm not sure this really works. For example, most microfounded models rely on utility functions with constant parameters - these are the "tastes" that Bob Lucas and other founders of moden macro believed to be fundamental and unchanging. But I'd be willing to bet that different macro policies can change people's risk aversion. If that's the case, then using microfoundations doesn't really answer the Lucas Critique.

A better reason to use microfoundations, in my opinion, is that they probably lead to better models. "Better," of course, means "more useful for predicting the future." If our models predict future aggregate macro variables (GDP, etc.) based solely on the past values of those variables, we'll almost certainly be using less information than is available; if we figure out how economic actors are making their decisions, we will have a lot more information. More information = better model. And there are all kinds of ways to observe and model individual behavior - survey data, lab experiments, etc.

So why would we want to use models that don't have microfoundations? Here is Simon Wren-Lewis' answer:

[S]uppose there is in fact more than one valid microfoundation for a particular aggregate model. In other words, there is not just one, but perhaps a variety of particular worlds which would lead to this set of aggregate macro relationships. (We could use an analogy, and say that these microfoundations were observationally equivalent in aggregate terms.) Furthermore, suppose that more than one of these particular worlds was a reasonable representation of reality. (Among this set of worlds, we cannot claim that one particular model represents the real world and the others do not.) It would seem to me that in this case the aggregate model derived from these different worlds has some utility beyond just one of these microfounded models. It is robust to alternative microfoundations. 

In these circumstances, it would seem sensible to go straight to the aggregate model, and ignore microfoundations...

I don't really like this answer. Presumably, there is some set of sets of microfoundations that leads to Aggregate Relationship A and some other set of sets of microfoundations that leads to Aggregate Relationship B. How do you choose which set is better? Well, you could look at survey data and lab experiments to figure out which microfoundations are really in effect. But if you can do that, why do you care about "robustness to alternative microfoundations" in the first place? And if you can't choose which microfoundations are better, why does "robustness to alternatives" matter?

Pretty much any model, in economics or physics or whatever, has a bunch of possible microfoundations that could give rise to it. That fact alone does not make microfoundations less important, since presumably some microfoundations are actually happening, and others aren't!

Here are Paul Krugman's answers to why we might not need microfoundations:

1. Even in microeconomics, we don’t insist on using models built up from maximizing behavior all the time. Exhibit A: supply and demand!...

2. Relatedly, as a practical matter intellectual scratch-pads — approximate version of what we really believe, but stripped down to be tractable — are what one uses for applied economic analysis all the time.If I want to ask what the effects of some shock will be, it rarely makes sense to demand that the analysis always go all the way back to the intertemporal choices of optimizing agents. 

Hmm. I think that incorporating microfoundations into a model is different than starting from microfoundations when applying that model.

3. In the hard sciences, when dealing with complex systems people have often used higher-level, aggregative concepts that seem to work empirically long before they have a full derivation of effects from the underlying laws of physics...Why, then, do some economists think that concepts like the IS curve or the multiplier are illegitimate because they aren’t necessarily grounded in optimization from the ground up?

I think this is where the Lucas Critique comes in. The Phillips Curve is the famous example of why aggregate relationships might not be useful without understanding the microfoundations. That doesn't make aggregate-only models useless, but it should make people cautious about using them.

4. And when making such comparisons between economics and physical science, there’s yet another point: what we call “microfoundations” are not like physical laws. Heck, they’re not even true. Maximizing consumers are just a metaphor, possibly useful in making sense of behavior, but possibly not. The metaphors we use for microfoundations have no claim to be regarded as representing a higher order of truth than the ad hoc aggregate metaphors we use in IS-LM or whatever; in fact, we have much more supportive evidence for Keynesian macro than we do for standard micro.

I think that this is the real argument against microfoundations as they are currently used in macro. Basically, Krugman is saying that the "microfoundations" we now use really deserve to have quotes around them, because they actually don't describe individual behavior.

In other words, our current microfoundations are mostly just garbage.

If this is true - and I think that the evidence overwhelmingly says that it is! - it means that our modern "microfounded" macro models are no more useful than aggregate-only models. The logic should be obvious. Using wrong descriptions of how people behave may or may not yield aggregate relationships that really do describe the economy. But the presence of the incorrect microfoundations will not give the aggregate results a leg up over models that simply started with the aggregates.

In other words, if you put garbage in, you may or may not get garbage out, but why bother putting the garbage in in the first place?

(Note: if you started to angrily type out the reply "But all models are wrong!", please refer to my 2nd Principle for Arguing With Economists. You are wrong.)

When I look at the macro models that have been constructed since Lucas first published his critique in the 1970s, I see a whole bunch of microfoundations that would be rejected by any sort of empirical or experimental evidence (on the RBC side as well as the Neo-Keynesian side). In other words, I see a bunch of crappy models of individual human behavior being tossed into macro models. This has basically convinced me that the "microfounded" DSGE models we now use are only occasionally superior to aggregate-only models. Macroeconomists seem to have basically nodded in the direction of the Lucas critique and in the direction of microeconomics as a whole, and then done one of two things: either A) gone right on using aggregate models, while writing down some "microfoundations" to please journal editors, or B) drawn policy recommendations directly from incorrect models of individual behavior.

Brad DeLong puts this rather more pithily:

I now have the most bizarre image in my mind: 

A seminar at the Library of Alexandria in 300 A.D., with an astronomer trying to provide micro foundations in the form of calculations of how large their wings must be and how fast their wings must beat for the angels to push the planets on their tracks through the quintessential spheres…

Thus it seems to me that the microfoundations revolution has not really gotten us very far yet. I would be willing, of course, to be convinced otherwise.

So what to do? The answer is clear: macroeconomists should continue using aggregate relationships for now, and try to check these against the best microfoundations available. But in the meantime, recognize that these aggregate models will have severe limitations until microeconomists come up with better explanations of individual behavior. Which, of course, they are working on.

But note that there is also a political danger here. Macroeconomists who desire a certain policy conclusion - for example, that fiscal stimulus never works - may be tempted to continue to use bad microfoundations that support that conclusion, even when microeconomists have found something better. This is sometheing the profession should work to avoid, by actively recognizing that microfoundations that fit the micro data are inherently preferable to those that do not.


Update: Paul Krugman, commenting, has a good point about what kind of predictions we should expect from economic models. Big qualitative predictions ("quantitative easing will cause runaway inflation") are more important than precise quantitative ones ("GDP growth will be 1.7% next quarter"). I agree, of course. When I say models should "predict the future," this is really what I mean.

Update 2: Richard Serlin points out that aggregation is a huge challenge for microfounded models, since complex systems often have chaotic properties. Very true. But that doesn't mean that just observing the aggregate will give you more information! What you need to handle complexity and chaos is a ton of computing power and some agent-based modeling, as is done in weather forecasting. This can provide a very important check on non-agent-based models that make simplifying assumptions in order to aggregate individual agents.

Update 3: Peter Dorman is of the opinion that the reason our current microfoundations are crappy is that the entire framework by which microeconomics is now done - equilibrium analysis and optimizing behavior - does not describe reality. I'm not willing to go that far (and besides, what about game theory?), but if he's right, it would certainly strengthen my case substantially.

Update 4: Andrew Gelman and Peter Dorman are basically on the "our current microfoundations suck, and we should get better ones" bandwagon.

A sketch of a model of higher education

I've had a model of higher education rolling around in my head for quite a while now, and I never had the time or energy to put it on paper. But then I read this post by Frances Woolley, which contains some ideas that are extremely similar to mine, so I thought I'd sketch out the basic idea of the model in a blog post.

Woolley asks why research is more important for a professor's career than teaching:

[W]ithin academia, research has higher status than teaching. The question is, why?... 

Perhaps research is highly valued because it is in short supply...But scarcity cannot explain why dime-a-dozen mediocre researchers are accorded higher status than excellent teachers...[and e]ven a scarce commodity will have a low price if there is not much demand for it... 

I think [research has higher status than teaching] is because research output is a signal of ability...Teaching just does not work as a signal in the same way. First, top rate teaching is extraordinarily difficult to measure... 

Second, I don't know if teaching performance is as highly correlated with intelligence, creativity, and originality as research performance is.

Basically, Woolley conjectures that research is valuable as a signal of unobservable teaching skill. I think that this is an excellent answer, for a reason that Woolley doesn't even mention: past research is paid more than future research.

Think about Joe Stiglitz' salary. Stiglitz has, by almost any measure imaginable, done a huge amount of great research. And he gets paid a very high salary. But how much great research do we expect Stiglitz to do in the future? He's old! And he's involved in other things, like speaking, getting involved in policy debates, etc. He is not really getting paid to do research. And, crucially, Stiglitz is getting paid a lot more than any economist whose best research years are ahead of her! If you look at total salary expenditure by universities, my bet is that you will find the same pattern - much more money being spent on past research than on future research.

Of course, from the labor supply side, this still functions as an incentive to do good research (so you can get paid more in the future). But from a demand side perspective, why the heck should a university pay professors for work they did in the past, when they were employed somewhere else? Unless universities are voluntarily internalizing the positive externality from research - i.e. unless universities just want to do good for the world by making research a well-rewarded activity - we must conclude that universities are not actually paying for research.

What are they paying for? I conjecture that they are paying for prestige. If Joe Stiglitz works at my university, it raises my university's prestige.

Why would a university want to raise its prestige? Well, if Woolley's conjecture is correct, prestige is a signal of teaching quality: a Columbia education is generally assumed to be better than an Ohio State education, in part because Columbia has more prestigious professors. So suppose that human capital is very important, but also difficult to observe; in this case, the prestige of your alma mater signals how valuable an employee you're likely to be, because of the fact that education matters, not in spite of it (as in the typical "signaling model" of education).

So here's a question: Why would universities care about prestige? Well, it might allow them to charge undergrads higher prices; Columbia tuition is certainly higher than Ohio State tuition. That in turn might lead to administrators (i.e. the people who make the hiring decisions) getting paid more, particularly if the number of administrators needed is proportional to the number of undergrads (so that higher tuition means higher expenditure-per-undergrad means more expenditure-per-administrator). Administrators trying to maximize their own salaries would then have an incentive to pay a lot of money for someone like Joe Stiglitz. (Full disclosure: I like and admire Joe Stiglitz. And naturally I can't pass up a chance to bag on Ohio State.)

So, to reiterate, here is a sketch of the Noah Smith (or perhaps Smith/Woolley?) Model of Higher Education:

1. The human capital benefit of an undergraduate education is highly sensitive to unobservable differences in teacher quality.

2. Past research accomplishments are a strong signal of teacher quality.

3. Thus, professors with stronger research records allow a university to charge more tuition per undergrad, increasing the salaries of administrators.

4. Although human capital signaling is itself inefficient, this system benefits society by providing a subsidy for the production of research, which as an almost completely nonrival good, is underprovided by the private sector. If teaching quality were observable, this research subsidy would not exist.

5. Extension: America's "legacy student" system (basically, auctioning off a few false ability signals for huge amounts of money, at only a small cost to the school's prestige) gives American universities an edge over foreign universities in terms of prestige, but it also increases the amount that America's university system subsidizes research. This may mean that the legacy system is good for the world.

Anyone can, of course, feel free to take this model and run with it if you like it (and if you do, feel free to include me as a co-author, or not, as you like). It definitely needs some serious empirical work to support each link in the chain. But notice that this model wouldn't just answer the question of why research is paid highly, it would (partially) answer the question of the value of universities to society as a whole, AND the question of why the dual research/teaching structure of the university has proven so durable over the years. And it would possibly point to ways in which the system could be tweaked to boost the degree to which it subsidizes basic research (some of these ways might seem very counterintuitive, e.g. admitting legacy students!).

And if you can see reasons why this model I've sketched is obviously wrong, please let me know, of course.

I care about rich people, but not about their riches.

An incredibly interesting article in Bloomberg this week reveals the devastating power of the economic phenomenon known as "habit formation." Basically, if you have enjoyed a high-consumption lifestyle in the past, it is extremely painful to switch to a lifestyle of moderate consumption. This is why so many finance-sector workers are unhappy right now, despite being so much richer than the vast majority of Americans - they are doing great relative to others, but not so good relative to the astronomical heights they reached in the early 2000s.

Conservatives sometimes use habit formation to try to get sympathy for the rich. When we redistribute income, they say, we have to realize that the rich are actually hurt, since they have gotten used to consuming a certain amount, and raising their taxes will bump them down to a less lavish lifestyle. We should care about the rich people too, they say. But most conservatives don't say this too loudly, because the instincts of the public are more Rawlsian than Benthamite; people are likely to respond by saying "Oh boo hoo hoo, no vacation in Aspen this year! Cry me a river!"

In other words, I think most people don't really care that much about the happiness of the rich. 

But I do.

I do care about the happiness of the rich. Why? Because most people I know are rich. Not in the vacation-in-Aspen sense, but compared to the vast majority of people on planet Earth. My family and friends are all in the top 50% of household income in the U.S., which makes them some of the richest people in the world. And I care about my family and friends. So I care about rich people. And because I realize this, when I see the vacation-in-Aspen crowd feeling bad, I have sympathy for them. I don't like to see those people sad.

Now, this doesn't mean I'm worried about raising taxes on the rich. Yes, habit formation will make higher taxes more of a blow, but let's face it - there is just not enough wealth in the world to keep the consumption habits of America's rich people growing at the rate they grew in the 2000s. A correction will be painful, but one way or another it is inevitable.

And to be honest, all this discussion of income and wealth seems to me to be a bit beside the point. We live in a world of incomplete markets, where the things that rich people really care about - the things upon which most of their happiness hinges - are not things that can be bought with money (or else they would have bought them). These are things like good friends, a feeling of accomplishment, a positive outlook, personal interests, individual expression, and the feeling of being a good person.

One especially important area, I should point out, is family. Research indicates that divorce, for example, results in a huge and long-lasting decline in happiness. Rich people can buy vacations in Aspen, but as things stand, no amount of money will keep their families together. In fact, higher divorce rates in rich countries may explain a large part of the Easterlin Paradox (the finding that people in rich countries aren't much happier than people in poor countries).

I am somewhat of a believer in Maslow's Hierarchy of Needs. Money can buy you security, and it can get you respect, but as things stand it still can't buy you love. And so when I worry about the problems of rich people, I worry much more about their personal emotional issues than their consumption habits.

Rich folks, you don't need a giant bonus. You don't need lower taxes. You don't need another vacation in Aspen. What you need is a hug.

Thursday Roundup (3/1/2012)

Blogging's been sparse lately. Here's some Thursday links to feed your Noahddiction:

1. Barry Ritzholtz reminds us that liquidity in financial markets can actually be a bad thing. On a somewhat related note, John Cochrane points out that high-frequency trading is a zero-sum game.

2. Steve Williamson explains the two main ways that financial crises fit into economic models: A) as amplifiers of shocks, or B) as generators of multiple equilibria. Naturally, both things could be important at the same time.

3. Steve Randy Waldmann reminds us that unit labor costs have been falling, not rising, in most rich countries, including almost all European countries, and in particular have been converging across Europe (as basic theory would predict). This weakens the argument of those who claim that high union-driven labor costs are the cause of Europe's woes. It also provides support for the notion of factor price equalization - the thesis that the stagnation in rich-world incomes is due to the massive China Labor Dumb of 2000-present.

4. Steve Williamson gives some arguments in favor of the auto bailouts.

5. The Incidental Economist reminds us of an important fact from first-year microeconomics: Although the First Welfare Theorem says that (under certain conditions) free markets are efficient, the Second Welfare Theorem says that (under only slightly more restrictive conditions), redistribution is efficient too! So before you use the Welfare Theorems as a justification for free markets, remember that. (H/T Thoma).

6. Menzie Chinn points out that government purchases - i.e., the government actually spending money on stuff - have been going down, not up. What has been going up are transfer payments. Attention conservatives: "Shift resources from transfers to public investment" is a better position than "Cut taxes no matter what."

7. Simon Wren-Lewis argues that even if the macroeconomy does not contain emergent properties, aggregate models with no microfoundations can be preferable to models with microfoundations, since different sets of microfoundations can produce the same aggregate behavior. But he admits that chucking microfoundations is a very dangerous thing to do, and I agree.

8. Here's Wren-Lewis again, with an explanation of why the "LM" in "IS-LM" should be ditched. The argument is probably familiar to anyone who has taken an intro grad macro class.

9. Karl Smith claims that the rich-world investment drought is not about "stagnation," it's about "deindustrialization." Tyler Cowen politely coughs his throat and points out that those are, um, the same thing. Cowen win.

10. Finally, here is an absolutely righteous takedown of Jagdish Bhagwati by the perspicacious Peter Dorman, who is rapidly usurping my rightful place as the sheriff of the econ blogosphere. He's out-pinioning Noahpinion!

What is opportunity? What is a "land of opportunity"?

A reader emailed me recently and wanted to discuss the issue of whether America is a "land of opportunity." Like many people, he was of the opinion that America's low intergenerational income mobility means that we're not the land of opportunity that many Americans think we are. He went on to speculate as to why Americans labor under this delusion.

But is it a delusion? As I see it, the question has as much to do with philosophy and semantics as it does with actual economics. It all hinges on what "opportunity" means, and what it means for a country to be a "land of opportunity."


Thought Experiment 1:

As a thought exercise, consider a world in which ability is 100% heritable; if your parents have abilities of 57 and 73, you will have an ability of 65, with absolute certainty. And suppose that in this world, income is purely a linear function of ability (I=kA, where k is a positive constant).

In this world, your income will just be the average of your parents' incomes. Intergenerational mobility, as measured by the likelihood of being in a different income class than your parents, will be zero at the individual level, and at the household level will be determined entirely by who you marry.

Is this world a "land of opportunity"? It's a pure meritocracy, after all. But no one's income differs from that of their parents.


Thought Experiment 2:

As another thought experiment, imagine a world in which income is determined entirely by luck. When you are born, a die is rolled that determines your lifetime income. In this world, therefore, no one's income will be correlated with their parents' income at all. There will be a very large degree of observed intergenerational mobility.

Is this a land of opportunity? You have the chance to be in a different income class than your parents, and your chance is just as good as anyone else's chance. But that's all it is - chance. You are completely at the mercy of the dice.


You may feel that one of these hypothetical worlds is a "land of opportunity," but I think you'll be in the minority. What about a combination of the two? If income were a weighted sum of inherited ability and pure luck, would that make us a "land of opportunity"? Would it depend on the weights?

I doubt many people would say "Yes." The reason is that inherited ability is just another kind of luck. If income is the sum of luck and more luck, it still probably doesn't feel like a land of opportunity. Luck just doesn't feel like opportunity to most people; that's almost certainly why low intergenerational mobility bothers people in the first place (because it implies your income is determined by the luck of your birth).

So what do we feel does constitute "opportunity"? I'm not sure, but after thinking about it, I think it has to do with a third variable: effort. I conjecture that we think of a "land of opportunity" as being "a place where hard work allows you to succeed."

If I'm right, this means that we value opportunity because it fits with a certain moral model that we have regarding rewards, punishments, and free will. We view humans as being able to freely decide how much effort to put forth; hard work is a choice. And we think society should be setup so as to reward hard work and punish laziness - to change the incentive structure of people's effort decisions so as to maximize the effort that they choose to put out.

Furthermore, I don't think we want this kind of society because of a concern for economic efficiency. Leisure is fun! Rewarding hard work does not necessarily increase utility, even if it increases production. Instead, I think that our desire to reward hard work and punish slot comes from a moral value judgment that work is good in and of itself.

This, I am guessing, is why the idea of a low-mobility society bothers people. If your parents' income determines your income, it means that working hard does not bring the rewards that it ought to bring. The people who are most concerned about low mobility are not, then, socialist types who want to redistribute outcomes; they are old-fashioned moralizers who want people to reward people for working hard. They are angry that Horatio Alger stories to rarely come true.

What does this mean for the debate about opportunity? Well, much of the pushback against the "low mobility" complaints has come from libertarian and conservative circles. But this fight may not be about differences in values - libertarianism vs. socialism - as much as it is a debate about facts on the ground. Libertarians and conservatives who claim that America is still a "land of opportunity" are saying that it's still the case that hard work still gets rewarded here, as it ought to be; and those who complain about intergenerational immobility are claiming that hard work isn't as rewarded these days as it ought to be. So I think both sides should realize that this is what they're arguing about.

As a final note, what about my own values? Well, I was raised to think that "equality of opportunity" was a good and desirable thing. But until today, I didn't really realize what that meant. And now I'm starting to question my own values! Do I really care about how much society rewards hard work for hard work's sake? I'm not sure if I do. Maybe there are other kinds of equality that I care about more...but that is a subject for another post...

Economists, release your inner nerd!

Time for a lighter post, on the culture of economics. (Warning: this post is tongue-in-cheek.)

Every field of study has its own peculiar conventions of how "very smart people" are supposed to act. If you're in physics, you should have unkempt hair, juggle or do some other unusual hobby, be sexually promiscuous, and try to make your intellectual achievements look effortless (i.e. you should do a Feynman or Einstein impression). If you're in computer science you should talk like a character out of a Neal Stephenson novel, use hacker jargon, and know every XKCD joke by heart. If you're in math, you should either act genuinely crazy (like Grothendieck or Perelman) or very soft-spoken and mild (like Fefferman or Tao) - i.e., you should convey the impression that you have a lot of very powerful software running in the background of your brain. I'm not sure what biologists act like, but it seems to include wearing a goofy grin all the time.

But all of these disciplines share some common markers of nerdiness. One of these is enthusiasm for new technologies. Another is a love of - appropriately enough - science fiction. the "gee whiz" attitude is an integral part of being a science nerd.

Economics is different. Very few economists read a lot of sci-fi (or, at least, are reluctant to admit it openly). At social gatherings, economists tend to discuss sports, politics, and money rather than augmented reality or driverless cars. They tend to be better-dressed and better-coiffed than natural scientists. In other words, they are a little bit like MBAs. In this way, economists show that they are about Business; that they are worthy of consulting gigs and Congressional testimony.

But economists can't be just like MBAs; they have to also show that they are Smart as well as Business. But if you can't show you're smart by quoting Vernor Vinge and gushing about Augmented Reality glasses,  what are you supposed to do?

Answer: Act like you have mild Asperger's. Don't pump people's hands when you encounter them at the AEA Meeting; stand there awkwardly as if you're not sure whether to shake their hand or not (then finally shake it). When discussing economics, pepper your explanations with excess formalism: instead of saying "figure out how the business cycle works," say "examine the joint time series properties of macroeconomic variables." Instead of "mean and variance," say "moments of interest." Talk...very...slowly...with...random...long... ... ... ...pauses. Drop the occasional politically incorrect comment, and then pretend you didn't realize you did it and apologize sincerely. If all else fails, start wearing Hawaiian shirts and make your hairstyle a combination of fauxhawk and mullet (OK, just kidding, don't do that).

But for Milton Friedman's sake, don't be a science nerd! If someone references Star Trek or Mass Effect, give them the hairy eyeball. Remember, "technology" has nothing to do with semiconductors or machine learning or General Relativity; "technology" is the Solow Residual! Economists evaluate technology from on high; we do not descend into the muck of knowing how it actually works. And remember: you do math well, but you don't like math, not for it's own sake. Do not gush about all the cool difficult math you just did, whine about it.

To sum up, let your social awkwardness indicate that you are a Smart Person, but let your self-consciously normal personality and appearance indicate that you are also Business.

OK, OK, I joke. But man, I wish that the economics profession would let loose its inner geek once in a while! Face it economists: you do math, stats, and coding for a living. It's too late; you are a nerd. You are not a backslapping deal-making executive or a trash-talking caffeine-guzzling Goldman Sachs trader. You are a scientist of some sort, or at least you should be. Embrace it! Watch Fringe. Grow your hair out. Learn to juggle.

Actually, I see some encouraging signs of a move toward nerdiness in the econ profession. Bloggers like Tyler Cowen, Karl Smith, and Robin Hanson are beginning to gush about the Next Big Thing in technology (while Brad DeLong regularly quotes all my favorite sci-fi authors). When he taught my macro field class, Miles Kimball kicked it off by talking about how in 100 years we'd all be cyborgs (a more optimistic variation on Keynes' quote about the Long Run).

Think about it. We are living in the Age of the Nerd. Economics is one of the last places where being a geek is not yet sexy. But the times, they are a-changin'. Live long and prosper.

Thursday Roundup (2/23/2012)

Almost forgot it was Thursday!

Hmm, some of my comments on these links are getting rather long. What do y'all think...should I start doing a bunch of mini-posts, or stick to long infrequent posts?

1. Barry Ritzholtz explains why trying to be an active investor without a supercomputer and a staff of trained finance professionals is a mug's game. Remember: Friends don't let friends day-trade.

2. Simon Wren-Lewis says what few are willing to say publicly: that macroeconomic arguments are really thinly veiled arguments about income redistribution. Is he right? I don't know. I'm a cynic, so I'll say "probably, a lot of the time."

3. Felix Salmon points out that tying company's fortunes to their short-term stock performance tends to destroy them. Very true. Remember that stock prices display excess volatility. Tying a company's fate (or the pay of an executive) directly to its stock price introduces a bunch of noise into the outcome of who lives and who dies (or who gets paid a lot and who gets paid a little).

4. Mark Thoma excerpts some recent NBER research on the importance of manufacturing. Interesting stuff. However, don't expect this to convince Ryan Avent.

5.  Steve Williamson flags and summarizes some very interesting research on chained transactions. I've been thinking about this in the context of counterparty risk for a long time, and I'm considering getting in on this research program.

6. Two Paul Krugman posts remind us why our intuition says fiscal stimulus works: A) GDP often seems to go up during wars, even though the things being produced are mostly destroyed, and B) cutting spending seems to deepen recessions. These are two big reasons that macroeconomists keep trying to make models in which stimulus works, even though the profession has not made it easy for them to make those models.

7. Brad DeLong flags a Larry Ball paper on hysteresis and unemployment. Very worrying stuff.

8.  Econ debate of the week: Arnold Kling says that if the output gap is negative, we should be seeing deflation. Ryan Avent says that no, low inflation by itself is enough to indicate a negative output gap. I tentatively score this debate Kling 1, Avent 1, which is just to say that I don't think anyone understands inflation dynamics well enough to know who's right here. If hysteresis is having a big effect, it helps Kling's argument.

9. Scott Sumner has a great post on why we should increase immigration from China.

10. Nick Rowe thinks we should stop thinking about money as a store of value, since all goods can be stores of value. But I think this is not right...since money can (under normal circumstances) be used to buy any good, we can expect the price of money to be less volatile than the price of any good. As a store of value, money gives you a real rate of return of -inflation (unless people ditch the currency for outside money), while any consumption good gives you a real return of -depreciation + change in relative price. Hence, I feel like money has special value as a passive investment vehicle...

11. And last but not least, Maria Popova discusses how to think like a scientist. Everyone should read this article, especially economists...

Are macroeconomic methods politically biased?

In a recent post, Steve Williamson writes:

The tools of modern macroeconomics are no more the tools of right-wingers than of left-wingers. These are not Republican tools, Libertarian tools, Democratic tools, or whatever. These are the tools of Economic Science[.]

I've thought about this for a long time, and I'm not sure that Steve is right. I think there is a case to be made that the methodology of modern macroeconomics has the effect of biasing the field toward conservative policy recommendations.

Let me explain why.

Modern models of the business cycle generally rely on one of two techniques: 1) Dynamic stochastic general equilibrium models (DSGEs), or 2) Structural vector autoregressions (SVARs). The former is by far the more popular and well-accepted (although Chris Sims won the Nobel Prize for inventing the latter), so when I talk about "the methodology of modern macro," I'm going to talk about DSGEs.

One of the main features of DSGEs is that they are microfounded; that is, they try to explain macroeconomic phenomena in terms of individual decisions. Another feature is that they are based on optimization, which means that the individual decisions are modeled using the calculus of variations.

Explaining macro phenomena based on individual optimization is hard to do. Individuals may take many things into account when making their decisions; in math terms, this means you can easily have a large "state space." Also, the thing that people optimize (their "objective function") may be very complicated; in principle, it can include all manner of weird things like non-rational expectations, learning, dynamic inconsistency, habits, overconfidence, reference points and framing effects, etc. Finally, aggregating a whole bunch of individual decisions into one giant macroeconomic outcome is in principle a very hard thing to do; it's even harder if you include things like firms and governments.

So, unsurprisingly, making a DSGE model is a lot easier if you make some simplifying assumptions. Here are some simplifying assumptions that make a DSGE pretty easy to solve:

1. The assumption that the economy can be modeled with a representative agent; in other words, that the macroeconomy behaves as if there's only one person in it.

2. The assumption that government doesn't exist, or exists only to transfer income from one person to another.

3. The assumption that prices are fully flexible.

4. The assumption that firms are simple profit-maximizers and make zero profits in equilibrium.

5. The assumption that individuals have rational expectations.

6. The assumption that risk preferences can be entirely modeled using people's utility of consumption, and that this utility can be modeled using a small number of parameters that do not change over time.

7. The assumption that labor markets clear.

8. The assumption that "technology" is represented by the Solow residual, and that technology is exogenous and evolves according to a simple time-series process (for example, an AR(1)).

9. The assumption that the business cycles we observe represent small enough fluctuations that the model that describes them can be linearized around its steady state.

If you make all of these simplifying assumptions (and a few more), you end up with something like the first DSGE business-cycle model: the "Real Business Cycle" model of Edward Prescott and Finn Kydland, first published in 1982. This model, and the approach it pioneered, won a Nobel Prize for its authors.

Now, if the above assumptions seem unrealistic to you, that's because they are! And if you think that this makes the RBC model unlikely to fit the data, well, you're right. It doesn't.

(Side note: If Kydland and Prescott's model didn't fit the data, then you may ask, why was it awarded with a Nobel Prize? The answer is "nobody knows the mind of the Nobel Prize committee," but it is probably because this model was the first business-cycle model to try to answer the Lucas critique. The Lucas critique says that models should only contain "deep structural" parameters - i.e., parameters that won't change when government policy changes. Kydland and Prescott's model bases everything on "tastes" and "technology," which most economists at the time - and many even now - were willing to accept as "structural." Thus, it seemed to many people at the time that Kydland and Prescott had invented a modeling approach that had a good shot at one day explaining the business cycle in a way that wouldn't change when policy changed. Many macroeconomists still believe this, as evidenced by the dominance of the DSGE modeling approach in the macro literature.)

So, the DSGE model that is easiest to make (RBC) doesn't do a great job of describing the business cycle, much less predicting it. What would be better?

Fast-forward to 2007, and have a look at the Smets-Wouters model of the business cycle. This "New Keynesian" model is currently considered the "best" DSGE model in terms of forecasting performance. Which is to say, it performs ever so slightly better than the judgment-based forecasts of well-informed individuals. Consequently, some variant of the Smets-Wouters model is used by most central banks as their DSGE model of choice (which they use as a complement to other types of models, such as SVARs, reduced-form models, and judgment-based forecasts). Of course, the fact that Smets-Wouters is the "best" DSGE model does not mean it is very "good." Its forecasts are basically useless more than one quarter into the future.

Of course, this slight improvement on the original Kydland-Prescott model comes at a high cost in terms of the complexity of the model. Instead of one or two "shocks" (exogenous factors that are postulated to drive the business cycle), Smets-Wouters has seven. And there is a lot of doubt that all of these shocks are "structural" in the sense of the Lucas critique - in other words, there seems to be a pretty big chance that the parameters of the Smets-Wouters model would change if policymakers changed their policies (thus begging the question of why Smets and Wouters bothered to use a microfounded DSGE modeling approach in the first place).

Now realize this: It took 25 years to go from Kydland-Prescott's RBC model to Smets-Wouters. That is comparable to the time it took physicists to develop quantum mechanics.

And yet, despite being so complex, and despite making heroic assumptions about the "structural-ness" of certain parameters, and despite being 25 years in the making, the Smets-Wouters model does not come even close to capturing all of the "frictions" that people believe are at work in the macroeconomy. It does not include the financial frictions that many people believe caused the 2008 financial crisis. It does not include behavioral effects like habit formation, hyperbolic discounting, etc. It does not include learning. It does not include limited enforcement of debt contracts. It does not include hysteresis in labor markets. It does not include income or wealth heterogeneity among households or firms. And this is not even close to an exhaustive list of the relevant things that it doesn't include. To include all those things in one model is prohibitively difficult with current technology; the state space of the model explodes, and you would need a supercomputer to solve it if it could be solved at all.

So what this illustrates is that it's really hard to make a DSGE model with even a few sort-of semi-realistic features. As a result, it's really hard to make a DSGE model in which government policy plays a useful role in stabilizing the business cycle. By contrast, it's pretty easy to make a DSGE model in which government plays no useful role, and can only mess things up. So what ends up happening? You guessed it: a macro literature where most papers have only a very limited role for government.

In other words, a macro literature whose policy advice is heavily tilted toward the political preferences of conservatives.

Is that bad? Not necessarily. If the facts had a well-known conservative bias - i.e., if the models that fit the data best were the models that implied no role for government - then that would just be too bad for liberals! Liberals would have to accept that their ideas were contradicted by the best scientific evidence available.

But I contend that in the case of DSGE models, conservative policy recommendations don't emerge because they come from the best models, but only because they come from the easiest models. Thus, the conservative slant of modern macro comes not from the weight of evidence, but from the combination of publication bias and the inherent unwieldiness of the DSGE framework.

Now here's something else that might be worth mentioning. The DSGE framework was invented in large part by Ed Prescott, a man with deeply conservative political beliefs. The insistence that microfounded models with individual optimization were the only believable "structural" models - i.e., the only models that could answer the Lucas critique - came mostly from people with deeply conservative political beliefs (including Robert Lucas himself). And the criticism of alternative modeling approaches - in particular, of SVARs - seems to be much louder from economists with deeply conservative political beliefs.

That by itself proves nothing. (Maybe they're conservative because they believe the results of their models! Maybe conservatives are more scientifically honest!) But it seems like circumstantial evidence against the alleged political neutrality of modern macro methods.

Was DSGE created as an intentional conspiracy by conservatives to force macroeconomists onto a playing field tilted toward laissez-faire policy conclusions? Almost certainly not. Have conservative-minded macroeconomists been privately pleased with the publication dominance of models that tend to vindicate their prior beliefs? Almost certainly yes. Do I have a better alternative modeling approach handy? No (I'm not brave or foolish enough to mount a spirited defense of SVARs).

The real question, though, is: Has the "conservative publication bias" of DSGE made macroeconomists more complacent than they should be about searching for alternative modeling approaches, even in light of the extremely limited usefulness of DSGE models three decades after their creation? I don't know the answer. But if the answer is "Yes," then the claim that DSGE is a politically neutral tool of economic science is not quite right...

Update: It's worth pointing out that Thomas Sargent, one of the pioneers of both DSGE and Rational Expectations, and one of the three Nobel Prize winners in the photo at the top of the post, is actually a Democrat (though it's also worth pointing out that he left the Rational Expectations paradigm and started working on learning-based models, which have proven to be a lot harder to work with!).

Thursday Roundup (2/16/2012)

Your weekly dose of under-discussed econ bloggery:

1. PUBLIC GOODS MINI-BONANZA!
1a. Mark Thoma says we should be taking advantage of low interest rates to spend big on infrastructure. I agree.
1b. John Cochrane laments the fact that our government spends a lot more on transfers (mostly health care) than on infrastructure. Can he be joining the new Thiel/Tabarrok conservatism? I hope so!
1c. Eric Rauchway notes that much of the productivity improvements during the Depression were due not to great exogenous leaps in technology, but simply due to massive infrastructure creation. That is cool, and I didn't know that.
1d. Tyler Cowen links to an Ed Glaeser column on how to improve U.S. infrastructure spending.

2. Brad DeLong discusses the forward march of human liberty. Upshot: the post-WW2 20th century was pretty much all good.

3. DeLong also cites some very interesting thoughts on the British industrial revolution. The idea is that technological progress was made necessary by high labor costs and made possible by cheap capital. Does this mean that the flood of cheap Chinese labor might be holding back innovation right now?

4. Steve Williamson reveals why his antipathy toward Paul Krugman is so strong. Basically, he sees Krugman as anti-intellectual and anti-math.

5. Robert Waldmann and Mark Thoma discuss the differences between New Keynesian and Old Keynesian models of the macroeconomy.

6. Simon Wren-Lewis has a great discussion of the splintering of the macro field into "schools of thought."

7. Mark Thoma presents some alternatives to Tyler Cowen's ideas for the big banks.

8. Matt Yglesias harps on one of my very favorite topics - the fact that building and land-use restrictions have retarded the growth of Silicon Valley.

9. There are many worse things you could read than the writings of Menzie Chinn on global imbalances.

10. Greg Mankiw points out that I am his grandstudent.

11. Last but not least, an oldie but goodie: Scott Sumner on why the lack of small-sized recessions casts doubt on the idea that real shocks cause business cycles.

How I survived the Economics Job Market

Come fall, I will collect my PhD from the University of Michigan and go to work as an assistant professor of finance at the Stony Brook University College of Business. It's a really excellent department, full of smart, good people doing interesting research, and it's in a really nice location, and, well, I'm very happy to be going there.

For me, this concludes the rite of passage known as the "economics job market" (or, to economists, simply "the Job Market"). It is difficult to overstate how important a component of the economics grad school experience the Job Market is. I'd always intended to do a lengthy post about the process, just to give outsiders a little taste of what it's like (kind of like I did with my first-year macro course). The problem is, when it came time to go on the Job Market, I went about it all wrong, ignoring or simply failing to follow almost every piece of advice that grad students receive. This is not to say that that advice is wrong; it's good advice! I'm just saying that I am a very unusual case, and therefore not the best person to be writing this post.

But heck, I'm going to write it anyway. Just keep in mind, my own approach is more of a "don't try this at home" kind of thing.

(Note: Economists can obviously skip most of this, since you know it already. For PhD students, I recommend reading John Cawley's guide to the job market instead of listening to anything I have to say. The following account is primarily for non-economists who want a window into our secret world.)


What is the Job Market?

The Job Market is the main process by which PhD economists get jobs, though not the only process. There are plenty of jobs outside the Job Market, which you get by applying in the normal way (cold calling, resume mailing, connections, etc.). But the Job Market has three advantages, namely:

1. The job advertisement, application, and interview processes are all centralized,

2. You get the resources of your academic department to help you send out your applications, and

3. You are almost completely assured of getting some sort of job at the end of the process.

Jobs that are included in the Job Market are almost all posted on one website: Job Openings for Economists, which is run by the American Economic Association. You find the jobs you want to apply for, you send a list of these to your department, and you find three or four people to write you letters of recommendation.

The department then does two things. The administrative people forward your letters to the employers, and the Placement Coordinator tries to help identify good targets to call and recommend you directly. (Here's a good place for me to give special thanks to Chris House, this year's Placement Coordinator at Michigan, who did a bang-up job, and put up with my unorthodox behavior with consummate patience.)

After applications go out, employers contact grad students for interviews. These interviews take place at the AEA's Annual Meeting in early January, which is a gigantic confab where most of the economists in the country go to eat free food and hobnob. As an interviewee, you generally don't have time to go to any of the presentations or speeches; you're running back and forth from hotel to hotel, going to interview after interview. This year's AEA Meeting was in Chicago.

You then wait a week or two, and the employers who are interested in you call you back and schedule a flyout. Flyouts, which happen in February and March, involve going to the place, meeting the people, getting taken to dinner, and presenting your research. After that, offers go out.

There is also something called the "Secondary Market" or "scramble," in which people who don't get jobs through the aforementioned process get matched with employers who couldn't find anyone to fill their slots. I don't know exactly how this works, but I think the Placement Coordinator has to do a lot of work, making phone calls and such. The sense I get is that essentially everyone at a top 50 school eventually winds up with some kind of job.

Which, if you think about it, is pretty awesome. Especially as things currently stand in our economy. Being an econ grad student has its drawbacks, but joblessness afterward is definitely not one of them.


What do you need in order to go on the Job Market?

You need a Job Market Paper, or "JMP." This is a piece of original research, usually the first chapter of your dissertation.

That's it! Really! That's all you need! In the words of the great professor Yusufcan Masatlioglu, "All you need is that one damn paper."

At your AEA Meeting interviews, you will mainly discuss your JMP. At your flyout, you will present this paper. You don't need to have published this paper; in fact, you don't need a single academic publication (most people save publishable papers for when they are already working as assistant profs, where the papers will count towards tenure). "All you need is that one damn paper."

The JMP is to economists what a demo reel is to animators. Although the research should be original and at least mildly interesting, the main purpose is to show off your skills - be they DSGE modeling, time series econometrics, game theory proofs, the ability to find novel instruments, or whatever. Whether or not you obtain a world-changing result is less important.

Actually, this is one area in which I didn't do the normal thing. My JMP (which you can read here) is more about an interesting result and a novel methodology than it is about showcasing my skills. Although I showed that I could run experiments and work with panel data, I didn't show the time-series econometrics or DSGE modeling skills I learned in my classes and in other research projects, and I definitely didn't show off any of the math skills that I have from my physics background. So my JMP, while (in my opinion) an interesting paper, wasn't really a complete "demo reel." That was risky.

For other examples of JMPs, check out the excellent papers of my fellow Michigan job candidates David Agrawal (tax), Jesse Gregory (labor/urban), Collin Raymond (micro theory), David Ratner (macro/labor), Caroline Weber (tax), Roy Chen (experimental), Gabriel Ehrlich (macro), Nora Dillon (labor), Bill Lincoln (trade), Noam Gruber (macro), and Brad Hershbein (education).


What kind of jobs do economists want from the Job Market?

Most economics grad students really, really want an academic job. It's part of the grad school culture to believe that academic jobs (or jobs at the Federal Reserve) strictly dominate all other kinds, regardless of money, location, or quality of the institution. (Keep in mind, I have no hard data to back this up, but this is just kind of the sense I get.) Consulting, for example, even though it pays more, is definitely viewed by most grad students as a fallback option. Maybe this is just because as a student in an academic department, you're surrounded by professors who chose the academic way of life, and so tend to think it's the greatest. And I guess if PhD students just wanted big bucks, they'd have gone for Masters in Financial Engineering instead.

Like most applicants, I applied to a mix of academic, private-sector, and government jobs; although I wasn't as dead set on academia as some of my peers, it ended up clearly being the best choice for me, given the quality of the department at Stony Brook.

Job Market applicants are strongly encouraged not to have location preferences - i.e., not to rule out any region of the country. I cant tell you how many times I heard this! "No location preferences!" But I had strong location preferences, and so in this way I totally flew in the face of everything I was told (sorry, Chris!). I wanted to live on one of the coasts, in or near a large city, preferably a place where I already knew a lot of people. So I ended up applying to far fewer places than most of my peers: whereas a normal Job Market candidate usually applies to about 120 places, I applied to only about 80. This meant that I got correspondingly fewer interviews at the AEA Meeting (14, which is slightly below the Michigan average). In the end I lucked out, finding a job in the NYC area where I have a number of friends. But stubbornly sticking to my location preferences was clearly a risky move ex ante.


What kind of Job Market candidates do the best?

In general, academic employers want someone who can publish a bunch of papers in top-ranked journals. Non-academic employers are rumored to want whoever the academic employers want, and some say that they content themselves with taking the people who didn't get the academic jobs (again, this seems weird to me, but whatever).

Academic departments often have "slots" to fill. Economics, as a profession, is divided into fields, and so a department will often look for a "macro person" who will be assumed to do things like DSGE macro models and VARs, or a "tax person," who will do, um, tax stuff. Etc. You signal what "slot" you will fill by what kind of things you put in your JMP, by what field classes you took, and to a lesser extent by what you say in your cover letter.

Yet another way in which I didn't take the usual path was in terms of what "slot" I went for. I took macro as my main field class, but my JMP, while relevant for macro issues, was not the kind of thing that macro people usually do. My JMP was an experiment. However, I also didn't apply to any of the traditional "experimental econ" schools (e.g. Texas A&M). So I was a bit of an unusual applicant, difficult to fit into a pre-existing slot. This probably hurt me somewhat; while in good hiring years, schools are much more likely to gamble on an idiosyncratic or unusual type of researcher, in bad years they are much more averse to that sort of risk...and this was not a good hiring year. It was definitely a big ex ante risk not to go on the Market in one of the standard categories.


What are the advantages and disadvantages of the way the Job Market is set up?

This part is purely subjective, of course.

The way the economics profession runs its Job Market has obvious advantages. With a centralized, standardized application system, you can send out a lot of applications in a relatively small time. The single location of the first-round interviews is incredibly convenient. And the fact that all the interviews and flyouts happen within a short space of time allows employers and candidates alike to weigh all their options and select the best fit (it also allows competitive bidding and negotiation). So as a market, I have to say that the Job Market works very well - exactly what we'd expect from the economics profession.

But I must say, the centralized system does seem to have some drawbacks. For one, the structure of the market forces departments to pick and choose where they push their own candidates. Typically, there will be a small number of "stars" that the department pushes very hard, in the hopes of getting them into top schools. The knowledge that there can only be a few of these "stars" is not lost on first- and second-year grad students. That can be stressful.

In general, the Job Market is very stressful for most of the people who participate in it, and not only because one's future hangs in the balance. The fact that everyone is doing the same thing at the same time, and judging their success by essentially the same criteria, invites people to spend their time comparing themselves to their peers instead of thinking about their own individual preferences and what they really want to do with their lives. This comparison is so frenzied and intense that the Job Market has its own anonymous forum, Economics Job Market Rumors, where the gossip flies fast and thick (here's a thread about yours truly; here's a second, and a third).

In fact, I must say that the whole process has a slightly unpleasant whiff of a hierarchical culture (the profession) telling people what to want in life, defining a game and then convincing people that winning that game is all that matters. I can't help being reminded just a tiny bit of the Japanese college entrance exam system.

I suppose that the latter is a big part of why I personally took such an unorthodox approach toward the Job Market. Instead of focusing on winning the game, I focused (perhaps excessively?) on doing only what I wanted to do. I wanted to do research that I thought was interesting, so I did it. I also wanted to apply only to places where I would really want to work, and so I did that too. I took the advice of the great urban economist Masahisa Fujita, who once told me just to do what I wanted to do and not to worry too much about where I ended up. That approach succeeded in lowering my stress level dramatically during the Job Market process (though it probably slightly raised the stress level of my dissertation committee and Michigan's Placement Coordinator, for which I apologize).

In the end, I found a great job; statistically speaking, I got lucky. The vast majority of economics job-seekers will benefit from being significantly more orthodox than I was, even if it comes at the cost of more temporary stress.

Anyway, the ever-exuberant anarchist in me looks at the Job Market and says: "Pah, look at this rigid apparatus of social control! Blow it all up. Decentralize it and leave everyone to their own devices. Let job seekers think about what kind of job and what kind of life they want instead of where they rank." But the pragmatist in me says: "No, this system works well. It's relatively quick and low-effort, and it ends up nearly guaranteeing a job to people who participate in it. That far outweighs the downsides." The pragmatist wins. And hey, the Job Market ended up working for me, so what do I have to complain about anyway?

(Final Note: In addition, I'd like to give special thanks to my thesis committee members, Miles Kimball, Bob Barsky, Uday Rajan, and Yusufcan Masatlioglu, for the nearly absurd amounts of help they've given me; Dave Agrawal and Jeff Smith, for all the valuable job-market advice; and, of course, Vinnie Vinjimoor of the UMich econ department front office, for being the most competent human being on the face of the planet.)

Chucking the Solow growth model, cont.

A few posts back, I wrestled with Jim Bullard's hypothesis that a fall in asset prices caused a permanent negative shock to GDP. Since then, a whole bunch of people have weighed in with interesting comments.

Scott Sumner and Paul Krugman raise the first of the two issues I had raised: It is tough to interpret housing price changes as destruction of the capital stock. I can actually think of a few ways that housing price changes might affect productivity, and so can Brad DeLong, but I think we both agree that the change would be second-order.

David Andolfatto conjectures that regime-switching models of productivity growth - a sort of modified RBC model - could produce the kind of effects Bullard is talking about. But I don't think that's quite right, at least not the models that Andolfatto is talking about. In those models, asset prices fall because productivity falls; asset price changes are not the cause of changes in output, as Bullard hypothesizes.

What I find more interesting is this older Andolfatto post, which David kindly linked me to in the comments. It points to a model by Robert Shimer in which real wages are sticky. In that model, destruction of part of the capital stock does, in fact, lead to a permanent fall in GDP. (It does represent a chucking of the Solow growth model, because in the Solow model, real wages are not sticky. Shimer's model will not exhibit the conditional convergence that is the Solow model's main result.). This is very interesting. I'll have to take a closer look at the model, to see exactly how it works. If this model matches reality, then my second problem with Bullard's hypothesis is no longer a problem.

Right off the bat, I have some doubts about Shimer's model. Rapid growth after wars and natural disasters is a common phenomenon. And the evidence for conditional convergence is pretty strong. But the later part of Shimer's paper replaces the "real wages can never change" model with a model of search frictions in which real wages eventually move, slowly enough to cause jobless recoveries but presumably quickly enough to allow conditional convergence in per capita GDP across countries. That seems a lot more believable, though it still doesn't explain the "bounce back" from wars and disasters. Anyway, like I said, I'll have to take a closer look at the paper to see if the model makes sense.

So, assuming Bullard has in mind a model like Shimer's, we still have the question of why a fall in housing prices represents destruction of the capital stock.


Update: Jim Bullard responds. It appears what he had in mind is something like this:

1. The "trend" rate of growth had slowed in the 2000s, for whatever reason (technology slowdown, etc.).

2. However, self-fulfilling (but unrealistic) expectations about asset prices temporarily pushed GDP growth above its new, lower trend.

3. The end of that expectations-fueled boom sent the economy back to its new, lower trend growth path.

Not sure if I've got it entirely correct, but that seems to be the gist. It does not rely on destruction of the capital stock to explain the recession, rendering the rest of this post largely irrelevant (though interesting nonetheless).

Readers of this blog will know that I am very sympathetic to the idea of self-fulfilling expectations, sunspot equilibria, etc. I do still have one problem with this story, though. For this kind of story to produce a one-time permanent drop in GDP followed by resumed growth at the old trend rate, it would have to be that the 2000s housing boom masked a productivity slowdown that is now over. That could happen, sure, but I'd like to see some evidence before I believe it. Also, it would leave a puzzle as to why inflation was low during the bubble...