Wednesday, September 02, 2015

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!

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.

Monday, August 31, 2015

Non-intuitive Neo-Fisherism

John Cochrane has another excellent post explaining the Neo-Fisherian view of monetary policy. Some key grafs (I think "graf" means "excerpt"):
Why is there so little inflation now? How will a rate rise affect inflation? How can we trust models of the latter that are so wrong on the former? 
Well, why don't we turn to the most utterly standard model for the answers to this question -- the sticky-price intertemporal substitution model. (It's often called "new-Keynesian" but I'm trying to avoid that word since its operation and predictions turn out to be diametrically opposed to anything "Keyneisan," as we'll see.) 
The basic simplest [New Keynesian] model makes a sharp and surprising [Neo-Fisherian] prediction... 
I started with the observation that it would be nice if the model we use to analyze the rate rise gave a vaguely plausible description of recent reality. 
The graph shows the Federal Funds rate (green), the 10 year bond rate (red) and core CPI inflation (blue). 
The conventional way of reading this graph is that inflation is unstable, and so needs the Fed to actively adjust rates...When inflation declines a bit, the Fed drives the funds rate down to push inflation back up...When inflation rises a bit, the Fed similarly quickly raises the funds rate. 
That view represents the conventional doctrine, that an interest rate peg is unstable, and will lead quickly to either hyperinflation (Milton Friedman's famous 1968 analysis) or to a deflationary "spiral" or "vortex."... 
But in 2008, interest rates hit zero...The conventional view predicted that the broom will topple. Traditional Keynesians warned that a deflationary "spiral" or "vortex" would break out. Traditional monetarists looked at QE, and warned hyperinflation would break out... 
The amazing thing about the last 7 years in the US and Europe -- and 20 in Japan -- is that nothing happened! After the recession ended, inflation continued its gently downward trend. 
This is monetary economics Michelson–Morley moment. We set off what were supposed to be atomic bombs -- reserves rose from $50 billion to $3,000 billion, the crucial stabilizer of interest rate movements was stuck, and nothing happened.  
This is a powerful argument, and I think that those who sneer at Neo-Fisherism don't take it seriously enough.

That said, there are some serious caveats. The first is that although the recent American and Japanese experience with QE are powerful pieces of evidence, they are by no means the only pieces of evidence or the only policy experiments. What about the Volcker disinflation, when Fed interest rate hikes were followed by disinflation? I assume there have been at least one or two similar episodes around the world in the last few decades.

Next, are we sure we want to think about interest rate policy as a series of interest rate pegs, each of which people believe will last forever? In the typical New Keynesian model, people believe something much more complicated - they believe that the Fed sets interest rates according to a Taylor-type rule, and monetary policy changes only cause people to change their beliefs when they represent a regime change - i.e. a change in the rule.

But the last reason we should be a little wary of the Neo-Fisherian idea is that it goes against our basic partial-equilibrium Marshallian idea of supply and demand.

Our basic supply-and-demand intuition says that demand curves slope down and supply curves slope up. Dump a lot of a commodity on the market, and its price will fall. Start buying up a commodity, and its price will rise.

Neo-Fisherianism goes against this intuition. Suppose the Fed lowers interest rates. Abstracting from banks, reserves, etc., it does this by printing money and using that money to buy bonds from people in the private sector. That increase in demand for bonds makes the price of bonds go up, and since interest rates are inversely related to bond prices, it makes interest rates go down.

Now, you can write down a model in which this doesn't happen - for example, a model in which Fed money-printing-and-bond-buying stimulates the economy so much that interest rates end up rising instead of falling. But in practice, it looks like the Fed has total control over interest rates (at least, the Federal Funds Rate; let's put aside the question of heterogeneous interest rates).

So when the Fed lowers interest rates, it prints money in order to do so. But in a Neo-Fisherian world, that makes inflation fall - in other words, it makes money more valuable. That's worth repeating: In a Neo-Fisherian world, dumping a ton of new money on the market makes money a more valuable commodity.

That is weird! That totally goes against our Econ 101 intuition! How does dumping money on the market make money more valuable?? Well, it could be one of those weird general equilibrium results, like the "paradox of thrift" or the "paradox of toil". Or it could be because Neo-Fisherians make very strong assumptions about what the fiscal authority does. As Cochrane writes:
One warning. In the above model, the interest rate peg is stable only so long as fiscal policy is solvent. Technically, I assume that fiscal surpluses are enough to pay off government debt at whatever inflation or deflation occurs.  Historically, pegs have fallen apart many times, and always when the government did not have the fiscal resources or fiscal desire to support them. The statement "an interest rate peg is stable" needs this huge asterisk.
This makes sense, and it seems like a good reason to wonder if interest rate policy really is best viewed as a series of pegs. If interest rate pegs historically fall apart because the fiscal authority couldn't do its part in maintaining them, it stands to reason that people wouldn't generally expect the current interest rate target to be permanent. Instead, it might be more reasonable for people to expect something more along the lines of a Taylor-type rule, as in the standard New Keynesian model.

Anyway, Neo-Fisherianism continues to be an interesting idea, but I continue to have serious doubts. I want to see international evidence, and evidence with "high" pegs as well as "low" ones, before I believe we've seen a Michelson-Morely moment. I do agree, however, that everyone who still has a standard, Milton Friedman type concept of how monetary policy affects inflation needs to be doing some serious rethinking right now.


In the comments, Steve Williamson writes:
[I]n the VAR evidence, it can be hard to get rid of the "price puzzle." That was called a puzzle because tight monetary policy led to higher prices. Maybe that's not so puzzling.
He points me to this Handbook of Macroeconomics chapter by Valerie Ramey, whose section 3 concerns VAR studies of monetary policy. Ramey describes the Price Puzzle on p. 27:
Another issue that arose during this period was the “Price Puzzle,” a term coined by Eichenbaum (1992) to describe the common result that a contractionary shock to monetary policy appeared to raise the price level in the short-run... 
Christiano, Eichenbaum, and Evans’ 1999 Handbook of Macroeconomics chapter...summarized and explored the implications of many of the 1990 innovations in studying monetary policy shocks. Perhaps the most important message of the chapter was the robustness of the finding that monetary policy shocks, however measured, had significant effects on output. On the other hand, the pesky price puzzle continued to pop up in many specifications.
So the evidence from the 1990s and earlier says that monetary policy works in the classically expected direction (rate hikes lower inflation, rate cuts boost it), but that in the very short term after a policy change, the direction of the effect is often reversed.

But if you look at Cochrane's Neo-Fisherian impulse response graph, that's exactly the opposite of what Ramey talks about:

In this graph, a rate hike is followed first by an indeterminate or perhaps negative jump in inflation, then by a slow convergence in the direction of the interest rate. But Ramey's summary of the evidence is that a rate hike is followed by an indeterminate or perhaps positive jump in inflation (the Price Puzzle), followed by a longer-term downward movement in inflation. In other words, exactly the opposite of the above graph.

So I still think this is a puzzle for Neo-Fisherism.

Steve also has a post responding to mine. Particularly interesting is the argument that Volcker's rate hikes in the early 1980s actually made the inflation situation worse, and that it was his subsequent rate cuts that actually whipped inflation. I'm probably more open to that story than most people, but I think there are a number of things about it that are very fishy, e.g. the fact that inflation started going down after the rate hikes instead of rising further.

Steve also shows a graph that displays a positive correlation between interest rates and inflation. However, this sort of logic leads would also lead us to believe that going to the doctor is the cause of illness, so I would rather trust the VAR evidence that Ramey cites in the chapter Steve linked to. Of course, I don't trust that VAR evidence that much, since it's hard to get credible structural identification on a VAR.

As a final random fun note, the Ramey chapter - which appears on the Hoover Institute's website - contains the following footnote on page 25:
Of course, this view was significantly strengthened by Kydland and Prescott’s (1982) seminal demonstration that business cycles could be explained with technology shocks.
LOL. RBC gaslighting knows no shame.

Saturday, August 29, 2015

The macro/micro validity tradeoff

Michael Lind wrote an article recently suggesting that universities abolish the social sciences. He unfairly credits me with the term "mathiness", which of course is Paul Romer's thing. But anyway, I tweeted the article (though I disagree with it pretty strongly), and that provoked an interesting discussion with Ryan Decker.

When economists defend the use of mathematical modeling, they often argue - as Ryan does - that mathematical modeling is good because it makes you lay our your assumptions clearly. If you lay out your assumptions clearly, you can think about how plausible they are (or aren't). But if you hide your assumptions behind a fog of imprecise English words, you can't pin down the assumptions and therefore you can't evaluate their plausibility.

True enough. But here's another thing I've noticed. Many economists insist that the realism of their assumptions is not important - the only important thing is that at the end of the day, the model fits the data of whatever phenomenon it's supposed to be modeling. This is called an "as if" model. For example, maybe individuals don't have rational expectations, but if the economy behaves as if they do, then it's OK to use a rational expectations model.

So I realized that there's a fundamental tradeoff here. The more you insist on fitting the micro data (plausibility), the less you will be able to fit the macro data ("as if" validity). I tried to write about this earlier, but I think this is a cleaner way of putting it: There is a tradeoff between macro validity and micro validity.

How severe is the tradeoff? It depends. For example, in physical chemistry, there's barely any tradeoff at all. If you use more precise quantum mechanics to model a molecule (micro validity), it will only improve your modeling of chemical reactions involving that molecule (macro validity). That's because, as a positivist might say, quantum mechanics really is the thing that is making the chemical reactions happen.

In econ, the tradeoff is often far more severe. For example, Smets-Wouters type macro models fit some aggregate time-series really well, but they rely on a bunch of pretty dodgy assumptions to do it. Another example is the micro/macro conflict over the Frisch elasticity of labor supply.

Why is the macro/micro validity tradeoff often so severe in econ? I think this happens when an entire theoretical framework is weak - i.e., when there are basic background assumptions that people don't question or tinker with, that are messing up the models.

For example, suppose our basic model of markets is that prices and quantities are set based purely on norms. People charge - and pay - what their conscience tells them they ought to, and people consume - and produce - the amount of stuff that people think they ought to, in the moral sense. 

Now suppose we want to explain the price and quantity consumed of strawberries. Microeconomists measure people's norms about how much strawberries ought to cost, and how many strawberries people ought to eat. They do surveys, they do experiments, they look for quasi-experimental shifts that might be expected to create shifts in these norms. They get estimates for price and quantity norms. But they can't match the actual prices and quantities of strawberries. Not only that, they can't match other macro facts, like the covariance of strawberry prices with weather in strawberry-growing regions. (A few microeconomists even whisper about discarding the idea of norm-driven prices, but these heretics are harshly ridiculed on blogs and around the drink table at AEA meetings.)

So the macroeconomists take a crack at it. They make up a class of highly mathematical models that involve a lot of complicated odd-sounding mechanisms for the creation of strawberry-related norms. These assumptions don't look plausible at all, and in fact we know that some of them aren't realistic - for example, the macro people assume that weather creates new norms that then spread from person to person, which is something people have never actually observed happening. But anyway, after making these wacky, tortured models, the macro people manage to fit the facts - their models fit the observed patterns of strawberry prices and strawberry consumption, and other facts like the dependence on weather.

Now you get to choose. You can accept the macro models, with all of their weird assumptions, and say "The economy works as if norms spread from the weather", etc. etc. Or you can believe the micro evidence, and argue that the macro people are using implausible assumptions, and frame the facts as "puzzles" - the "strawberry weather premium puzzle" and so on. You have a tradeoff between valuing macro validity and valuing micro validity. 

But the real reason you have this tradeoff is because you have big huge unchallenged assumptions in the background governing your entire model-making process. By focusing on norms you ignore production costs, consumption utility, etc. You can tinker with the silly curve-fitting assumptions in the macro model all you like, but it won't do you any good, because you're using the wrong kind of model in the first place. 

So when we see this kind of tradeoff popping up a lot, I think it's a sign that there are some big deep problems with the modeling framework. 

What kind of big deep problems might there be in business cycle models? Well, people or firms might not have rational expectations. They might not act as price-takers. They might not be very forward-looking. Norms might actually matter a lot. Their preferences might be something very weird that no one has thought of yet. Or several of the above might be true.

But anyway, until we figure out what the heck is, as a positivist might say, really going on in economies, we're going to have to choose between having plausible assumptions and having models that work "as if" they're true.

Saturday, August 22, 2015

A great critique of Rational Expectations

How did I miss this great critique of Rational Expectations? Charles Manski, an econometrician at Northwestern University, published a paper in 2004 in Econometrica looking at the way economists measure expectations. Here is the final working-paper version. Manski spends a lot of his time discussing the possibility of measuring expectations through surveys. But in one section he critiques the idea of Rational Expectations, which is assumed in most economic models. Manski writes:
Suppose that the true state of nature actually is the realization of a random variable distributed P. A decision maker attempting to learn P faces the same inferential problems – identification and induction from finite samples – that empirical economists confront in their research. Whoever one is, decision maker or empirical economist, the inferences that one can logically draw are determined by the available data and the assumptions that one brings to bear. Empirical economists seldom are able to completely learn objective probability distributions of interest, and they often cannot learn much at all. It therefore seems hopelessly optimistic to suppose that, as a rule, expectations are either literally or approximately rational.
Rational Expectations basically say that economic agents behave as if the true model of the economy is the same as the model the economist is currently writing down. But that model includes stochastic processes. And in most situations, it's impossible to pin down the stochastic processes governing the economy - you have to make some guesses. Rational Expectations forces you to assume that economic agents are making all the same guesses you are. That goes way beyond rationality. It is also highly implausible, when you think about it, especially since econometricians themselves will almost always disagree on which guesses are appropriate.

Manski continues:
I would particularly stress that decision makers and empirical economists alike must contend with the logical unobservability of counterfactual outcomes. Much as economists attempt to infer the returns to schooling from data on schooling choices and outcomes, youth may attempt to learn through observation of the outcomes experienced by family, friends, and others who have made their own past schooling decisions. However, youth cannot observe the outcomes that these people would have experienced had they made other decisions. The possibilities for inference, and the implications for decision making, depend fundamentally on the assumptions that youth maintain about these counterfactual outcomes. 
In other words, economic agents just have no physical way of learning about all of the possible outcomes in an economy that never end up happening. 

Here's a simple example. Suppose I think that if I use pachinko machine A, I'll win with a 51% chance and lose with a 49% chance. And suppose that I think that if I use pachinko machine B, I'll win with a 40% chance and lose with a 60% chance. What do I do? I use pachinko machine A every time. Now suppose that I'm right about the odds of machine A (which I confirm by multiple uses), but wrong about machine B. Suppose that machine B actually has odds of 55% win, 45% lose. I should be using machine B, but I never do, so I never find out that I'm wrong, and I keep making the wrong decision! 

Now, if there are lots of people playing on lots of machines and we can all observe each other, it's clear that we'll figure out the odds of all the machines. But many economic models are macro models. The macroeconomy can only make one decision at a time. What would have happened if we had stayed on the gold standard in the Great Depression? We can make guesses, but we'll never really know. So this kind of limited knowledge makes Rational Expectations especially difficult to swallow in the context of macro.

Note that a lot of people think that Rational Expectations becomes a better and better assumption as the economy settles down into a long-term steady state. But the pachinko example above shows how this may not be the case, since in the steady state, the decision maker never learns the truth.

So why does everyone and their dog use Rational Expectations? Manski says that, basically, it's because A) it's easy, and B) there's no obviously better alternative:
Why do economists so often assume that they and the decision makers they study share rational expectations? Part of the reason may be the elegant manner in which these assumptions close an economic model. A researcher specifies his own vision of how the economy works, and he assumes that the persons who populate the economy share this vision. This is tidy and self-gratifying. 
Another part of the reason must be the data used in empirical research. As illustrated in Section 2, choice data do not necessarily enable one to infer the expectations that decision makers hold. Hence, researchers who are uncomfortable with rational expectations assumptions can do no better than invoke some other unsubstantiated assumption. Rather than speculate on how expectations actually are formed, they follow convention and assume rational expectations.
I'd add a third, more cynical reason: Rational Expectations can't be challenged on data grounds. If you measure expectations with surveys, people can poke holes not just in your theoretical model, but in the expectations data that you gathered and the econometric methods that you used to extract a signal from it. But if you assume Rational Expectations, they can only poke holes in the model itself. Basically, substituting theoretical assumptions for empirical results makes a model a more hardened target. If it makes the model less able to fit the data at the end of the day, well..."all models are wrong", right?

Anyway, everyone should go read Manzi's entire paper. Very interesting stuff, even if a decade old.

Thursday, August 20, 2015

Have interest rates actually risen?

The Council of Economic Advisors recently put out a report on the long, steady decline in long-term interest rates over the last two decades. John Cochrane called the report "excellent", and reposts the following graph:

These are government bond yields - they represent the government's cost of borrowing. Steve Williamson, however, notes that the return on government bond yields is not the same thing as the return on capital. He writes:
Some of this discussion seems to work from the assumption that the rate of return on government debt and the rate of return on capital are the same thing...Bernanke appears to think that low real Treasury yields are associated with low rates of return on capital.
Williamson is responding to a quote by Bernanke stating that if (real) interest rates get low enough, investment will eventually be stimulated. Williamson points out the distinction between government borrowing rates and rates of return on capital in order to argue (I think) that pushing down government bond rates will not necessarily induce companies to invest.

In defense of this thesis, Williamson cites a St. Louis Fed report by Paul Gomme, B. Ravikumar, and Peter Rupert, which in turn draws on this 2011 paper by the same authors (though the data series have been updated). Gomme et al. measure what they call the "real return on capital" by dividing an income measure by a measure of book value. Here, via Williamson, is the chart of what they find:

I am not sure whether Gomme et al. are measuring the return on capital correctly here. But I am pretty sure that Williamson is making sort of an error here - or at least overlooking an important distinction. What should matter for business investment is not businesses' return on capital, but the difference between their return on capital and their cost of capital. 

That's just basic corporate finance. If your internal rate of return (basically, your return on capital) is higher than your cost of capital, you buy the capital (i.e. you invest), and you undertake the project. 

Gomme et al.'s time series - whether or not it's a good measure of the return on capital or not - is not a measure of the cost of capital. And if we're comparing government borrowing costs to business borrowing costs, we want to look at the cost of capital. 

Now, there are two basic types of capital, equity capital and debt capital. To find the cost of equity capital - which is an opportunity cost - we need a model of risk. But to find the cost of debt capital is easy - it's just the yield on corporate bonds. So here, via FRED, is the nominal yield on Aaa and Baa corporate bonds:

Here we see the same story that we saw in the CEA graph. Nominal borrowing costs for businesses have been falling more-or-less steadily since the mid-80s.

How about real rates? Here are real rates (annual, not monthly like the previous series):

Again, same exact story.

So real corporate borrowing costs have been falling more-or-less steadily for decades, just like government borrowing costs. Gomme et al.'s work on rates of return on capital does not measure a risk premium, and it does not bear on this basic story.

That does, of course, lead to a little bit of a puzzle: If Gomme et al. measure rates of return on capital correctly, then why haven't falling real costs of capital combined with rising rates of return on capital lead to a business investment boom? Even if the opportunity cost of equity capital (the equity risk premium) went up, wouldn't that just cause an investment boom, accompanies by a shift from equity financing to debt financing? For this reason, I suspect that either 1) Gomme et al. have measured the return on capital incorrectly, or 2) basic corporate finance theory doesn't capture what's going on in our economy, or 3) both. (Note: As Robert Waldmann and many others have pointed out, Gomme et al.'s series is an average rate of return, where what should matter for investment is the marginal rate. So given that, it's not even clear what kind of conclusions we could draw from Gomme et al.'s time series.)

(Fun random tidbit: While I was looking at Gomme et al.'s 2011 paper, I noticed that the 2011 abstract uses the words "A fairly basic real business cycle model". But the 2007 working paper abstract uses the words "The standard business cycle model" to refer to the same thing. Hehehe. The standard business cycle model, eh? Riiiiight...)

Wednesday, August 19, 2015

Science vs. politics

Ever since Paul Romer went on the attack against what he sees as the politicization of growth theory, there has been a lively Twitter discussion about whether and how politics and science should be combined. Should we try to keep politics out of science? And what does that even mean? Sociology grad student Dan Hirschman challenged me to lay out my thoughts in a blog post, so here it is.

One thing I absolutely don't mean by "separate politics and science" is that scientists should refrain from political activism. I think scientists should definitely be free to engage in any political activism they like. I just think that they should try their best to avoid incorporating their activism into their science. To make an analogy, I think particle physicists should refrain from having sex inside a particle collider (cue SMBC comic!), but that doesn't mean I want particle physicists to be celibate.

Another thing I don't mean by "separate politics and science" is to claim that it is possible to do this 100%. It is inevitable that scientists' political views will sometimes seep into their assessment of the facts. But just because it's inevitable doesn't mean it's desirable. To make an analogy, every desk has some water particles on it, but that doesn't mean you shouldn't dry off your desk if you spill some water on it.

So that's what I don't mean. On to what I do mean.

I'm making a normative statement - I'm telling scientists what I think they ought to do. More specifically, I'm telling them about what they ought to try to do. I'm telling them what I think their objective function ought to be when they do science. In econ-ese, I have preferences over their preferences.

I'm assuming that there's a fundamental difference between factual assessments and desires. They affect each other, sure - no one is totally objective, and people's desires are also shaped by what they think is possible. But they aren't the same thing.

I'm saying that when doing science, people ought to try to ignore their desires and just assess facts. Basically, they should try to be as objective as they possibly can.

To be more precise, I think there ought to be an activity called "science" that consists only of people trying as hard as they can to ignore all desires and just assess facts. 

Now you might ask: "Noah, why do you think there ought to be such an activity?"

Well, I could just reply that it's purely my moral intuition, and as a Humean, I don't need any other justification. In fact, any justification I give will open itself up to questions of "But why?", until I finally just say "Because that's just how I feel", or "Oh come ON!". But just for fun, let me try to explain some of the "good" consequences I think will generally result from people following my science-and-politics norm.

Basically, I think societies where scientists obey this norm will generally be more effective - whatever their goals - then societies that don't. For example, suppose there are two societies, Raccoonia and Wombatistan, and both are suffering from lots of bacterial diseases. Both countries generally subscribe to a religion that says that invisible gnomes cause disease. But Raccoonia is committed to the norm of science that I described above, while in Wombatistan people think that politics and science should be mixed. In Raccoonia, scientists put aside their religion and discover that antibiotics fight bacterial disease, while in Wombatistan, scientists publish papers calling the Racconian papers into doubt, and arguing for gnome-based theories. Raccoonia will discover the truth more quickly and manage to save a lot of its people.

WAIT!, you say. Isn't the goal of stopping disease itself a political goal? Well, sure. There's a clear division of labor here: The politicians tell the scientists a goal ("Find the cause of disease!"), and the scientists pursue the goal (actually, the scientists could even assign themselves the goal for political reasons, then try to disregard politics while pursuing it, and they'd still be following my norm). When the scientists go into a "science mode" in which they disregard all political considerations, they are more effective in reaching the goal.

This norm I'm suggesting won't solve all of society's problems, obviously, because that depends on what you think is a problem. If you have bad politics - for example, if you think disease is a just punishment for sins and shouldn't be cured - then all the scientific discoveries in the world won't help you much (I think the Soviets kind of demonstrated this). But whatever your goals, following my norm of science will make you more effective in accomplishing them.

Now, I don't think this norm is universal and overriding. I'm a Humean, not a deontologist - I have no need to establish a priori moral axioms that encompass all situations. I can think of extreme situations where I'd violate this norm. If the Nazis tell you to build a nuke, go ahead and sabotage that project!

But I think that in the long term, the human race benefits from being able to do more things, not less. Fundamentally, that's what my science norm is all about - empowering the human species as a whole. Over the long term I trust the human species with power. Your mileage may vary.

Saturday, August 08, 2015

The backlash to the backlash against p-values

Suddenly, everyone is getting really upset about p-values and statistical significance testing. The backlash has reached such a frenzy that some psych journals are starting to ban significance testing. Though there are some well-known problems with p-values and significance testing, this backlash doesn't pass the smell test. When a technique has been in wide use for decades, it's certain that LOTS of smart scientists have had a chance to think carefully about it. The fact that we're only now getting the backlash means that the cause is something other than the inherent uselessness of the methodology.

The problems with significance testing are pretty obvious. First of all, p-hacking causes publication bias - scientists have an incentive to keep mining the data until they get something with a p-value just under the level that people consider interesting. Also, significance testing doesn't usually specify alternative hypotheses very well - this means that rejection of a null hypothesis is usually over-interpreted as being evidence for the researcher's chosen alternative.

But the backlash against p-values and significance testing has been way overdone. Here are some reasons I think this is true.

1. Both of the aforementioned problems are easily correctable by the normal practices of science - by which I mean that if people are doing science right, these problems won't matter in the long run. Both p-hacking and improperly specified alternatives will cause false positives - people will think something is interesting when it's really not. The solution to false positives is replication. Natural sciences have been doing this for centuries. When an interesting result comes out, people 1) do the experiment again, 2) do other kinds of experiments to confirm the finding, and 3) try to apply the finding for a bunch of other stuff. If the finding was a false positive, it won't work. The person who got the false positive will take a hit to his or her reputation. Science will advance. My electrical engineer friends tell me that their field is full of false positives, and that they always get caught eventually when someone tries to apply them. That's how things are supposed to work.

2. Significance tests results shouldn't be used in a vacuum - to do good science, you should also look at effect sizes and goodness-of-fit. There is a culture out there - in econ, and probably in other fields, that thinks "if the finding is statistically significant, it's interesting." This is a bad way of thinking. Yes, it's true that most statistically insignificant findings are uninteresting, but the converse is not true. For something to be interesting, it should also have a big effect size. The definition of "big" will vary depending on the scientific question, of course. And in cases where you care about predictive power in addition to treatment effects, an interesting model should also do well on some kind of goodness-of-fit measure, like an information criterion or an adjusted R-squared or whatever - again, with "well" defined differently for different problems. Yes, there are people out there who only look at p-values when deciding whether a finding is interesting, but that just means they're using the tool of p-values wrong, not that p-values are a bad tool.

3. There is no one-size-fits-all tool for data analysis. Take any alternative to classical frequentist significance testing - for example, Bayesian techniques or machine learning approaches - and you'll find situations where it works well right out of the box, and situations where it has to be applied carefully in order to give useful results, and situations in which it doesn't work nearly as well as alternatives. Now, I don't have proof of this assertion, so it's just a conjecture. If any statisticians, data scientists, or econometricians want to challenge me on this - if any of y'all think there is some methodology that will always yield useful results whenever any research assistant presses a button to apply it to any given data set - please let me know. In the meantime, I will continue to believe that it's the culture of push-button statistical analysis that's the problem, not the thing that the button does when pushed.

Fortunately I see a few signs of a backlash-to-the-backlash against significance testing. In 2005 we had a famous paper that used simulations to show that "most published research findings [should be] false". Now, in 2015, we have a meta-analysis showing that the effect of p-hacking, though real, is probably quantitatively small. In addition, I see some signs on Twitter, blogs, etc. that people are starting to get tired of the constant denunciation of significance testing - it's more of a hipster trend than anything. Dissing p-values in 2015 is a little like dissing macroeconomics in 2011 - something that gives you a free pass to sound smart in certain circles (and as someone who did a lot of macro-dissing in 2011, I should know!). But like all hipster fads, I expect this one to fade.

Wednesday, August 05, 2015

Iran is weak

I'm no international relations expert, of course, but I think there's one big mistake that's made in discussions about Iran these days. The presumption is that Iran is a rising power, at the peak of its influence. The basic story is that Iran is ascendant in the Middle East because its main threat, Saddam Hussein, has been removed and replaced with a Shia regime sympathetic to Tehran. In addition, the story goes, Iran has a strong network of regional allies - Hezbollah, the Assad regime, and the Houthi rebels in Yemen. The theory of Iranian strength is often put forth by those who oppose Obama's deal with Iran; these opponents seem to think that the deal would strengthen an already rising power, pushing Iran into a firm position of regional supremacy.

But I believe that the theory of Iranian strength is wrong. Iran is in an extremely weak position, and is poised to get weaker, even with the U.S. deal.

Here are four reasons I think Iran is weak.

Reason 1: Unwinnable Proxy Wars

Iran is now involved in three major proxy wars: the Assad regime's war against the Syrian rebels, the Iraqi government's war against ISIS, and the Houthis' war against the Saudi-backed Yemeni government.

Proxy wars take lots of money and effort. The Iranian public has no real reason to bear these costs, except perhaps in the case of Iraq, and will probably get progressively dissatisfied as they go on. But they will go on, because there is little chance that Iran can actually win any of these three wars. The Houthis are too small in number, and too close to Saudi Arabia, to ever control Yemen. The Iraqi government shows essentially zero ability to pacify the Sunni western areas of the country. And Assad is probably doomed.

None of these three unwinnable proxy wars is equivalent to a Vietnam or an Afghanistan, because only a few Iranian troops are actually fighting. But supporting proxies costs money, and Iran does not have a lot of money to spare. In addition, the loss of Assad will rob Iran of its most powerful regional ally, and the Syrian rebels (or ISIS) may then move on to pressuring Iran's other powerful ally, Hezbollah.

In other words, the military situation looks very bad for Iran.

Reason 2: Many Rivals, No Allies

Iran is surrounded by rivals. There are the openly hostile Saudis to the southwest. There are the Sunni Turks to the northwest, a traditional rival that is now working to overthrow Iran's ally Assad. To the east looms the giant unstable Sunni country Pakistan. And western Iraq and eastern Syria are filled with Sunni Arabs who have very unfavorable opinions of Iran.

Basically, Iran is surrounded:

So who are Iran's big allies? China can be counted on only for intermittent, lukewarm backing, probably motivated purely by China's desire to buy Iranian oil. Russia has been mooted as an Iranian ally despite their history of enmity, but Putin has his hands full with Ukraine, and it's not clear whether Russia would lift a hand to help Iran against its real threats, i.e. the various Sunni populations that surround it. It certainly hasn't done so in the past, and shows no inclination to do so now.

Reason 3: Poor Economic Outlook

Iran has a sclerotic and oil-cursed economy. Thanks to the U.S. shale revolution, oil prices - currently pretty low - are not forecast to rise much, since every time they rise, U.S. shale production will surge and force them back down. In the longer-term future - two or three decades from now -  electric vehicles will start becoming prevalent, driving down the demand for oil.

In other words, Iran's economy is kind of screwed, unless it can wean itself off oil. But in the best of worlds, that takes time and effort, and Iran is not living in anywhere close to the best of worlds - its economy is dominated and choked by the mafia-like Revolutionary Guard.

Perhaps this is one reason why Iranian military spending is so low:

Not exactly the spending profile of a rising regional power, and certainly not of a dominant regional power.

Reason 4: Declining Demographics

Here, via Index Mundi, is a chart of Iran's Total Fertility Rate:

For 15 years, Iran's fertility has been below 2.1, which is the replacement level. In other words, Iran is rapidly running out of young men to fight wars. When Saddam attacked Iran in the 1980s, it could throw near-endless waves of young men into the fray; now, that is impossible.

Meanwhile, Iraq's TFR is listed at 3.41, Pakistan's at 2.86, Syria's at 2.68, and Saudi Arabia's at 2.17. In other words, all of Iran's enemies and threats have populations that are growing faster than Iran's.

So Iran is out of friends, out of money, out of young men, and out of options in its numerous proxy wars. This is not a strong, ascendant regional power. This is a weak, threatened, isolated country living on borrowed time. Seen in this light, Obama's offer of rapprochement looks less like the capitulation its opponents allege - and more like a lifeline.

Monday, August 03, 2015

Translating Paul Romer

I love what Paul Romer is doing on his blog. It's great to see someone recounting the old 80s/90s macro debates, and criticizing the "freshwater" folks, who is not a "Keynesian". I mean, Romer was in the thick of it all. He got his PhD from Chicago in 1983, and Lucas was his advisor! Plus he's been working in macro for decades. If Romer doesn't know what was up with the Macro Wars of the 80s and 90s, nobody does. And he's telling it like he sees it and pulling no punches. As The Dude would say, that's far out.

The problem is that Romer's writing style is not exactly ideal for what he's doing. He's writing blog posts like academic papers - very careful to specify definitions, cover all bases, and speak in a dry, neutral tone. That makes sense when you're writing a paper, because you're writing to a highly selective audience - i.e., the tiny handful of people in the world who are specialized enough to care about your research topic, and experienced enough to know all the background. It also makes sense because of the way papers get published - it's a long process, and you can't easily go back and change things, write addenda, write quick follow-ups, respond to arguments point-by-point, etc. Blogging is a whole different animal. Romer could get his message across more effectively, I think, if he would just take greater advantage of the format.

For example, let me attempt to translate a Paul Romer post into blog-ese. The post is called "Freshwater Feedback Part 1: “Everybody does it”". That's an OK title, though I would have gone with "justification" instead of "feedback" ("excuse" being a little too antagonistic). Anyway, I love this post. But it could still use some translating. Here, let's begin.

Romer says:
You can boil my claim about mathiness down to two assertions: 
1. Economist N did X.
2. X is wrong because it undermines the scientific method. 
#1 is a positive assertion, a statement about “what is …”#2 is a normative assertion, a statement about “what ought …” 
And now, in blog-ese:
Basically, my "mathiness" claim comes in two parts. First, I'm trying to expose some things I see some economists doing. Second, I'm trying to explain why I think those things are wrong, because they undermine the scientific method. 
As you would expect from an economist, the normative assertion in #2 is based on what I thought would be a shared premise: that the scientific method is a better way to determine what is true about economic activity than any alternative method, and that knowing what is true is valuable. 
In conversations with economists who are sympathetic to the freshwater economists I singled out for criticism in my AEA paper on mathiness, it has become clear that freshwater economists do not share this premise. What I did not anticipate was their assertion that economists do not follow the scientific method, so it is not realistic or relevant to make normative statements of the form “we ought to behave like scientists.” 
In a series of three posts that summarize what I have learned since publishing that paper, I will try to stick to positive assertions, that is assertions about the facts, concerning this difference between the premises that freshwater economists take for granted and the premises that I and other economists take for granted.
I personally believe very strongly in the scientific method. I thought that pretty much all economists believed in it. But in my conversations with economists who are sympathetic to the freshwater folks I criticized in my "mathiness" paper, I realized that a lot of them don't actually believe in the scientific method at all. Or at least, they don't believe economists ought to follow it! 
As you might expect, I was pretty surprised to find this out. Surprised, and dismayed. So I'm going to write three posts about my conversations with these people. I'll try my best to stick to the facts. I just want to explain how these people think differently from the way I do.
In my conversations, the freshwater sympathizers generally have not disagreed with my characterization of the facts in assertion #1–that specific freshwater economists did X. In their response, two themes recur: 
a) Yes, but everybody does X; that is how the adversarial method works. 
b) By selectively expressing disapproval of this behavior by the freshwater economists that you name, you, Paul, are doing something wrong because you are helping “those guys.” 
In the rest of this post, I’ll address response a). In a subsequent post, I’ll address response b). Then in a third post, I’ll observe that in my AEA paper, I also criticized a paper by Piketty and Zucman, who are not freshwater economists. The response I heard back from them was very different from the response from the freshwater economists. In short, Piketty and Zucman disagreed with my statement that they did X, but they did not dispute my assertion that X would be wrong because it would be a violation of the scientific method.
The freshwater sympathizers generally don't deny that the freshwater people do the things I accused them of doing. Instead, their response was basically "Everybody's doing it." Also, they told me that by calling them out on it, I was giving aid and comfort to the forces of darkness.
For right now, let's talk about the defense that "Everybody's doing it."
Together, the evidence I summarize in these three posts suggests that freshwater economists differ sharply from other economists. This evidence strengthens my belief that the fundamental divide here is between the norms of political discourse and the norms of scientific discourse. Lawyers and politicians both engage in a version of the adversarial method, but they differ in another crucial way. In the suggestive terminology introduced by Jon Haidt in his book The Righteous Mind, lawyers are selfish, but politicians are groupish. What is distinctive about the freshwater economists is that their groupishness depends on a narrow definition of group that sharply separates them from all other economists. One unfortunate result of this narrow groupishness may be that the freshwater economists do not know the facts about how most economists actually behave.
First, a pessimistic note. I've come to realize that freshwater economists are just...different. I'm more and more convinced that the freshwater people are used to playing politics instead of doing science. I'm talking about academic politics, of course, not national politics - you know, where "the battles are vicious because the stakes are so low."  
The sad thing is that the freshwater folks think they're no more political than everyone else. They really do believe that "everybody's doing it." They're wrong, but that's what they think.
In my informal conversations, no one from the freshwater camp has articulated exactly what they mean by the adversarial method, so I’ll try to fill in the blanks here. (It would be helpful if someone who supports this view indicated whether I have characterized it accurately.) 
In an equilibrium where everyone follows the adversarial method, each side tries to make the best possible case for its position. What we might call the rules of evidence are that an advocate cannot make statements that are false, but it is to be expected that an advocate will withhold information that does not support the advocate’s position.
So if the freshwater camp doesn't follow the scientific method, what do they follow? I call it the "adversarial method". The adversarial method is where each side tries to make the best possible case for its position - not lying, not saying anything false, but omitting any evidence that might support an alternative point of view.
As always, it helps to be specific. One of the issues that I raised in a conversation concerned the support (in the mathematical sense) of the distribution of productivity in the Lucas-Moll model. Their assumption 1 states that at time 0, the support for this distribution is an unbounded interval of the form [x,infinity). In response to objections of the general form “this assumption is unacceptable because it means that everything that people will ever know is already known by some person at time 0,” Lucas and Moll present a “bounded” model in which the support for the distribution of productivity at time zero is [x,B]. Using words, they claim that this bounded model leads to essentially the same conclusions as the model based on assumption 1. (I disagree, but let’s stipulate for now that this verbal claim is correct.) I observed that Lucas and Moll do not give the reader any verbal warning that because of other assumptions that they make, the support for the distribution of productivity jumps discontinuously back to [x,infinity) at all dates t>0 so it is a bounded model in only the most tenuous sense. 
When I suggested that this omission concerning the behavior of the support of the distribution was a sign of something wrong, the response I got was that in presentations of the paper, members of the audience and/or discussants pointed out that this is how the support behaves. This implied that it was evident to someone who digs into the math that the support for the distribution is bounded only at date 0 and not at any other date. Because this result could be inferred from a careful examination of the math, the authors met their obligations to the reader. Because the support is bounded at date 0, they did not, technically, make a false statement when they use the word “bounded” to describe the model’s behavior. Beyond this, they should not be expected to say in words anything that would weaken their argument. 
I defined mathiness as a combination of words and symbols in which the meaning of the words diverges from the meaning implied by the symbols. One of the ways they can diverge is if the words selectively disclose only some of the formal results. Calling their model “bounded” is an example of this type of divergence.
The freshwater sympathizers agreed, for example, that Lucas and Moll strategically refrained from verbal disclosures about some of the properties of the underlying mathematical formalism. Where we disagreed was whether this was a sign of behavior by the authors that is wrong. In effect, their response was caveat emptor; this is what all economists do.
For example, I was talking to someone about the Lucas-Moll model that I complained about earlier. Lucas and Moll assume that at time 0, the distribution of productivity has support over the interval [x, infinity) - in other words, that everything that can possibly be known is already known by someone out there. That's totally unrealistic. In the real world, there are a bunch of things that nobody knows yet. 
So then Lucas and Moll claim that they have a "bounded" version of the model, where at the beginning, only a finite number of things are known - the productivity distribution starts out with support [x,B] at time 0. They say this model leads to the same conclusions as the earlier one (I disagree, but that's a story for another day). But here's the trick that Lucas and Moll soon as time starts, the distribution of productivity instantly goes back to [x, infinity)! In other words, in the microsecond after time begins, humanity discovers an infinite number of new things! 
If that isn't mathiness, I don't know what is. 
The thing is, Lucas and Moll stick this assumption in there with zero warning to the reader. If you don't look incredibly closely and dig through the math, you never even notice that this is what they're doing. To take a crazy-sounding assumption like this and just bury it in the math and hope no one notices it - well, that's mathiness, right there. 
So I complained about this to a freshwater sympathizer. (S)he told me that, well, this issue came up in seminars, so people definitely noticed it. And if people noticed it, (s)he said, I shouldn't be worrying. Did I expect Lucas and Moll to go out of their way to point out this flaw to readers? 
Well...yeah, I kind of did. 
Science shouldn't be a caveat emptor situation. If you're a scientist, you have a duty to point out the weak points in your arguments instead of forcing people to go hunting around for them.
For me, the most interesting thing to emerge out of these conversations was the realization that the difference between us, which seems like an intractable difference in values or beliefs about right and wrong, may simply reflect different inferences the facts. We agreed about what about what Lucas and Moll did but we disagreed about what other economists do. 
One way to characterize the underlying disagreement about what is wrong is to say that we are both commenting on strategic interaction between economists that takes the form of a repeated, multi-player prisoner’s dilemma. In this game, following the scientific method corresponds to cooperation; following the adversarial method corresponds to defection. My claim is that economics is characterized by a trigger strategy/reputational equilibrium that sustains the scientific method. In calling attention to defection by the freshwater economists, I am following a strategy that is equilibrium play in this reputational equilibrium. 
In contrast, the freshwater economists believe that we are already in the noncooperative adversarial equilibrium, so it is wrong to express disapproval of economists who are simply engaging in the type of behavior that is rational in that equilibrium. The freshwater economists might agree that in some first-best sense, an equilibrium based on the scientific method would be preferable, but they apparently believe that we are not in such an equilibrium; that it is not possible to get back to such an equilibrium; and that even if we did, it would not be possible to sustain it. 
If so, what looks superficially like a deep and intractable disagreement about values or morality may simply reflect disagreement over the facts about what most economists do. When the freshwater types say “everybody is following the adversarial method,” what they may honestly be saying is that “everybody I know is following the adversarial method and they all believe that everyone else is doing this too.” But because freshwater economists have so sharply isolated themselves from the rest of the profession, they may be generalizing from an unrepresentative set of observations. 
To me, the facts seem to be that freshwater economists are following a coordinated strategy based on the adversarial method yet that many other economists are still committed to the scientific method.
So what explains the difference between my approach - which I think is the mainstream approach in econ - and this adversarial freshwater strategy? I have a little pet theory as to why they do what they do. 
Basically, scientific dialogue is like a repeated, multi-player prisoner's dilemma. Following the scientific method is "cooperation"; following the adversarial method is "defection". If you know about repeated multi-player prisoner's dilemmas, you know about trigger strategies and reputational equilibria
I've always followed the scientific method because I thought I was in a cooperative reputational equilibrium. I pointed out the weaknesses of my theories without being prompted, because I thought everyone else would do the same for me. But the freshwater folks didn't share my beliefs. They think they're in a noncooperative adversarial equilibrium, where everyone will always defect, defect, defect. They might wish that everyone followed the scientific method, but they think no one does, so they better not follow it either. They're simply carrying out their rational best responses, given their cynical, pessimistic beliefs about the world. 
So these freshwater folks aren't bad guys. They're just cynical. We don't disagree about values, we just disagree about the facts. They've isolated themselves from the rest of the profession - walled themselves off inside their little freshwater bubble - so they don't realize that in the outside world, people are playing a much friendlier, more cooperative game. 
If I could persuade the thoughtful freshwater types of just one point, it would be that they owe it to themselves to assess the factual basis for their belief that all economists follow the adversarial method instead of the scientific method. Everyone they talk to may honestly believe this, but a bit of empirical inquiry may reveal that this is a fact about whom they talk to, not a fact about the behavior of most economists.
If I could tell the freshwater economists just  one thing, it would be that the rest of economics is doing things differently. Really. We're out here being honest with each other, trying to get to the truth together, not politicking for our own pet theories. We're being scientists. You can too. If you get outside your bubble, you'll see I'm telling the truth.
Anyway, so that's my translation of a Paul Romer post. The cool thing is that, due to the magic of blogs, if I've translated anything incorrectly or incompletely, I can just go back and correct it!

Also, remember that I'm just the translator. I don't necessarily endorse the things Romer says here. Personally, I think his theory about the freshwater folks sounds a bit far-fetched - it relies on some pretty persistent heterogeneous beliefs. Surely the freshwater folks wouldn't take too long to realize that they were doing things very differently than others...

Update: Romer's more recent posts are adopting a more bloggy tone, and - in my opinion - are much more fun and readable!

Sunday, August 02, 2015

Is radical leftism a trap for minorities?

I was struck by Cornell West's negative reaction to Ta-Nehisi Coates' new book, Between the World and Me. This line in particular caught my attention:
Coates can grow and mature, but without an analysis of capitalist wealth inequality, gender domination, homophobic degradation, Imperial occupation (all concrete forms of plunder) and collective fightback (not just personal struggle) Coates will remain a mere darling of White and Black Neo-liberals, paralyzed by their Obama worship[.]
I've seen a bit of this idea among humanities folks before - the idea that the only way that racial minorities will win true freedom is with a revolution that overthrows capitalism.

I kind of think that this idea is a trap that helps keep minorities down.

First of all, I agree with Jamelle Bouie that racial disparities in America - and everywhere, really - are about a lot more than class. Attempts to define the struggle of black people for social equality as simply one more case of the eternal Marxian struggle of the proletariat against the capitalist overclass fundamentally miss a lot of the important reasons why black people struggle in America. It's not just because they're poor and capitalism hurts the poor. (This is also the glum conclusion of the protagonist in the novel Invisible Man, who joins a communist-type organization called the Brotherhood, only to realize that racism can't really be understood through the lens of class conflict.)

But also, taking a historical perspective, I doubt that the strategy of anti-capitalism will do anything to help minorities. The example I'm thinking of is my own ancestors: Jews in Europe. Now, Jews were not a racial minority per se, but in an age when religion was mostly inherited, they were somewhat similar to one. European Jews were persecuted for millennia - regularly attacked and massacred, excluded from many types of economic of activity, kept from holding political power, etc.

European Jews mostly responded to this with nonviolence. Instead of defending themselves from regular attacks, they routinely fled. Instead of trying to overthrow the government, they isolated themselves in secluded communities. Instead of trying to redistribute wealth to themselves by militant force, they engaged in commerce, attempting to get rich in industries like baking and jewelry.

This approach - an early version of what you might call a "model minority" strategy - seems not to have worked very well, at least for a long time. Many Jews got rich - so much so that Jews developed a stereotype as being wealthy - but the massacres and exclusion continued in many places.

Some European Jews took a different tack at the beginning of the modern age. They signed on to the new international communist/socialist movement that was sweeping the continent. Some Jews, like Marx, even helped define the movement. Eventually, this movement turned into violent anti-imperialist revolution in Russia. Many Jews, like Leon Trotsky, joined the Russian Revolution and helped successfully overthrow imperialism.

Unfortunately, this didn't really work either. Jews continued to suffer extreme and often violent discrimination and exclusion in the Soviet Union. The leftist gambit failed - it turned out that social inequality was about a lot more than the imperialist system. It seems pretty clear that a similar thing would happen with black people in America if we ever experienced our own version of the Russian Revolution. Cornell West's anti-capitalist "fightback" would be a disaster for black people.

So what did eventually work for Jews? Moving to tolerant societies. In the Netherlands and England, discrimination still existed, but there were no massacres. Eventually, as societies became richer and more democratic, even social exclusion was reduced. Britain even had a Jewish prime minister - Benjamin Disraeli. In the modern day, many Jews moved to the United States, where anti-semitism was never more severe than the various other frictions between ethnic and religious groups.

And within these tolerant societies, Jews (mostly) didn't try to overthrow capitalism - they worked within the system, doing essentially the same thing they had done in medieval Europe. But with the advent of modern capitalism, this strategy bore a lot more fruit than before. Jews have, overall, flourished economically in the U.S. without suffering the discrimination and violence that used to accompany it.

Obviously, direct application of the "model minority" solution is not going to work for African Americans, since this country for historical reasons has entrenched discrimination against black people in a way that it doesn't have against Jews (or Asians, or Hispanics, or Italians, etc.).

But anti-capitalist revolt is not going to work either. It's a seductive mirage that will only destroy those who chase after it. Capitalism isn't a cuddly, friendly system, but its destruction tends to lead to things far more baleful.

Leftism, as a philosophy and worldview, has suffered enormous setbacks in the past few decades, because communist countries both A) collapsed, and B) were revealed as being nightmarish to live in. A natural strategy for proponents of hardcore leftism - at least, those who choose not to do the sensible thing and moderate their views - would seem to be to try to co-opt oppressed racial minorities, telling them that their social exclusion is due to capitalism.

But ultimately, hopping on the radical leftist boat will hurt minorities. And I suspect that leftists in the humanities are doing minorities no favors by trying to convince them that radical leftism is their only hope, when in fact it is a self-defeating strategy.

So what will work? If history is any guide, the only option is to increase tolerance. I don't pretend to know how to increase tolerance. For immigrant groups, it seems to naturally fade over time, especially if those groups 1) organize to fight discriminatory policy, and 2) make a bunch of money. For African-Americans, intolerance seems much more entrenched. I don't pretend to know how to get rid of it, but I am pretty sure that a militant overthrow of capitalism would make things much, much worse.

Tuesday, July 28, 2015

Big Data vs. Big Gladwell

Here is a news article about a Malcolm Gladwell speech. This news article is of great interest to me, since it suggests that it's not actually very hard to build a lucrative career going around and giving knowledgeable-sounding speeches about concepts, technologies, or companies that are in the news. I could do that job. Dear readers, you know I could do that job. 

A more minor reason that this article is of interest to me is that it gives me a chance to do a snarky point-by-point refutation, which is something I have to do periodically or else go (more) insane. So let's go through and count some of the silly things that Malcolm Gladwell is quoted in this article as having said.
Last night futurist, journalist, prognosticator, and author Malcolm Gladwell told pretty much the most data-driven marketing technologist crowd imaginable that data is not their salvation. 
In fact, it could be their curse.

So how is data our curse?
“More data increases our confidence, not our accuracy,” [Gladwell] said at mobile marketing analytics provider Tune’s Postback 2015 event in Seattle. “I want to puncture marketers’ confidence and show you where data can’t help us.”
Except sometimes more data does increase our accuracy. For example, you can have an estimator that is asymptotically unbiased, but biased in small samples. So Gladwell is totally wrong about that.

Next, Gladwell tells us about the "Snapchat Problem":
The average person under 25 is texting more each day than the average person over 55 texts each year, Gladwell says. That’s what the data can tell us. 
What it can’t tell us is why. 
“The data can’t tell us the nature of the behavior,” Gladwell said. “Maybe it’s developmental … or maybe it’s generational.”
Well that particular piece of data won't tell you, but maybe others could. For example, you could use regional/national variation in the time that countries got smartphone service, and compare Snapchat uptake among age-matched cohorts. 

Of course, that is a different piece of data than the one Gladwell cited. Does Gladwell think it is a significant, penetrating insight to point out that for different questions, you may need different data sets? When Gladwell calls data a "curse", is he using the word "curse" to mean "something that you might need more than one of in order to be omniscient"?

Developmental change, in Gladwell’s story, is behavior that occurs as people age...Generational change, on the other hand, is different. That’s behavior that belongs to a generation, a cohort that grows up and continues the behavior...The question is whether Snapchat-style behavior is developmental or behavioral. 
“In the answer to that question is the answer to whether Snapchat will be around in 10 years,” Gladwell said.
No, that will most certainly not tell us whether Snapchat will be around in 10 years. For example, suppose Snapchat is "developmental", so that young people like it more than old people. Well, there is a constant new supply of young people. But suppose instead that Snapchat is "generational", so that people who grow up with it like it. Well, why wouldn't new generations like growing up with it just as much as old generations did? So even if we answer Gladwell's question, it does not, in fact, tell us much about the future of Snapchat.

Next, Gladwell tells us about the "Facebook Problem":
“Facebook is at the stage that the telephone was at when they thought the phone was not for gossiping — it’s in its infancy,” Gladwell said... 
The diffusion of new technologies always takes longer than we would assume, Gladwell said. The first telephone exchange was launched in 1878, but only took off in the 1920s. The VCR was created in the 1960s in England, but didn’t reach its tipping point until the 1980s... 
Technologies that are both innovative and complicated, like Facebook, take even longer to really emerge.
Except that this doesn't apply to Facebook, because everyone already uses Facebook. Yes, there was a period in time when social networks - Friendster, Myspace - were not widely used. That era is now in the past. People may find new ways to use Facebook, but it's not in its infancy - it has already experienced near-universal uptake. Discussing when Facebook might "really emerge" is like discussing when television might "really emerge".

Finally, Gladwell tells us about the "Airbnb Problem":
The sharing economy, featuring companies like Airbnb, Uber/Lyft, even eBay, rely on trust... 
And yet, if you look at recent polls of trust and trustworthiness, people’s — and especially millennials — trust is at an all-time low. Out of ten American “institutions,” including church, Congress, the presidency, and others, millennials only trust two: the military and science... 
That’s conflicting data. And what the data can’t tell us is how both can be true, Gladwell said...“So which is right? Do people not trust others, as the polls say … or are they lying to the surveys?”
So is it a contradiction if people trust the clocks on their cell phones but distrust Vladimir Putin? Is it a contradiction if people trust their neighbors but distrust the mafia? Are data contradictory whenever they show differing levels of aggregate trust in different people, institutions, or objects? And in general, why should trust in institutions be correlated with trust in other individuals?

What really startles me is that people trust Malcolm Gladwell to say useful things at marketing conferences. 

Anyway, generating such jaw-dropping nonsense must get tiring, so Gladwell falls back on some good old tried-and-true incorrect facts:
[Gladwell said there has been] a massive shift in American society over the past few decades: a huge reduction in violent crime. For example, New York City had over 2,000 murders in 1990. Last year it was 300. In the same time frame, the overall violent crime index has gone down from 2,500 per 100,000 people to 500. 
“That means that there is an entire generation of people growing up today not just with Internet and mobile phones … but also growing up who have never known on a personal, visceral level what crime is,” Gladwell said. 
Baby boomers, who had very personal experiences of crime, were given powerful evidence that they should not trust.
Except here is a chart of U.S. homicide rates:

You'll see that when Baby Boomers were young (under 20), there was even less homicide (and other crime) than when Millennials were under 20. Oops.

Also, Gladwell's statement that young people don't know "what crime is" ignores the fact that U.S. crime rates are still many times what they are in other countries. It's just an obviously false statement.

Also, just to be complete I should note that if Gladwell were right, regions that experienced much less of a crime spike in the 70s and 80s should have higher Airbnb use among Baby Boomers. But I think we've seen very high uptake in, say, Northern California and the Pacific Northwest, where the crime boom was much less severe. However, rigorous analysis (with yes...gasp...DATA!) would be able to answer this question more definitely.

Folks, there are many important cautions to be made about the use of Big Data. These are not they.

And now, finally, just for fun, we have the Coup de Gladwell:
“I think millennials are very trusting,” Gladwell said. “And when they say they’re not...they’re bullshitting.”
And there you have it, folks. Who needs data when you have Gladwellian Pronouncements. The future is not the era of Big is the era of Big Gladwell. 

Now if only we could put Gladwell's insight in an app and sell it...