Theory vs. Data in economics

OK, I promised a more pompous/wanky followup to my last post about "theory vs. data", so here it is. What's really going on in econ? Here are my guesses.

First of all, there's a difference between empirics and empiricism. Empirics is just the practice of analyzing data. Empiricism is a philosophy - it's about how much you believe theories in the absence of data. You can be a pure theorist and still subscribe to empiricism - you just don't believe your theories (or anyone else's theories) until they've been successfully tested against data. Of course, empiricism isn't a binary, yes-or-no-thing, nor can it be quantitatively measured. It's just a general idea. Empiricism can encompass things like having diffuse priors, incorporating model uncertainty into decision-making, heavily penalizing Type 1 errors, etc.

Traditionally, econ doesn't seem to have been very empiricist. Economists had strong priors. They tended to believe their theories in the absence of evidence to the contrary - and since evidence of any kind was sparse before the IT Revolution, that meant that people believed a lot of untested theories. It was an age of great theoryderp.

That created a scientific culture that valued theory very highly. Valuable skills included the ability to make theories (math skill), and the ability to argue for your theories (rhetorical skill). Econ courses taught math skill, while econ seminars taught rhetorical skill.

Then came the IT Revolution, which dramatically reduced the costs of gathering data, transmitting data, and analyzing data. It became much much easier to do both high-quality empirical econ and low-quality empirical econ.

But at the same time, doing mediocre theory became easier and easier. The DSGE revolution established a paradigm - an example plus a framework - that made it really easy to do mediocre theory. Just make some assumptions, plug them into an RBC-type model, and see what pops out. With tools like Dynare, doing this kind of plug-and-chug theory became almost as easy as running regressions.

But Dynare and RBC didn't make it any easier to do really good theory. Really good theory requires either incorporating new math techniques, or coming up with new intuition. Computers still can't do that for us, and the supply of humans who can do that can't be easily increased.

So the supply of both good and mediocre empirics has increased, but only the supply of mediocre theory has increased. And demand for good papers - in the form of top-journal publications - is basically constant. The natural result is that empirical papers are crowding out theory papers.

But - and here comes some vigorous hand-waving - it takes some time for culture to adjust. Econ departments were slow to realize that these supply shifts would be as dramatic and swift as they were. So they focused too much on teaching people how to do (mediocre) theory, and not enough on teaching them how to do empirics. Plus you have all the old folks who learned to rely on theory in a theory-driven age. That probably left a lot of economists with skill mismatch, and those people are going to be mad.

At the same time (more hand-waving) the abruptness of the shift probably creates the fear that older economists - who review papers, grant tenure, etc. - won't be able to tell good empirical econ from mediocre. Hence, even empirical economists are quick to police the overuse of sloppy empirical methods, to separate the wheat from the chaff.

Now add two more factors - 1) philosophy, and 2) politics.

People have a deep-seated need to think we know how the world works. We have a very hard time living with uncertainty - most of us are not like Feynman. When all we have is theory, we believe it. We hate Popperianism - we recoil against the idea that we can only falsify theories, but never confirm them.

But when we have both facts and theory, and the two come into a local conflict, we tend to go with the facts over the theory. The stronger the facts (i.e. the more plausible the identification strategy seems), the more this is true.

The data revolution, especially the "credibility revolution" (natural experiments), means that more and more econ theories are getting locally falsified. But unlike in the lab sciences, where experiments allow you to test theories much more globally, these new facts are killing a lot of econ theories but not confirming many others. It's a Popperian nightmare. Local evidence is telling us a lot about what doesn't work, but not a lot about what does.

In physics it's easy to be a philosophical empiricist. As a physics theorist, you don't need to be afraid that the data will leave you adrift in the waters of existential uncertainty for very long. Physics is very non-Popperian - experimental evidence kills the bad theories, but it also confirms the good ones. In the early 20th century, a bunch of experimental results poked holes in classical theories, but quickly confirmed that relativity and quantum mechanics were good replacements. Crisis averted.

But that doesn't work in econ. A natural experiment can tell you that raising the minimum wage from $4.25 to $5.05 in New Jersey in 1992 didn't cause big drops in employment. But it doesn't tell you why. Since you can't easily repeat that natural experiment for other regions, other wage levels, and other time periods, you don't get a general understanding of how employment responds to minimum wages, or how labor markets work in general. Crisis not averted.

So philosophical empiricism is far more frightening for economists than for natural scientists. Living in a world of theoryderp is easy and comforting. Moving from that world into a Popperian void of uncertainty and frustration is a daunting prospect. But that is exactly what the credibility revolution demands.

So that's probably going to cause some instinctive pushback to the empirical revolution.

The final factor is politics. Theoretical priors tend to be influenced to some degree by politics (in sociology, that's usually left-wing politics, while in econ it tends to be more libertarian politics, though some left-wing politics is also out there). A long age of theoryderp created a certain mix of political opinions in the econ profession. New empirical results are certain to contradict those political biases in many cases. That's going to add to the pushback against empirics.

So there are a lot of reasons that the econ profession will tend to push back against the empirical tide: skill mismatch, the limitations of natural experiments, and the existing mix of political ideology.

Of course, all this is just my hand-waving guess as to what's going on in the profession. My guess is that econ will be dragged kicking and screaming into the empiricist fold, but will get there in the end.

A bit of pushback against the empirical tide

There has naturally been a bit of pushback against empiricist triumphalism in econ. Here are a couple of blog posts that I think represent the pushback fairly well, and probably represent some of the things that are being said at seminars and the like.

First, Ryan Decker has a post about how the results of natural experiments give you only limited information about policy choices:

[T]he “credibility revolution”...which in my view has dramatically elevated the value and usefulness of the profession, typically produces results that are local to the data used. Often it's reasonable to assume that the "real world" is approximately linear locally, which is why this research agenda is so useful and successful. But...the usefulness of such results declines as the policies motivated by them get further from the specific dataset with which the results were derived. The only way around this is to make assumptions about the linearity of the “real world”[.] (emphasis mine)

Great point. For example, suppose one city hikes minimum wages from $10 to $11, and careful econometric analysis shows that the effect on employment was tiny. We can probably assume that going to $11.50 wouldn't be a lot worse. But how about $13? How about $15? By the time we try to push our luck all the way to $50, we're almost certainly going to be outside of the model's domain of applicability.

I have not seen economists spend much time thinking about domains of applicability (what physicists usually call "scope conditions"). But it's an important topic to think about.

Ryan doesn't say it, but his post also shows one reason why natural experiments are still not as good as lab experiments. With lab experiments you can retest and retest a hypothesis over a wide set of different conditions. This allows you to effectively test whole theories. Of course, at some point your ability to build ever bigger particle colliders will fail, so you can never verify that you have The Final Theory of Everything. But you can get a really good sense of whether a theory is reliable for any practical application.

Not so in econ. You have to take natural experiments as they come. You can test hypotheses locally, but you usually can't test whole theories. There are exceptions, especially in micro, where for example you can test out auction theories over a huge range of auction situations. But in terms of policy-relevant theories, you're usually stuck with only a small epsilon-sized ball of knowledge, and no one tells you how large epsilon is.

This, I think, is why economists talk about "theory vs. data", whereas you almost never hear lab scientists frame it as a conflict. In econ policy-making or policy-recommending, you're often left with a choice of A) extending a local empirical result with a simple linear theory and hoping it holds, or B) buying into a complicated nonlinear theory that sounds plausible but which hasn't really been tested in the relevant domain. That choice is really what the "theory vs. data" argument is all about.

Anyway, the second blog post is Kevin Grier on Instrumental Variables. Grier basically says IV sucks and you shouldn't use it, because people can always easily question your identification assumptions:

First of all, no matter what you may have read or been taught, identification is always and everywhere an ASSUMPTION. You cannot prove your IV is valid...

I pretty much refuse to let my grad students go on the market with an IV in the job market paper. No way, no how. Even the 80 year old deadwoods in the back of the seminar room at your job talk know how to argue about the validity of your instruments. It's one of the easiest ways to lose control of your seminar. 

We've had really good luck placing students who used Diff in diff (in diff), propensity score matching, synthetic control, and even regression discontinuity. All of these approaches have their own problems, but they are like little grains of sand compared to the boulder-sized issues in IV.

He's absolutely right about the seminar thing. Every IV seminar degenerates into hand-waving about whether the instrument is valid. He doesn't mention the problem of weak instruments, either, which is a big problem that has been recognized for decades.

Now, Kevin is being hyperbolic when he categorically rejects IV as a technique. If you find a great instrument, it's really no different than regression discontinuity. And when you find a really good instrument, even the "deadwoods" in the back of the room are going to recognize it.

As for IV's weakness in the job market, that's probably somewhat due to the fact that it's been eclipsed by other methods that have not been around as long as IV. If and when people overuse those methods, it's highly probable that people will start making a lot of noise about their limitations. And as Ed Leamer reminds us, there will always be holes to poke.

Anyway, these posts both make good points, though Kevin's is a little over-the-top. Any research trend will have a pushback. In a later, more pompous/wanky post, I'll try to think about how this will affect the overall trend toward empiricism in econ... (Update: Here you go!)

Is the EMH research project dead?

Brad DeLong:

[I]t is, I think, worth stepping back to recognize how very little is left of the original efficient market hypothesis project, and how far the finance community has drifted--nay, galloped--away from it, all the while claiming that it has not done so... 

The original EMH claim was...[y]ou can expect to earn higher average returns [than the market], but only by taking on unwarranted systematic risks that place you at a lower expected utility... 

[But f]inance today has given up any preference that the--widely fluctuating over time--expected systematic risk premium has anything to do with [risk]...It is very, very possible for the average person to beat the market in a utility sense and quite probably in a money sense by [buying portfolios of systematically mispriced assets].

DeLong cites the interesting new paper "Mispricing Factors", by Robert Stambaugh and Yu Yuan. The paper puts sentiment-based mispricing into the form of a traditional factor model.

Is DeLong right? Is the Efficient Markets research project dead?

Well, no. Models that explain time-varying risk premia (really, time-varying excess returns) as the result of time-varying utility are far from dead. The finance academia community doesn't use these models exclusively, but they are still very common. Probably the most popular of these is the "long-run risks" model of Bansal and Yaron (2004), which relies on Epstein-Zin preferences to produce time-varying risk aversion. As far as I am aware, lots of people in finance academia still consider this to be the best explanation for "excess volatility" (the time-series part of the EMH anomalies literature). In a different paper from around the same time, Bansal et al. claim that this approach can also explain the cross-section of expected returns.

(Note: As Brad mentions in the comments, Epstein-Zin preferences are different from Von Neumann-Morganstern expected utility. It represents a departure from the standard model of risk preferences, but not from the core idea of the risk-return tradeoff.)

So the idea of explaining asset returns with funky risk preferences is not dead by any means. But this literature does seem to have diverged a bit from the literature on factor models.

As soon as multifactor models like Fama-French started coming out, people pointed out that they weren't microfounded in economic behavior. There was no concrete reason to think that size and value should be associated with higher risk to the marginal investor. EMH-leaning supporters of the models - like Fama himself - waved their hands and suggested that these factors might be connected to the business cycle, and thus possibly to risk preferences. But in the end, it didn't really matter. The models seemed to work - they fit the data, so practitioners started using them.

But since factor models aren't explicitly connected to preferences, there's no reason not to simply treat apparent mispricings as factors in a factor model. Really, the first example of this was "momentum factors". But the new Stambaugh and Yuan paper takes this approach further. From their abstract:

A four-factor model with two "mispricing" factors, in addition to market and size factors, accommodates a large set of anomalies better than notable four- and five-factor alternative models...The mispricing factors aggregate information across 11 prominent anomalies...Investor sentiment predicts the mispricing factors...consistent with a mispricing interpretation and the asymmetry in ease of buying versus shorting. Replacing book-to-market with a single composite mispricing factor produces a better-performing three-factor model.

Stambaugh and Yuan take the "mispricing factors" approach further than in the past, by looking at limits to arbitrage and at investor sentiment. Limits to arbitrage and investor sentimennt are microfoundations - they are an explanation of mispricing factors in terms of deeper things in the financial markets. In other words, Stambaugh and Yuan aren't just fitting curves, as the momentum factor people were. This is behavioral finance in action.

Now this doesn't mean that the EMH research project is dead. First of all,  Stambaugh and Yuan still have to compete with papers by Bansal and other people working on the EMH research project. Second of all, increased attention to the "mispricing factors", or decreases in the institutional limits to arbitrage, may make them go away in the future. Third, risk-preference-based factors may still coexist with mispricing factors. And fourth, even if the mispricing factors are robust, the EMH is still a great jumping-off-point for thinking about financial markets.

So I think the rise of mispricing factors doesn't really signal the death of the EMH research project. What I think it signals is that finance researchers as a group are open-minded and eclectic, unwilling to restrict themselves to a single paradigm. Which I think is a good thing, and something econ people could stand to learn from...

Whig vs. Haan

If you want to understand Whig History, just look at the difference between the traditional European and the Disney versions of The Little Mermaid (spoiler alert!). Up until the end, they're pretty much the same - the mermaid dreams of love, and makes a deal with the evil witch, but she fails to get the prince to kiss her, and as a result she forfeits her life to the witch. In the European version, the mermaid dies and turns into sea foam, her dreams dashed. In the American version, however, the mermaid and the prince simply stab the witch in the chest with a broken bowsprit, and everyone lives happily ever after.

I think this difference is no coincidence. Around 1800, history had a structural break. Suddenly, the old Malthusian cycle of boom and bust was broken, and living standards entered a rapid exponential increase that is still going today. No wonder Americans love the Hollywood ending. In an economic sense, that's all we've ever really known. 

So Whig History - the notion that everything gets better and better - overcame Malthusian History. But there's another challenge to historical optimism that's much less easy to overcome. This is the notion that no matter how much better things get, society is fundamentally evil and unfair. 

I know of only one good name for this: the Korean word "Haan". (It's often spelled "Han," but I'll use the double "a" to avoid confusion with the Chinese race, the Chinese dynasty, and the Korean surname.) Wikipedia defines Haan thus:

Haan is a concept in Korean culture attributed as a unique Korean cultural trait which has resulted from Korea's frequent exposure to invasions by overwhelming foreign powers. [Haan] denotes a collective feeling of oppression and isolation in the face of insurmountable odds (the overcoming of which is beyond the nation's capabilities on its own). It connotes aspects of lament and unavenged injustice. 

The [writer] Suh Nam-dong describes [haan] as a "feeling of unresolved resentment against injustices suffered, a sense of helplessness because of the overwhelming odds against one, a feeling of acute pain in one's guts and bowels, making the whole body writhe and squirm, and an obstinate urge to take revenge and to right the wrong—all these combined."... 

Some scholars theorize the concept of [Haan] evolved from Korea's history of having been invaded by other neighboring nations, such as Han China, the Khitans, the Manchu/Jurchens, the Mongols, and the Japanese.

Though Korean writers claim that Haan is a uniquely and indescribably Korean experience, there seem to be parallels in certain other cultures. A number of Koreans have told me that "Korea is the Ireland of the East," comparing Korea's frequent subjugation to the domination of Ireland by England. 

Now, I am hugely skeptical of cultural essentialism. I doubt Haan is either unique to certain cultures or indelible. In fact, I bet that economic progress will drastically reduce it. There are signs that this is already happening - young Koreans are much, much less antagonistic toward Japan than the older generation.

But in a more general sense, Haan seems to describe an undercurrent of thought that runs through many modern, rich societies. You see it, for example, in leftist resistance to Steve Pinker's thesis that violence has decreased hugely. Pinker brought huge reams of data showing that violent crime and war have been in a long-term decline for centuries now. Leftist critics respond by citing anecdotal examples of war, atrocity, and injustice that still exist. 

This seems like a Haan view to me. The idea is that as long as examples of serious violence exist, it's not just incorrect but immoral to celebrate the fact that they are much more rare and generally less severe than in past times. 

Actually, talking about Pinker can often draw out what I think of as Haan attitudes. I was talking about Pinker to a friend of mine, a very sensitive lefty writer type. Instead of citing ISIS or the Iraq War as counterexamples, she talked about the problem of transphobia, and how "trans panic" legal defenses were still being used to excuse the murder of transsexual people. I checked, and this has in fact happened once or twice. My friend presented this as evidence that - contra Pinker - the world isn't really getting better. Injustice anywhere, under Haan thinking, invalidates justice everywhere else.

Another example of Haan is Ta-Nehisi Coates' view of history. The subheading of Coates' epic article, "The Case for Reparations," is this:

Two hundred fifty years of slavery. Ninety years of Jim Crow. Sixty years of separate but equal. Thirty-five years of racist housing policy. Until we reckon with our compounding moral debts, America will never be whole.

Now unless Coates gets to write his own subheadings, he didn't write those words. But they accurately sum up the message of the piece. The idea is that these wrongs against African Americans cause a moral debt that need to be repaid. It's not clear, of course, how the debt could be repaid, or what "reparations" actually would entail. But what's clear is the anti-Whig perspective. Progress does not fix things. The fact that Jim Crow was less horrible than slavery, and that redlining was less horrible than Jim Crow, and that today's housing policy is less horrible than redlining, does not mean that things are getting better. What matters is not just the flow of current injustice, but the stock of past injustices.

Haan presents a vision of stasis that is different from the Malthusian version. By focusing on the accumulated weight of history instead of the current situation, and by focusing on the injustices and atrocities and negative aspects of history, it asserts that the modern age, for all its comforts and liberties and sensitivity, is inherently wrong.

And Haan asserts that the world will remain wrong, until...what? That's usually not clearly specified. For Korean Haan theorists, it's a vague notion of "vengeance." For Coates, it's "reparations". For leftists, it's usually a revolution - a massive social upheaval that will overthrow all aspects of current power, hierarchy, and privilege, and make a new society ex nihilo. The details of that revolution are usually left a bit ambiguous.

But the vagueness and ambiguity of the imagined deliverance doesn't seem to be a big problem for most Haan thinking. What's important seems to be the constant struggle. In a world pervaded and defined by injustice and wrongness, the only true victory is in resistance. Ta-Nehisi Coates expressed this in an open letter to his son, when he wrote: "You are called to struggle, not because it assures you victory but because it assures you an honorable and sane life."

Haan thinking presents a big challenge for Whig thinking.

Whig History didn't have much trouble beating the old Malthusian version of history - after a hundred years of progress, people realized that this time was different. But Haan thinking presents a much bigger challenge, because progress doesn't automatically disprove Haan ideas. Making the world better satisfies Whigs, but doesn't remove the accumulated weight of history that fuels Haan. 

Nor can all instances of injustice be eliminated. It will never be a perfect world, and the better the world gets, the more each case of remaining injustice stands out to an increasingly sensitive populace. One or two cases of "trans panic" murder would barely have merited mention in the America of 1860. But precisely because there has been so much progress - precisely because our world is so much more peaceful and so much more just now than  it was then - those cases stick out like a sore thumb now. So Whig progress makes Haan anger easier, by raising people's expectations.

There's also the question: Should Whigs even want to defeat the Haan mentality? After all, if we trust in the inevitability of progress, it may sap our motivation to fight for further progress. Optimism can lead to complacency. So Haan resentment might be the fuel that Whigs need to see our visions fulfilled.

But Haan carries some risks. Massive social revolutions, when they happen, are capable of producing nightmare regimes like the USSR. With a few exceptions, the kind of progress Whigs like is usually achieved by the amelioration of specific ills - either by gradual reform, or by violent action like the Civil War - rather than by a comprehensive revolution that seeks to remake society from scratch. In other words, as one might expect, Whig goals are usually best achieved by Whig ends.

As a character would always say in a video game I used to play, "I am a staunch believer in amelioration."

In any case, I personally like the Whig view of the world, and I want to see it triumph. The idea of a world that gets better and better is appealing on every level. I don't just want to believe in it (though I do believe in it). I want to actually make it happen. And when I make it happen, or when I see it happen, I want to feel good about that. I want to savor the victories of progress, and the expectation of future victories, rather than to be tormented by the weight of unhappy history that can never be undone. I want to be able to think not just about the people around the world who are still suffering from deprivation, violence, and injustice, but also about the people who are no longer suffering from these things.

To me, the Whig view of history and progress is the only acceptable one. But Haan presents a stern challenge to that view - a challenge that Whigs have yet to find a way to overcome.


Update: Thabiti Anyabwile, writing in The Atlantic, says similar things in reference to Coates' writings.

"Loan fairness" as redistribution

I've noticed an interesting desire, especially on the political left, to want to use loans as a means of redistribution. The idea is that lenders should be willing to make loans to poor people when the risk-return tradeoff is worse than for loans to rich people. This could mean, for example, loaning money to high-default-risk poor borrowers at the same interest rate as to low-default-risk rich borrowers. Or it could mean extending loans to poor people whose perceived default risk would previously have prevented them from getting loans. The notion that this is "fair" - or that lenders "owe" it to poor people to give them favorable lending terms - pervades such works as David Graeber's Debt: The First 5000 Years.

A more recent example is Cathy O'Neil's recent post on Big Data and disparate impact in lending:

Did you hear about this recent story whereby Facebook just got a patent to measure someone’s creditworthiness by looking at who their friends are and what their credit scores are? They idea is, you are more likely to be able to pay back your loans if the people you’re friends with pay back their loans... 

[This] sounds like an unfair way to distribute loans... 

[In the neoliberal mindset], why would anyone want to loan money to a poor person? That wouldn’t make economic sense. Or, more relevantly, why would anyone not distinguish between a poor person and a rich person before making a loan? That’s the absolute heart of how the big data movement operates. Changing that would be like throwing away money. 

Since every interaction boils down to game theory and strategies for winning, “fairness” doesn’t come into the equation (note, the more equations the better!) of an individual’s striving for more opportunity and more money. Fairness isn’t even definable unless you give context, and context is exactly what this [neoliberal] mindset ignores. 

Here’s how I talk to someone when this subject comes up. I right away distinguish between the goal of the loaner – namely, accuracy and profit – and the goal of the public at large, namely that we have a reasonable financial system that doesn’t exacerbate the current inequalities or send people into debt spirals. This second goal has a lot to do with fairness and definitely pertains broadly to groups of people.

I don't get the random swipe at "equations", but the rest all seems pretty clear, even if it is couched in vague terms like "context", "reasonable", and "pertains broadly to groups of people". The basic idea is simple: Society is more fair when lenders give poor borrowers favorable terms relative to rich borrowers.

Let's think about this idea.

One problem with the idea would be that following it might force lenders to accept negative expected returns, which would drive them into bankruptcy. But let's assume for the moment that this doesn't happen - that lenders can lend to poor people and make lower, but still positive, profit margins overall. Loan "fairness" would then act as a subsidy from lenders to borrowers - a form of redistribution via a tax on loan-making businesses.

Another problem would be a more subtle version of the first problem - the implicit "fairness tax" on lenders might reduce the amount that they lend overall, and thus hurt the economy. This would be an example of the "leaky bucket" of taxation, in which we trade efficiency losses for welfare gains.

But let's ignore that issue. Let's think not about efficiency concerns, but only about the fairness of this type of redistribution.

Obviously fairness is a a matter of opinion, but there are some things we can clarify. Who are the recipients of "loan fairness" redistribution? Answer: Poor people who ask for loans.

Some poor people ask for loans because they have businesses to start, or for standard consumption-smoothing reasons. If these people are currently subject to borrowing constraints because of asymmetric information - in other words, if they can't get a loan because lenders don't realize they can and will pay it back - then these borrowing constraints will be ameliorated by "loan fairness" redistribution. That seems like a good (and fair) thing to me.

Other poor people ask for loans that they are unlikely to be able to pay back. This might be because they don't realize that their chances of repayment are low. Or it might be because they don't really intend to pay the loans back. Both of these groups of people will benefit from "loan fairness" redistribution.

One effect of implementing "loan fairness" redistribution would be an incentive for more people to join the latter group. Once poor people realize that society's desire for redistribution has given them the opportunity to get loans on more favorable terms, some poor people - it's not clear how many, but more than zero - will certainly take advantage of this by taking out a bunch of loans that they can't or don't intend to pay back.

A final group will be those poor people who don't ask for loans. Some will probably have ideas of morality that tell them to work hard, save money, and "neither a borrower nor a lender be". Others will think it unfair to request loans that they know they are unlikely to pay back. Others will simply not need to borrow that much. These groups of poor people will not benefit from "loan fairness" redistribution, because they will not ask for loans.

This introduces what I see as a source of unfairness. Poor people who are honest, and who refuse to borrow money that they know they can't pay back, will suffer compared to poor people who are dishonest and will just borrow as much as they can without any intention of returning the money. I think one could probably find some evidence of this kind of behavior among poor-country governments that borrow money and then ask for loan "forgiveness".

That seems clearly unfair. But there also seems to be another source of borderline unfairness here. Poor people whose moral values prevent them from asking for loans will be disadvantaged relative to poor people who have no moral problem asking for loans. Morality-based redistribution sounds a little iffy to me in the fairness department.

So purely in terms of the fairness of "loan fairness" redistribution - without even talking about efficiency concerns - I see some big problems with the idea of opportunistically redistributing money to only those poor people who are willing to walk into a lender's office and ask for a loan.

A more intuitively fair method of redistribution might simply be to tax rich people and give the money to poor people. Crazy idea, I know.

"The Case For Mindless Economics", 10 years on

Ten years ago, two economic theorists, Faruk Gul and Wolfgang Pesendorfer, wrote an essay called "The Case for Mindless Economics". The essay pushes back against the enthusiasm for neuroeconomics and behavioral economics. It's a very interesting read, both for people interested in philosophy of science, and anyone who wants to know how economists think about what they do. (Before you read this post, consider reading the whole essay, because there's lots in there that I gloss over.)

Gul and Pesendorfer don't discount the possibility that neurological and psychological research can be useful in economics. They write:

Neuroeconomics goes beyond the common practice of economists to use psychological insights as inspiration for economic modeling or to take into account experimental evidence that challenges behavioral assumptions of economic models. Neuroeconomics appeals directly to the neuroscience evidence to reject standard economic models or to question economic constructs.

In other words, GP aren't arguing against using neuroscience and psychology to inform economic model-making. What are they arguing against? Two things:

1. The use of neuro/psych findings to support or reject economic models, and

2. The use of neuro/psych to establish new welfare criteria, e.g. happiness.

GP's argument against using neuro/psych to test economic models can basically be summed up by these excerpts:

Standard economics focuses on revealed preference because economic data come in this form. Economic data can — at best — reveal what the agent wants (or has chosen) in a particular situation...The standard approach provides no methods for utilizing non-choice data to calibrate preference parameters. The individual’s coefficient of risk aversion, for example, cannot be identified through a physiological examination; it can only be revealed through choice behavior. If an economist proposes a new theory based on non-choice evidence then either the new theory leads to novel behavioral predictions, in which case it can be tested with revealed preference evidence, or it does not, in which case the modification is vacuous. In standard economics, the testable implications of a theory are its content; once they are identified, the non-choice evidence that motivated a novel theory becomes irrelevant.

I'm not sure I buy the logic of this argument. In general, preemptively throwing away any entire category of evidence seems dangerous to me. Why should economists only validate/reject their models based on choice data?

Here's a concrete example to help explain what I mean. In finance, there is a big and ongoing debate over the reason for Shiller's excess volatility finding - i.e., the finding that market returns are slightly predictable over the long run. Some people say it's due to time-varying risk aversion. Others say that it's due to non-rational expectations. As John Cochrane has pointed out, price data - i.e., choice data - can't distinguish between these explanations. The standard asset pricing equation is of the form p = E[mx], where p is price, m is related to utility, and x is related to expectations/beliefs. You'll never be able to use price data alone to know whether price movements are due to changes in m or changes in x. To do that, you need additional evidence - direct measures of either preferences, beliefs, or both.

That's the kind of evidence that psychology might - in principle - be able to provide. For example, suppose psychologists find that most human beings are incapable of forming the kind of expectations that time-varying utility models say they do. That would mean one of two things. It could mean that the economy as a whole behaves qualitatively differently than the individuals who make it up (in physics jargon, that would mean that the representative agent is "emergent"). Or it could mean that time-varying utility models must not be the reason for excess volatility.

So GP might respond something along the lines of: "So? Why do we care?" Of what use would be the knowledge that excess volatility is caused by psychological constraints rather than time-varying utility, if both ideas lead to the same predictions about prices? The answer is: They don't lead to the same predictions, if you expand the data set. For example, suppose you find that survey expectations can predict price movements. What once could be modeled only as randomness now becomes a partially predictable process. You can make some money with that knowledge! All you do is take a bunch of surveys, and place bets based on the results.

Are survey responses "economic data"? Are they choice data? That question is a bit academic. What if you could use brain scans to predict market movements?

In other words, I think there's not really any conceptual difference between what GP say psych can be used for ("tak[ing] into account experimental evidence that challenges behavioral assumptions of economic models") and what they say it can't be used for ("appeal[ing] directly to the neuroscience evidence to reject standard economic models or to question economic constructs"). It's all just the same thing - using evidence to create theories that help you predict stuff.

Anyway, GP's second point - that psych/neuro evidence can't provide new welfare criteria - also doesn't make sense to me, in principle. Here, in a nutshell, is their argument:

Welfare analysis for neuroeconomics is a form of social activism; it is a recommendation for someone to change his preferences or for someone in a position of authority to intervene on behalf of someone else. In contrast, welfare economics in the standard economic model is integrated with the model’s positive analysis; it takes agents’ preferences as given and evaluates the performance of economic institutions.

I don't see this distinction at all. To be blunt, all welfare criteria seem fairly arbitrary and made-up to me. Data on choices do not automatically give you a welfare measure - you have to decide how to aggregate those choices. Why simply add up people's utilities with equal weights to get welfare? Why not use the utility of the minimum-utility individual (a Rawlsian welfare function)? Or why not use a Nash welfare function? There seems no objective principle to select from the vast menu of welfare criteria already available. The selection of a welfare criterion thus seems like a matter of opinion - i.e., a normative question, or what GP call "social activism". So why not include happiness among the possible welfare criteria? Why restrict our set of possible welfare criteria to choice-based criteria? I don't see any reason, other than pure tradition and habit.

So personally, I find the logic of both of GP's main arguments unconvincing. In principle, it seems like psych/neuro data could help choose between models when choice data is insufficient to do so. And in principle, it seems like neuro-based or psych-based welfare criteria are no more arbitrary than choice-based welfare criteria (or any other welfare criteria, like "virtue").

But that's in principle. What about in practice? It's been 10 years since GP's essay, and many more since psychology and neuroscience entered the economist's toolbox. Psychology seems to have made real contributions to certain areas of economics, in particular finance. In general, those contributions have come in the form of generating hypotheses about constraints - for example, attention constraints - rather than by motivating new behavioral assumptions for standard models. In other words, psych ideas have occasionally given economists power to predict real data in ways that standard behavioral models didn't allow. These contributions have been modest overall, but real.

But I can't really think of examples where neuroscience has made much of a successful contribution to economics yet. That might be because neuroscience is still too rudimentary. It might be that it has, and I just haven't heard of it. Or it might be that it's just incredibly hard to map from neuro concepts to econ models. In fact, GP spend much of their essay showing how incredibly hard it is to map from neuro to econ. They are right about this. (And in fact, GP's essay should be required reading for economists, because the difficulty of mapping between disciplines really gets at the heart of what models are and what we can expect them to do.)

Also, in practice, no psychology-based welfare criterion, including happiness, has gained much popular traction as a replacement for traditional utilitarian welfare criteria based on choices. So while welfare is a matter of opinion, most opinion seems to have sided with GP.

All this doesn't mean I think neuroeconomics is doomed to be useless, just that it seems like it's in its very early days. There are a few hints that neuro might be used to select between competing economic models. And the topic of using happiness as a measure of economic success occasionally crops up in the media. But the task of using neuro (and psych) for economics has turned out to be much harder than wild-eyed optimists probably assumed when the fields of neuroeconomics and behavioral economics were conceived.

So I think that while Gul and Pesendorfer didn't make a watertight logical case, their warnings about the difficulty of using neuro evidence for econ have been borne out in practice - so far. Ten years might seem like a long time, but let's see what happens in forty years.

RBC as gaslighting

"Say it wasn't you"
- Shaggy

On my last post, I wrote that "RBC gaslighting knows no shame." To which Steve Williamson said "You're a real meany with the poor RBC guys." Which reminds me that it's been a while since I wrote a gratuitous, cruel RBC-bashing post! (Fortunately the "poor RBC guys" all have high-paying jobs, secure legacies, and widespread intellectual respect that sometimes includes Nobel Prizes, so a mean blog post or two from lil' old me is unlikely to cause them any harm.)

Anyway, I used the word "gaslighting", but in case you don't know what it means, here's the def'n:

Gaslighting or gas-lighting is a form of mental abuse in which information is twisted or spun, selectively omitted to favor the abuser, or false information is presented with the intent of making victims doubt their own memory, perception, and sanity.

Basically, this is what Shaggy advises Rikrok to do in the famous 1990s song "It wasn't me." Rikrok's girlfriend saw him cheating, but Rikrok just keeps repeating his blatantly absurd defense until his girlfriend - presumably - starts to wonder if she's going crazy. Another classic example is the cheating wife in the third episode of Black Mirror.

The basic 1982 Nobel-winning RBC model - a complete-markets, representative-agent theory of business cycles where productivity shocks, leisure preference shocks, and/or government policy distortions drive business cycles - has never been very good at matching the data. This didn't take long to figure out - a lot of its implications seemed fishy right from the start and required patching. Simple patches, like news shocks, didn't really improve the fit that much. The model isn't very robust to small frictions, either. And one of the main data techniques used in RBC models - the Hodrick-Prescott filter - has been mathematically shown to be very dodgy. Furthermore, the Nobel-winning empirical work of Chris Sims showed that the main policy implication of RBC - that monetary policy can't be used to stabilize the real economy - doesn't hold up.

Now, that doesn't mean RBC is a total failure. There are some cases, as with large oil discoveries, when it sort of looks like it's describing what's going on. And very advanced modifications of basic RBC - labor search models, heterogeneous-agent models, network models, etc. - offer some hope that models that rely on TFP shocks as the main stochastic driver of aggregate volatility may eventually fit the macro data.

But that's not enough for RBC fans! The idea of RBC as one potentially small ingredient of an eventual useful theory of the business cycle is not enough. RBC fans maintain that RBC is the basic workhorse business cycle model.

For example, just last year, Ed Prescott and Ellen McGrattan released a paper claiming that if you just patch basic RBC up with one additional type of capital, it fits the data just fine. As if this were the only empirical problem with RBC, and as if this new type of capital has empirical support!

A 2007 paper by Gomme, Ravikumar and Rupert (which I mentioned in a previous post) refers to RBC as "the standard business-cycle model". As if anyone actually uses it as such!

A 2015 Handbook of Macroeconomics chapter by Valerie Ramey says:

Of course, [the] view [that monetary policy is not an important factor in business cycles] was significantly strengthened by Kydland and Prescott’s (1982) seminal demonstration that business cycles could be explained with technology shocks.

As if any such thing was actually demonstrated!

There are a number of other examples.

This strikes me as a form of gaslighting - RBC fans just blithely repeat, again and again, that the 1982 RBC model was a great empirical success, that it is now the standard model, and that any flaws are easily and simply patched up. They do this without engaging with or even acknowledging the bulk of evidence from the 1990s and early 2000s showing numerous data holes and troubling implications for the model. They don't argue, they just bypass. Eventually, like the victims of gaslighting, skeptical readers may begin to wonder if maybe their reasoning capacity is broken.

Why do RBC fans keep on blithely repeating that RBC was a huge success, needs only minor patches, and is now the standard model? One reason might be a struggle over history. In case you haven't noticed from reading the blogs of Paul Romer, Roger Farmer, Steve Williamson, Simon Wren-Lewis, Robert Waldmann, Brad DeLong, John Cochrane, and Paul Krugman (to name just a few), there is a very contentious debate over whether the macro revolutions of the late 1970s and early 1980s were a good thing or a wrong turn. If RBC was refuted - or relegated to a minor role in more modern theories - it means that the Lucas/Prescott/Sargent revolution looks just a little bit more like a wrong turn. But if RBC sailed on victorious, then that revolution looks like an unmitigated victory for science. We may be through with the past, but the past is not through with us!

Or maybe RBC represents a form of wish fulfillment. If RBC is right, stabilization policy - which, if you believe Hayek, just might be the thin edge of a socialist wedge - is just a "rain dance". Maybe people just really hope that recessions are caused by technological slowdowns, outbreaks of laziness, and/or government meddling.

It could also be a sort of high-level debating tactic. Paul Krugman talks about how Lucas and other "freshwater" economists basically failed to engage with "saltwater" ideas, preferring instead to dismiss them (Prescott and McGrattan's paper does exactly this). Maybe the blithe insistence that RBC is the standard model is simply a dig at a competitor.

Anyway, whatever the reason, it's kind of entertaining to watch. For those who are secure in the knowledge of their own sanity, watching people try to gaslight can be a form of entertainment. And besides...who cares about any of this? It's not like anyone who opposes stabilization policy ever needed an RBC model to back them up.