A little politics now and then

A brief political observation posted to My Other Blog (tm):  The Job.  Content note: contains mention Anthony Scaramucci.  (I know: who?)

That Guy on the Internet: My Other Self

While I try to keep things non-technical, this blog is deliberately econ-centric, and will remain so. But occasionally I have thoughts on other subjects:  non-econ thoughts on non-econ topics.

I imagine that the reaction of most readers to this startling revelation will be along the lines of, "And...?"  Or possibly, "God help us, he's going to rant about something!  Probably Donald Trump!  Run!"

The good news is that I'm keeping my profound and penetrating observations on matters social, political, and philosophical segregated over here:

  That Guy On The Internet's Blog

Check it out.  Or don't: I'll understand.  Yes, there's a bit about Donald Trump.  Sorry about that.

The Next New Macro

Amidst all the artillery fire, it's easy to forget that the New Classical and New Keynesian "schools" of macroeconomics were once ...well...new.  (Hence the names.)  The New Classical school arose as a response to (a) real defects in conventional macro-econometric practice and (b) the failure of conventional macro theorists to make progress towards an actual explanation of the observed behavior of modern economies.  The New Keynesian school arose as a response, taking on board both the econometric critique (yay) and the proposed solution (boo, hiss) more or less in toto.  The New Keynesians also "fixed" the most obvious defect in the New Classical models -- the fact that depressions are ruled impossible by assumption -- but did so in a manner that largely failed to advance our actual understanding of the economy (1).

The seeds of disaster, in retrospect, lay in how easily New Classical-style models could be tweaked to get Keynesian behavior.  Failing that, the New Classical-style models could never have achieved the near-monopoly position they now hold in macroeconomics (2).  And the mess in macro did not come about because some economists committed to a modelling style which turns out to yield little insight.  The mess is a consequence of the universality of this commitment -- the macro-mono-culture, if you will.

It is the danger of mono-culture that should be uppermost in our minds as we try to figure out the next "New" macro.  So much so that convergence on anything purporting to be the Next New Macro any time in the next five years (at a minimum) should be regarded with alarm.

The fact is that, right now, we do not know the way forward, and no approach, no matter how promising (or how congenial to our pre-conceptions and policy preferences) should be allowed to dominate the field until it has proven itself empirically successful.

It's important to remember that the New Classicals didn't claim that their models were successful -- only that they were more "sound" on theoretical and econometric grounds.  They were quite clear at the outset that their approach was in its infancy, and that a great deal of work remained to be done before its value could be determined (3). Why, then, did the entire discipline latch on to the New Classical approach?

In a word: panic.

Whatever the flaws in the New Classicals' positive program, their negative critique of existing econometric practice was both true and devastating.   You can't just say, "I feel in my heart that A, B, and C cause D, so here's a regression," and claim to be doing social science.  And when you're constantly saying, "Did I say 'B'?  I meant X!  A, X, and C cause D.  Also, maybe the log of B.  Here's another regression!" your credibility does not improve (4).

So imagine the plight of the working macro-economist, circa 1978 or so.  The demand for policy advice is intense -- CPI inflation had spiked to 9% by the end of the year (compared with 4.7% at the end of 1968), and unemployment seemed to have bottomed out at a floor of around 6% (compared with 3.4% in 1968).  From the President on down, your bosses want to know what the hell is going on and -- scarier still -- how to fix it.  At the same time, the terrible truth has begun to sink in:  there's no particular reason to believe that your working models actually tell you anything about how the economy works, particularly under these conditions.  (It turns out that you've actually got a pretty good idea what to do about a demand-failure depression, but this isn't that.)  Similarly, in academe, economists are scrambling to produce policy-relevant results, which is difficult with your colleagues pointing out the gaping flaws in your econometric logic.  (They literally point and laugh.  Jerks.)

So with the desperation of a drowning man, macroeconomics latched on to the first floating object to drift within reach.  Which, regrettably, then carried us all out to sea.

So here's the lesson:  don't panic.  The basic Keynesian premise ("demand matters") is firmly established.  The key policy implications (monetary policy affects the real economy as well as prices; in a demand constrained economy, public spending can increase output) are pretty much conventional wisdom, buttressed with a slew of new evidence emerging from the recent unpleasantness.  Disputes over monetary and fiscal policy are, today, largely political rather than technical (5).   The world will probably muddle through more-or-less adequately long enough for us to verify that this research agenda or that one actually leads someplace worth going.

The good news is that we have more, more detailed, higher quality, and more diverse data available than ever before.  (Example: we've only been systematically estimating the number of job openings in the U.S. for about fifteen years.  Previously, we relied on weak proxies like the volume of help-wanted advertising.  Seriously.)  So the discipline is much better prepared to weed out models that just don't work than it was forty years ago.  We just need to get out of the bad habit, acquired back when data was scarce and unreliable, of claiming that it's "too soon" to abandon "promising" models, just because they're empirically false.

I have my own notions about the best way forward, of course, and I'll post about those soon.  But I could easily be wrong and so could anyone else.  I hope we bear that in mind.  Maybe we'll be able to retire the Robert E. Lucas Jr. Award For Derailing An Entire Discipline without ever bestowing it on a second recipient.  




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(1) Short version:  In New Classical-style models, prices are perfectly flexible and markets (including the labor market) always clear, so there's no such thing as involuntary unemployment.  To generate depressions -- you know, like in real life -- New Keynesian models introduce some form of inflexible pricing by assumption.  (Example:  "Calvo pricing," named after economist Guillermo Calvo, assumes that firms only alter their prices when granted permission by an imaginary magical being -- traditionally known as the Calvo Fairy -- who periodically bestows such permission at random.  No, seriously; I am not making the Calvo Fairy up.)

(2) Well, in academic macroeconomics, anyway.  Professional forecasters still use Big Macro models, the large, complex systems which attempt to model the actual economy on a sector-by-sector basis.  (It turns out that Big Macro was not fundamentally "discredited" in the 1970's, as the New Classicals liked to claim, but was simply infeasible with the datasets and computers available circa 1975.  We're better at it now.)  And central banks tend to employ a mix of Big Macro, Paleo-Keynesian, and New Keynesian DSGE models.  (Though one might suspect that the function of the DSGE models at central banks is to merely prove that we can tweak a DSGE model to yield the same results as the Big Macro models and Paleo-Keynesian models.  Houdini lives!)

(3) Today, forty years later, we know the answer: nope, doesn't really work.  (And the claim of theoretical soundness was always...tenuous.)

(4) Note also that there was nothing especially "anti-Keynesian" about the critique.  It applied with equal force to Monetarism, the other theoretical school of the day.  The New Classical critique was about method, not theory.  The anti-Keynesian theoretical program slipped in quietly behind it, using the disrepute of mainstream econometrics to tar the reputation of mainstream Keynesianism.  But the two were, in fact, quite unrelated.

(5) Largely.  However.  Note to central banks: if your forecast for a variable (inflation, for example) is wrong in the same direction (e.g. too high) every quarter for more than five years...your model has technical problems.  Fix, please.

The Microfoundations Hoax

Demolition work on the rotten edifice of "modern macroeconomics" continues apace.  The emperor, it turns out, is not merely without clothes.  Upon closer inspection, he appears to be simply an empty cardboard box with the words "Emperor Inside" scrawled across its surface in felt-tip pen.  Paul Romer's devastating critique really deserves to be the final word on the matter.   But even Paul leaves one stone unturned, an element of modern macro so transparently intellectually dishonest that it may properly be termed a hoax: its so-called "microfoundations."

No modern macro model is complete without a pean to the virtues of its own microfoundations.  It seems that the word "microfoundations" is not allowed to appear unaccompanied by at least one self-congratulatory adjective -- "careful," "well-specified," even (shudder) "rigorous."  But, as diligent readers of George Orwell will recall, war is not peace, freedom is not slavery, ignorance is not strength, and representative agent models are not rigorously microfounded.

But let's back up a step.  What is this "microfoundations" business anyway, and why should anyone not currently seeking a tenure-track appointment in econ care even a tiny bit?  Here's a short version of the very long story:

Forty years ago, the name of the game in macroeconomics wasn't theory at all; it was forecasting.  And it wasn't particularly successful.  In retrospect, the lack of success isn't surprising.  Models were typically estimated by running regressions on a handful aggregate data series representing the experience of a single country over a very short (and rather placid) period of time (1). Moreover, in macroeconomic data, everything is pretty highly correlated with everything else.  So you could put pretty much whatever you liked into your regressions and get a really good fit with in-sample data.  Then history would happen, new data would arrive to contradict the model's predictions, and you'd either re-estimate the model (and watch the coefficients bounce around more or less at random) or you'd declare the latest data to be some kind of special case and "adjust" for it.

So when critics denigrated the models of the early '70's as "ad hoc," they had a pretty serious point.

But what was the solution to all of this ad hoc-ery?  Where were we to look for the all-important virtue of discipline?   Ideally, in social science as in physical science, the source of discipline is data.  If you want to tell the difference between a true theory and a false one, you ask reality to settle the question.  But that was the heart of the problem: with so little data, all the models looked equally good in-sample, and no model looked especially good out-of-sample.  Discipline, if there was to be any, would have to come from theory instead.  And "microfoundations" was put forward as one form of theoretical discipline (2).

The idea certainly sounded good: rather than simply making up relationships between aggregate variables like interest rates, output, unemployment, and inflation, we should show how those relationships arise from the behavior of individuals.  Or, failing that, we should at least restrict the relationships in our macro models to those which are consistent with our understanding of individual behavior.  For surely our standard assumptions about individual behavior (basically: people do the best they can under the circumstances they find themselves in) must imply restrictions on how the system behaves in the aggregate.

Sadly, this intellectual bet was lost even before it was placed.  If we take Lucas (1976) as the beginning of the microfoundations movement, we may note with some puzzlement that the premise was mathematically proven false two years earlier, in Debreu (1974) and Mantel (1974).

It is sometimes said that modern macro suffers from too much math.  But the problem is not "too much," but rather that its use of math is strangely selective.  In particular, the idea that microfoundations per se can impose "discipline" on aggregate models represents deliberate ignorance of one of the most important results in mathematical economics.  Debreu, Mantel, and Hugo Sonnenschein had shown conclusively that, for any macro behavior you care to invent, there exists a set of classically well-behaved rational utility optimizing agents that will, collectively, exhibit the desired behavior.

Put another way, the classical assumptions about individual behavior impose no limits whatsoever on the behavior of aggregate models.  Oops.

The specious pretense, then, that one's preferred models are "better" microfounded than the competition (when, in fact, all models are equally micro-foundable) is part one of the microfoundations hoax.  But it gets better.  (Or worse, depending.)

The models which preen themselves most ostentatiously in their "rigorous" microfoundations are invariably based on so-called "representative agents."  Now, every economist, at some point in their first year of graduate school, learns the mathematically necessary conditions for the existence of a representative agent corresponding to a collection of individual agents.  In the lingo of the field, we say that a representative agent exists only if (a) all of the individual agents have identical preferences; and (b) if those preferences are [jargon] quasi-homothetic [/jargon].  "Quasi-homothetic" is a fancy way of saying that 10,000 households whose resources added together equal those of Bill Gates will buy exactly what Bill Gates will buy.

Neither of these conditions are remotely plausible, and nobody believes that they are true, including macroeconomists. And if either of those conditions fails to hold, an economy which behaves as if it posessed a representative agent cannot be derived from classical microeconomic foundations.

Let that sink in for a moment: of all the macro models that have been floated over the last century or so, only the so-called microfounded models are completely and demonstrably incompatible with classical microfoundations.  And this is (or should be) obvious to anyone who didn't sleep through the first year of graduate microeconomic theory.

So when I call "microfoundations" a hoax, I'm not kidding around.  The only question is, what proportion of macroeconomists have perpetrated this hoax upon themselves, and what proportion has known this all along.








(1) Bear in mind that the entire apparatus for gathering and reporting economic statistics in the U.S. was basically created in 1947.  Pity the macroeconomist circa 1965, trying to understand the most complex social system in history based on n < 20 observations.  Yikes.

(2) "Rational expectations" was another.  The "rational expectations revolution" business probably deserves a separate post.  For now, just know that the name is a kind of mathematical pun, and that neither "rational" nor "expectations" means what you probably think they mean.  Aren't we clever?

Houdini's Straightjacket

Consider the escape artist.  He dons handcuffs, a straightjacket, leg irons, a blindfold, and a skin-tight leotard made entirely of SuperGlue (tm).  His assistants seal him inside a steamer trunk, weld the locks shut, and sink the whole mess to the bottom of a shark-infested lagoon.  Then, in dazzling display of skill, grit, and showmanship, he frees himself and emerges, alive, unharmed, and not at all eaten by sharks.  Crowd goes wild.

Now, escape artistry may be a fine form of entertainment, but it probably wouldn't be anyone's first choice as a model for the conduct of social science.  Yet, bizarrely, it has become the prevailing paradigm in macroeconomics.

How so?

Consider the macroeconomist.  She constructs a rigorously micro-founded model, grounded purely in representative agents solving intertemporal dynamic optimization problems in a context of strict rational expectations.  Then, in a dazzling display of mathematical sophistication, theoretical acuity, and showmanship (some things never change), she derives results and policy implications that are exactly what the IS-LM model has been telling us all along.  Crowd -- such as it is -- goes wild.

And let's be clear:  not even the most enthusiastic players of the macroeconomics game imagine that representative agents or rational expectations are, in any sense, empirical realities.  They are conventions, "rules of the game."  That is, they are arbitrary difficulties we impose on ourselves in order to demonstrate our superior cleverness in being able to escape them.

They are, in a word, Houdini's straightjacket.

Of course, this would all be good, clean fun, except for one thing:  Harry Houdini drowned in a straightjacket. (1)

Similarly, even defenders of "modern" DSGE models, including those of the New Keynesian (NK) variety, seem to agree that their modelling approach made arriving at sound policy recommendations unnecessarily difficult.  For example, George Evans at the University of Oregon writes:

[T]he profession as a whole seemed to many of us slow to appreciate the implications of the NK model for policy during and following the financial crisis ... because many macro economists using NK models in 2007-8 did not fully appreciate the Keynesian mechanisms present in the model.

Now, if after thirty years of study economists failed to "fully appreciate the Keynesian mechanisms present in the model," one might wonder exactly what such models have to recommend themselves.  What is the advantage of an intellectually demanding and mathematically complex modelling approach that makes it harder to actually get the job done?

The answer, I suspect, is that "intellectually demanding and mathematically complex" has become an end in itself -- that modern macro has become an arena within which to show off technical virtuosity for its own sake.  And the harder we make it look, the tighter the straightjacket, the cleverer we appear when (after long, painful struggle) we finally emerge.

Which is fine as long as the goal is entertainment.  But if the goal is, you know, looking after the economic welfare of seven billion human beings, the whole enterprise begins to look more than a little bit self-indulgent.


(1) Well, in the movies.  In real life, he died in a hospital of peritonitis.  Dammit.  But why let the facts get in the way of a perfectly good analogy?

No, social media is not undervalued in GDP

There's an argument rattling around the Interwebz suggesting that the observed slowdown in productivity growth is, at least in part, a statistical illusion arising from the fact that users don't pay to use Twitter or Facebook.  Or Blogger (to chose a free blogging platform totally at random; I mean, who uses Blogger these days?).

I'm skeptical.

First, let's keep in mind that, when we talk about GDP, we're talking about the market value of goods and services, not their value value.  It's no objection to say that the market value of social media differs from its "real" value -- that's true of literally everything that composes GDP.  It's perfectly valid to argue that GDP is a meaningless construct, at best reifying the current distribution of income and wealth, and at worst merely an exercise in adding apples to oranges because it's easier for our simple brains to think about one number than about two (1).  But it's an entirely different matter to claim that a particular component is being mis-measured.

The usual argument about social media is that, surely, some users would be willing and able to pay to use it.  Ability and willingness to pay for something is, more or less, the definition of market value.  So, to the extent that these users get something they would pay for for free, there is unmeasured economic output being created and consumed.

And that's fine as far as it goes.  However, it's important to keep in mind that a social media property "creates value" for multiple groups of consumers: at minimum, for both users and advertisers.  The total value produced is the sum of the value delivered to all of the consumers.  And that brings us to the question of pricing strategy.

To talk about pricing strategy, I'm going to make up a hypothetical social media property.  My business plan is to combine the decorum and wit of Twitter with Facebook's custom of conducting unauthorized psychological experiments on its users.  Naturally, we will call this transformative platform "TwitBook." (Tagline: "Giving dude-bros and mean girls the social media experience they deserve.")

Now, TwitBook will need to set some prices.  What should we charge users for this unique and innovative experience?  What should we charge advertisers for the opportunity to reach our bizarrely sought-after demographic?  And finally, what should we charge non-participating audience members for the chance to observe the hijinks in real time, much as they might watch a reality TV show or monster truck rally?  OK, I'm kidding about the third one.  Sort of.

To devise this strategy, I'm going to hire the most ruthless greed-heads in sharp suits that I can find.  (Or, as an economist might say, "Assume firms maximize profit, pi, defined as total revenue (TR) less total cost (TC)."  The bit about the sharp suits is, formally, exogenous.)  My greedy minions quickly arrive at two important conclusions.

First, because of the nature of the business, we have unprecedented technical flexibility in price setting.  We can meter literally everything a user does (including interactions with advertisements), and payment processing has never been easier.  This is probably the first industry ever in which implementing a truly optimal price structure is technologically feasible.

Second, there is an inconvenient tension between the prices we charge users and the prices we charge advertisers.  The more we charge our users, the fewer users we will have (because the elasticity of demand is, if not infinite, quite far from zero), and the less we can charge the advertisers.

"Very insightful," I say to my sharp-suited legion of doom, "but I didn't hire you for your penetrating understanding of market mechanisms; I hired you to make me very, very rich.  I don't care whether my money comes from users, advertisers, or space aliens.  Or from spectators, if you can figure out how to make the whole monster truck angle work.  Just find the set of prices that maximizes the total value of the platform."

"You bet, boss," say my compliant lackeys, because that's how my imaginary friends talk.  In my imagination.  Stop looking at me like that.

Soon, they're back with the a pricing structure...and the weird bit is that all of the user-facing prices are zero.  I'm disappointed, because the idea of dude-bros and mean girls literally paying me to run experiments on them made me alarmingly gleeful.  But business is business.

Still, I want an explanation.

"Yes, your highness," say my imaginary business strategists, because now they've started mocking me.  "Our users do value the platform," they explain, as if to a small child, "but not very much, is all."  (If they weren't talking to me as if I were a small child, they'd probably add something clever sounding like, "at the margin."  Jerks.)  "Charging users even the tiniest amount actually reduces the total value of the platform, since the loss of value to the advertisers is measurably higher than the user-revenue we gain."

The upshot of all this is that their advertising revenue is actually a pretty good indicator of the total "value created" by platforms like Facebook and Twitter.  Nobody is leaving "unpriced value" on the table.

But what about consumer surplus?  The fact that the marginal Facebook user places a value of approximately zero on the service doesn't mean that lots of people don't love Facebook.  Surely that must count for something!  But social media is hardly the only industry to yield a large-ish consumer surplus.  Agriculture (a.k.a. "food") comes to mind.  And more generally, the consumer surplus argument is really just another version of the point that "market value is not value value," which is true, but irrelevant to the measurement (or mis-measurement) of GDP.

So I'm inclined to doubt that the growth of social media leads us to systematically underestimate growth, productivity, or the growth of productivity.  In fact, there's a stronger argument for the reverse.

Time is the universally binding budget constraint: there aren't any more hours in the day than there were in 1972 (or in 1066, or in 800 BCE).  Consequently, people's consumption of social media comes at the expense of other activities, notably at the expense of watching broadcast television.

Now, in terms of pricing, TV looks a lot like social media: it's advertising supported and free-to-consume for viewers.  However, there is a major technical difference:  charging users to view broadcast television has been infeasible until very recently.  So, while we know that the observed pricing structure for social media is optimal (or, at least, unconstrained by the feasibility of monitoring and billing for usage), we do not know this for broadcast TV.  That is, the argument that advertiser-supported media is undervalued in the economic statistics applies with much more force to broadcast TV than it does to social media.  And the fact that people turn out to be willing to pay for commercial-supported cable channels suggests that broadcast TV has been undervalued for decades.

So the real story of bias in GDP measurement is the decline of an undervalued product (broadcast TV) in favor of  a properly valued one (social media), which means that economic and productivity growth are both being systematically overstated.

Now, I'm not persuaded that this is, quantitatively, a very important phenomenon.  But it does leave me skeptical of the notion that free stuff on the Internet is somehow concealing a productivity bonanza from the national income and product accounts.




(1) What to make of the fact that I score intellectual laziness as "worse" than disingenuously naturalizing the social status quo is left as an exercise to the reader.

What did Lucas and Sargent mean?

In their response to Benjamin Friedman's comment on their provocative paper, titled "After Keynesian Macroeconomics," (1978) Lucas and Sargent wrote this:

In his concluding paragraph, Friedman objects to our "rhetorical profile," an objection which several others also expressed at the Conference. To illustrate his point, he cites our reference to "wildly incorrect" predictions of Keynesian macroeconometric models, to "the spectacular failure of the Keynesian models in the 1970s," or their "econometric failure on a grand scale." These phrases were intended to refer to a specific and well-documented historical event. In 1970, the leading econometric models predicted that an inflation of 4 percent on a sustained basis would be associated with unemployment rates less than 4 percent. This prediction was not one which was teased from the models by unsympathetic critics; on the contrary, it was placed by the authors of these models and by many other economists at the center of a policy recommendation to the effect that such an expansionary policy be deliberately pursued. 

Source:  After the Phillips Curve (pp. 81-82, emphasis added)

Unfortunately, Lucas and Sargent didn't feel the need to spell out exactly what "historical event" they were talking about, and perhaps it was too "well-documented" to require citations to any actual documents.  But what may have been common knowledge among academic economists in 1978 is lost to the rest of us.

So: does anybody know what 1970 "historical event" Lucas and Sargent were alluding to?  And if it is "well-documented," can someone point me in the direction of this documentation?