Economist's View

Unemployment, Vacancies, and Inflation Since 1951: The Movie

This is from Roger Farmer's "Farewell to the natural rate: Why unemployment persists." He argues that "the relationship between unemployment and inflation is more complicated than that suggested by simple new-Keynesian models that incorporate a “natural rate” of unemployment." Why is this important?:

...In two forthcoming books,... I provide a theory that explains these data. I argue that there is no natural rate of unemployment and that the economy can come to rest in a stationary equilibrium at any point on the Beveridge curve. Which equilibrium persists, is decided by the confidence of households and firms that pins down asset values as reflected in housing wealth and the value of the stock market.

When households feel wealthy, that belief is self-fulfilling. Consumers spend a lot, firms hire workers, and the economy comes to rest at a point on the Beveridge curve with low unemployment and high vacancies. When the values of houses, factories, and machines fall, households spend less, firms lay off workers, and the economy comes to rest at a point on the Beveridge curve with high unemployment and low vacancies. Both situations – and anything in between – are zero-profit equilibria. ...

Policy implications

Most policymakers subscribe to the theory of the existence of a natural rate of unemployment. The data suggest that this theory is unconfirmed at best. To make the theory consistent with data, one must posit that the natural rate changes between recessions in unpredictable ways. This version of natural rate theory is difficult or impossible to refute. It is religion, not science.

For more than fifty years policy makers have been trying to hit two targets, unemployment and inflation, with one instrument, the interest rate. Recently, central bankers have discovered a second instrument – quantitative easing. I believe that quantitative easing works by influencing the value of real assets as reflected in housing wealth and the stock market and that it was successfully deployed by central banks in 2009 to maintain aggregate demand. In my two forthcoming books, I argue that quantitative easing should permanently enter the lexicon of central banking as a second instrument of monetary policy and that it will prove to be a more effective and flexible tool than fiscal policy for restoring and maintaining full employment.

I seem to be the only skeptic about the ability of quantitative easing to have a substantial impact on unemployment.

'How Economics Managed to Make Amends'

Arvind Subramanian defends economics:

How economics managed to make amends, by Arvind Subramanian, Commentary, Financial Times: In 2008, as the global financial crisis unfolded, the reputation of economics as a discipline and economists as useful policy practitioners seemed to be irredeemably sunk. Queen Elizabeth captured the mood when she asked pointedly why no one (in particular economists) had seen the crisis coming. There was no doubt that, notwithstanding the few Cassandras who correctly prophesied gloom and doom, the profession had failed colossally. ...

But crises will always happen and ... their timing, form and provenance will elude prognostication. Most crises, notably the big ones, creep up on us from unsuspected quarters. ... So, if the value of economics in preventing crises will always be limited (although hopefully not non-existent), perhaps a fairer and more realistic yardstick should be its value as a guide in responding to them. Here, one year on, we can say that economics stands vindicated.

How so? Recall that the recession of the late 1920s in the US became the Great Depression, owing to a combination of three factors: overly tight monetary policy; overly cautious fiscal policy...; and dramatic recourse to beggar-thy-neighbour policies, including competitive devaluations ... and increases in trade barriers. The impact of this global financial crisis has been significantly limited because on each of these scores, the policy mistakes of the past were strenuously and knowingly avoided. ...

What is striking about the influence of economics is that similar policy responses in the fiscal and monetary areas, and non-responses in relation to competitive devaluations and protectionism, were crafted across the globe. They were evident in emerging market economies and developing countries as much as in the industrial world; in red-blooded capitalist countries as well as in communist China and still-dirigiste India. If ever there was a Great Consensus, this was it.

If the Great Depression had not happened 80 years before,...  perhaps 2009 might have turned out differently. But... We were not condemned to repeat the mistakes of history because the economics profession had learnt and distilled the right lessons from that event.

For sure, we have not learnt all the lessons; we may even have learnt some wrong ones. It is also probable that we are setting the stage for future crises... So, economics is bound to fail again. But the avoidance of the Greatest Depression that could so easily have happened in 2009 is an outcome the world owes to economics; at the least, it is the discipline’s atonement for allowing the crisis of 2008 to unfold.

"How Bad Biology Killed the Economy"

Frans de Waal says many people believe that "the economy was killed by irresponsible risk-taking, a lack of regulation or a bubbling housing market, but the problem goes deeper. ... The ultimate flaw was the lure of bad biology, which resulted in a gross simplification of human nature," In particular, the reduction of human behavior to one motive, self-interest, is at fault (this is a much shortened version of the original):

How bad biology killed the economy, by Frans de Waal, RSA Journal: ...The book of nature is like the Bible: everyone reads into it what they like, from tolerance to intolerance and from altruism to greed. But it’s good to realize that, if biologists never stop talking about competition, this doesn’t mean that they advocate it, and if they call genes selfish, this doesn’t mean that genes actually are. Genes can’t be any more ‘selfish’ than a river can be ‘angry’ or sun rays ‘loving’. Genes are little chunks of DNA. At most, they are self-promoting, because successful genes help their carriers spread more copies of themselves. ...

[Many people have] fallen hook, line and sinker for the selfish-gene metaphor, thinking that if our genes are selfish, then we must be selfish, too. ... [T]oo many economists and politicians ... model human society on the perpetual struggle that they believe exists in nature, which is actually no more than a projection. Like magicians, they first throw their ideological prejudices into the hat of nature, then pull them out by their very ears to show how much nature agrees with them. It’s a trick for which we have fallen for too long. Obviously, competition is part of the picture, but humans can’t live by competition alone. ...

Lovers of open competition can’t resist invoking evolution. The e-word even slipped into the infamous ‘greed speech’ of Gordon Gekko, the corporate raider played by Michael Douglas in the 1987 movie Wall Street: “The point is, ladies and gentleman, that ‘greed’ – for lack of a better word – is good. Greed is right. Greed works. Greed clarifies, cuts through and captures the essence of the evolutionary spirit.” ... Is the evolutionary spirit really all about greed, as Gekko claimed, or is there more to it?

"The Civil War in Development Economics"

Anything with the words "Civil War" in it is catching my attention today:

The Civil War in Development Economics, by William Easterly: Few people outside academia realize how badly Randomized Evaluation has polarized academic development economists for and against. My little debate with Sachs seems like gentle whispers by comparison.

Want to understand what’s got some so upset and others true believers? A conference volume has just come out from Brookings. At first glance, this is your typical sleepy conference volume, currently ranked on Amazon at #201,635.

But attendees at that conference realized that it was a major showdown between the two sides, and now the volume lays out in plain view the case for the prosecution and the case for the defense of Randomized Evaluation.

OK, self-promotion confession, I am one of the editors of the volume, and was one of the organizers of the conference... Angus Deaton also gave a major luncheon talk at the conference, which was already committed for publication elsewhere. A previous blog discussed his paper.

Here’s an imagined dialogue between the two sides on Randomized Evaluation (RE) based on this book:

FOR: Amazing RE power lets us identify causal effect of project treatment on the treated.

AGAINST: Congrats on finding the effect on a few hundred people under particular circumstances, too bad it doesn’t apply anywhere else.

FOR: No problem, we can replicate RE to make sure effect applies elsewhere.

AGAINST: Like that’s going to happen. Since when is there any academic incentive to replicate already published results? And how do you ever know when you have enough replications of the right kind? You can’t EVER make a generic “X works” statement for any development intervention X. Why don’t you try some theory about why things work?

FOR: We are now moving in the direction of using RE to test theory about why people behave the way they do.

AGAINST: I think we might be converging on that one. But your advertising has not yet got the message, like the JPAL ad on “best buys on the Millennium Development Goals.”

FOR: Well, at least it’s better than your crappy macro regressions that never resolve what causes what, and where even the correlations are suspect because of data mining.

AGAINST: OK, you drew some blood with that one. But you are not so holy on data mining either, because you can pick and choose after the research is finished whatever sub-samples give you results, and there is also publication bias that shows positive results but not zero results.

FOR: OK we admit we shouldn’t do that, and we should enter all REs into a registry including those with no results.

AGAINST: Good luck with that. By the way, even if do you show something “works,” is that enough to get it adopted by politicians and implemented by bureaucrats?

FOR: But voters will want to support politicians who do things that work based on rigorous evidence.

AGAINST: Now you seem naïve about voters as well as politicians. Please be clear: do RE-guided economists know something the local people do not know, or do they have different values on what is good for them? What about tacit knowledge that cannot be tested by RE? Why has RE hardly ever been used for policymaking in developed countries?

FOR: You can take as many potshots as you want, at the end we are producing solid evidence that convinces many people involved in aid.

AGAINST: Well, at least we agree on the on the much larger question of what is not respectable evidence, namely, most of what is currently relied on in development policy discussions. Compared to the evidence-free majority, what unites us is larger than what divides us.

[On the civil war reference: I'm at the University of Oregon, and my brother played football for Oregon State many years ago - he was a defensive end - so to the extent that either of us cares after all these years, it's a Ducks versus Beavers family war as well (the next generation seems to care more than we do).]

"Catastrophe Theory and the Business Cycle"

As a follow up to the recent post on non-linear dynamics that continued the discussion on what's wrong with modern macroeconomics, here is a paper written many years ago by Hal Varian that extends the Goodwin-Kaldor model of business cycles. It is old-fashioned macro, but the interesting part is the wealth effect causing the difference between recessions and depressions. In particular, the results of the paper imply that shocks to wealth that change savings propensities -- as we are seeing now -- can cause recoveries that "may take a very long time, and differ quite substantially from the recovery pattern of a [typical] recession."

Here are a few selections from the paper:

Catastrophe Theory and the Business Cycle, by Hal Varian: In this paper we examine a variation on Kaldor's (1940) model of the business cycle using some of the methods of catastrophe theory. (Thom (1975), Zeeman (1977)). The development proceeds in several stages. Section I provides a brief outline of catastrophe theory, while Section II applies some of these techniques to a simple macroeconomic model. This model yields, as a special case, Kaldor's business cycles. ... In Section III, we describe a generalization of Kaldor's model that allows not only for cyclical recessions, but also allows for long term depressions. Section IV presents a brief review and summary.

On Buiter, Goodwin, and Nonlinear Dynamics

Rajiv Sethi continues the discussion on the state of modern macroeconomics:

On Buiter, Goodwin, and Nonlinear Dynamics, by Rajiv Sethi: A few months ago, Willem Buiter published a scathing attack on modern macroeconomics in the Financial Times. While a lot of attention has been paid to the column's sharp tone and rhetorical flourishes, it also contains some specific and quite constructive comments about economic theory that deserve a close reading. One of these has to do with the limitations of linearity assumptions in models of economic dynamics:

When you linearize a model, and shock it with additive random disturbances, an unfortunate by-product is that the resulting linearised model behaves either in a very strongly stabilising fashion or in a relentlessly explosive manner.  There is no ‘bounded instability’ in such models.  The dynamic stochastic general equilibrium (DSGE) crowd saw that the economy had not exploded without bound in the past, and concluded from this that it made sense to rule out, in the linearized model, the explosive solution trajectories.  What they were left with was something that, following an exogenous  random disturbance, would return to the deterministic steady state pretty smartly.  No L-shaped recessions.  No processes of cumulative causation and bounded but persistent decline or expansion.  Just nice V-shaped recessions.

Buiter is objecting here to a vision of the economy as a stable, self-correcting system in which fluctuations arise only in response to exogneous shocks or impulses. This has come to be called the Frisch-Slutsky approach to business cycles, and its intellectual origins date back to a memorable metaphor introduced by Knut Wicksell more than a century ago: "If you hit a wooden rocking horse with a club, the movement of the horse will be very different to that of the club" (translated and quoted in Frisch 1933). The key idea here is that irregular, erratic impulses can be transformed into fairly regular oscillations by the structure of the economy. This insight can be captured using linear models, but only if the oscillations are damped - in the absence of further shocks, there is convergence to a stable steady state. This is true no matter how large the initial impulse happens to be, because local and global stability are equivalent in linear models.

A very different approach to business cycles views fluctuations as being caused by the local instability of steady states, which leads initially to cumulative divergence away from balanced growth. Nonlinearities are then required to ensure that trajectories remain bounded. Shocks to the economy can make trajectories more erratic and unpredictable, but are not required to account for persistent fluctuations. An energetic and  life-long proponent of this approach to business cycles was Richard Goodwin, who also produced one of the earliest such models in economics (Econometrica, 1951). Most of the literature in this vein has used aggregate investment functions and would not be considered properly microfounded by contemporary standards (see, for instance, Chang and Smyth 1971, Varian 1979, or Foley 1987). But endogenous bounded fluctuations can also arise in neoclassical models with overlapping generations (Benhabib and Day 1982, Grandmont 1985).

The advantage of a nonlinear approach is that it can accomodate a very broad range of phenomena. Locally stable steady states need not be globally stable, so an economy that is self-correcting in the face of small shocks may experience instability and crisis when hit by a large shock. This is Axel Leijonhufvud's corridor hypothesis, which its author has discussed in a recent column. Nonlinear models are also required to capture Hyman Minsky's financial instability hypothesis, which argues that periods of stable growth give rise to underlying behavioral changes that eventually destabilize the system. Such hypotheses cannot possibly be explored formally using linear models.

This, I think, is the point that Buiter was trying to make. It is the same point made by Goodwin in his 1951 Econometrica paper, which begins as follows:

Almost without exception economists have entertained the hypothesis of linear structural relations as a basis for cycle theory. As such it is an oversimplified special case and, for this reason, is the easiest to handle, the most readily available. Yet it is not well adapted for directing attention to the basic elements in oscillations - for these we must turn to nonlinear types. With them we are enabled to analyze a much wider range of phenomena, and in a manner at once more advanced and more elementary. 

By dropping the highly restrictive assumptions of linearity we neatly escape the rather embarrassing special conclusions which follow. Thus, whether we are dealing with difference or differential equations, so long as they are linear, they either explode or die away with the consequent disappearance of the cycle or the society. One may hope to avoid this unpleasant dilemma by choosing that case (as with the frictionless pendulum) just in between. Such a way out is helpful in the classroom, but it is nothing more than a mathematical abstraction. Therefore, economists will be led, as natural scientists have been led, to seek in nonlinearities an explanation of the maintenance of oscillation. Advice to this effect, given by Professor Le Corbeiller in one of the earliest issues of this journal, has gone largely unheeded.

And sixty years later, it remains largely unheeded.

"What if a Recovery Is All in Your Head?"

Robert Shiller wonders if the recovery is based upon a self-fulfilling prophecy:

What if a Recovery Is All in Your Head?, by Robert J. Shiller, Commentary, NY Times: Beyond fiscal stimulus and government bailouts, the economic recovery that appears under way may be based on little more than self-fulfilling prophecy.

Consider this possibility: after all these months, people start to think it’s time for the recession to end. The very thought begins to renew confidence, and some people start spending again — in turn, generating visible signs of recovery. This may seem absurd, and is rarely mentioned... but economic theorists have long been fascinated by such a possibility.

The notion isn’t as farfetched as it may appear. As we all know, recessions generally last no more than a couple of years. The current recession ... is almost two years old. According to the standard schedule, we’re due for recovery. Given this knowledge, the mere passage of time may spur our confidence, though no formal statistical analysis can prove it.

Certainly, people did not always believe that there is a regular “business cycle” that starts and stops in a definite pattern. The idea began to spread in the popular consciousness in the 1920s and reached full bloom in the ’30s — with one major complication, the Great Depression... “Recession,” a kinder, gentler term, began to be used around the time of the 1937-38 contraction to refer to a normal downturn in the business cycle. ...

Recessions, as the term came to be used, implied timetables that mark their expected end. Uttering the word does not risk damaging confidence, at least not fundamentally. A diagnosis of a recession can be shrugged off as something from which you will recover... A depression came to be another matter entirely.

It wasn’t until 1948 that the Columbia University sociologist Robert K. Merton wrote an article ... titled “The Self-Fulfilling Prophecy,” using the Great Depression as his first example. He is often credited with having invented the “self-fulfilling prophesy” phrase...

In important ways, we are still using that 1930s pattern of thinking. We are instinctively fearful of reckless talk about depressions, and we try to support one another’s confidence. We like the idea that modern scientific economics seems to show that all recessions end in due course.

For now, our common efforts at building confidence appear to be working somewhat. But the economy has still not recovered, by any means. ...

The problem might be put this way: There is still a nagging doubt afloat that the current event is really just another example in that long sequence of recessions. In which mental category does the current contraction belong: recession or depression? We may still be at a tipping point. To the extent that the theory of the self-fulfilling prophecy is correct, there is a case for continued vigilance, to ensure that adverse events don’t encourage widespread talk of the second category.

Barry Ritholtz responds [Note: Updated version posted at Barry's request]:

How Overrated is Sentiment in Economics?, by Barry Ritholtz: There is a small cadre of Economists — original thinkers, contrarians, out of the box theorists — whom  I respect a great deal. It is a modest list ranging from Richard Thaler to David Rosenberg to Robert Shiller, with lots of smart econ wonks in between.

This morning, however, I find myself somewhat disagreeing with one of the smarter of the economists, Professor Bob Shiller... Hence, it is with trepidation that I point out the flaws in Shiller’s discussion about the recovery, (titled “What if a Recovery Is All in Your Head?“). It is a thought provoking but unpersuasive argument... To be fair, he uses the column to incite a debate, rather than defend the position that the recovery is “mostly mental.”

I find numerous things worth challenging in the column... Let me offer 10 items..:

1. Time: The typical post-war Recession lasts 8 months, not “a couple of years”; We are now in month 23. If people started to spend because they sensed it was “late in the recession” or somehow intuited that it was time for the contraction to end, well then, based upon history, that would have been somewhere around August 2008.

2. Not Totally Irrational: One of my complaints about economics is it over-emphasizes people as rational, unemotional actors. However, when it comes to sentiment, economics seems to make the same mistake in the opposite direction — it assumes that people are foolish, unthinking creatures unable to engage in ANY rational thought whatsoever. All sentiment, no rationality at all.

The reality is quite different: Sometimes, people behave the way they do because they have figured out a problem and are responding to it intelligently.

Home Economicus does not really exist — but then again, neither does Homo Idiotus.

3. Healthy Fear of Job Loss: Employed people began to spend their money more carefully when they saw coworkers getting laid off in increasing numbers. That is a rational act in the face of an increasing possibility of a loss of income. This is unlikely to change in the near future, so long as large public layoffs remain a news item. Is this a Sentiment factor — or a rational response to changing conditions?

4. Asset Deflation: Consumers cut back their spending when they saw their biggest assets (Homes, Stocks) lose a significant value. Again, a rational response to a change in personal financial conditions, or bad sentiment?

5. False Belief System: Earlier this year, the Dow had dropped over 5,000 points in 6 months. One of the collective fallacies our culture operates under is the delusion that the market is some kind of astute forecasting machine. It is not — it represents the collective wisdom of 10 million panicked monkeys. That millions of slightly clever, pants wearing primates can combine their collective ignorance, their intellectual foibles, biases and false beliefs somehow into something resembling intelligence was one of the false beliefs of the era. Unfortunately, this is a condition the monkeys are prone towards (Witch burning, bloodletting, organized religion, etc.).

Note however that this does not reflect collective negative sentiment, but is actually the result of what happens when a faulty belief system dominates a society.

6. Doom Warnings Began Making Sense: Many of the doomsayers have been warning of the coming apocalypse for years. ... Why did this group suddenly gain traction in 2008? Maybe it was because  the population is not nearly so stupid as the politicians believe. The masses saw with their own two eyes the decay in the economy. Suddenly, the warnings were not as far fetched as they previously seemed.

7. Reacting to Flat Income: Families have recognized their incomes have remained flat to negative over the past decade, while their expenses have increased. What should be the rational reaction to this realization? (Hint: a new car, a bigger house, a new vacation are not on the list of options).

8. Time to Exit the Bunkers: Ten months ago, people were betting the economic world was coming to an end. The economy was in freefall, consumers froze, dramatically reduced spending. But the freefall is now over, and while its arguable whether the recession is over (by some measures it is, others not) most of us will agree that the Great Recession ended sometime in Spring of ‘09.

The US consumer is no longer frozen like deer in headlights. Is that sentiment, of just the reality of the situation — what happens when the ice melted?

9. The Cheerleaders Now Look Like Fools: At the onset of a recession, we often see cheerleaders, OpEd writers, and money losing fund managers make the argument that there is no economic slowdown — that the weakness is only in people’s minds. I call these people the Pervasive Pollyannas of Prosperity. (Think Phil Gramm, Amity Shlaes, Don Luskin). Some are partisans, others are dumb, others still merely incompetent — a few are all three. Yet despite their best efforts of the cheerleaders, the economy still went into freefall.  Perhaps the public has learned (a teeny bit) who to listen to and who to ignore.

10. Deleveraging: We know why this recession was so deep and long — the wanton use of leverage by people and financial institutions. The deleveraging that is taking place is a long slow process. It is rational, it is intelligent, and it will be how families will restore their balance sheets — the paradox of thrift be damned . . .

I appreciate that Professor Shiller was not arguing in favor of “its all mental.” He sought to spark a debate; I hope this response rose to the challenge . . .

I find that I have a knee-jerk, negative reaction to explanations based upon mass psychology, sentiment, story-telling, and the like. I have to consciously force myself not to dismiss them. I'm not sure why that is, though it probably has something to do with a feeling that such explanations aren't scientific, and hence have no place in serious academic investigations. That is, prior to the crisis I thought that the real economy drove sentiment, and not the other way around. Sentiment could definitely provide a feedback loop that strengthens negative or positive economic shocks, but psychology was not the prime mover. Thus, sentiment changes that did not have evidence to support them would quickly die out before having much, if any effect.

But this crisis has caused me to reevaluate. I still find the Shiller-type animal spirits, psychology based explanations hard to swallow, but when the foundation supporting your beliefs is called into question (in this case modern macroeconomic models), it's important to open your mind and at least give alternative explanations a chance. That's particularly true when the person pushing the stories has a pretty darn good record of using them to warn of bubbles, as Shiller does. So I'm trying.

Stabilities and Instabilities in the Macroeconomy

More on what's wrong with modern macro, this time from Axel Leijonhufvud:

Stabilities and instabilities in the macroeconomy, by Axel Leijonhufvud, Vox EU: Fifty-some years ago, students were taught that the private sector had no tendency to gravitate to full employment, that it was prone to undesirable fluctuations amplified by multiplier and accelerator effects, and that it was riddled with market failures of various sorts. But it was also believed that a benevolent, competent, democratic government could stabilize the macroeconomy and reduce the welfare consequences of most market failures to relative insignificance.

Fifty years later, around the beginning years of this century, students were taught that representative governments produce pointless fluctuations in prices and output but, if they can be constrained from doing so – by an independent central bank, for example – free markets are sure to produce full employment and, of course, many other blessings besides. Macroeconomic policy doctrine had shifted from stabilizing the private to constraining the public sector.

This long swing in our understanding of the economy spans a half-century of prolific technical accomplishments in economics (Blanchard 2008). But what the story shows is that, ontologically, economics has been completely at sea, drifting on the surface in currents of our own making. We lack an anchored understanding of the nature of the reality that economics is supposed to illuminate.

Top-Down versus Bottom-Up Macroeconomics

When Paul DeGrauwe presented this paper at the What's Wrong with Modern Macroeconomics conference (papers here), his argument that rational expectations models are the intellectual heirs of central planning seemed to ruffle a few feathers:

Top-down versus bottom-up macroeconomics, by Paul De Grauwe, Commentary, Vox EU: There is a general perception today that the financial crisis came about as a result of inefficiencies in the financial markets and economic actors’ poor understanding of the nature of risks. Yet mainstream macroeconomic models, as exemplified by the dynamic stochastic general equilibrium (DSGE) models, are populated by agents who are maximising their utilities in an intertemporal framework using all available information including the structure of the model – see Smets and Wouters (2003), Woodford (2003), Christiano et al. (2005), and Adjemian, et al. (2007), for example. In other words, agents in these models have incredible cognitive abilities. They are able to understand the complexities of the world, and they can figure out the probability distributions of all the shocks that can hit the economy. These are extraordinary assumptions that leave the outside world perplexed about what macroeconomists have been doing during the last decades.

Evidence on rationality from other sciences

These developments in mainstream macroeconomics are surprising for other reasons. While macroeconomic theory enthusiastically embraced the view that some if not all agents fully understand the structure of the underlying models in which they operate, other sciences like psychology and neurology increasingly uncovered the cognitive limitations of individuals (see e.g. Kahneman 2002, Camerer et al. 2005, Kahneman and Thaler 2006, and Della Vigna 2007). We learn from these sciences that agents only understand small bits and pieces of the world in which they live, and instead of maximising continuously taking all available information into account, agents use simple rules (heuristics) in guiding their behaviour (Gigerenzer and Todd 1999). The recent financial crisis seems to support the view that agents have limited understanding of the big picture. If they had understood the full complexity of the financial system, they would have understood the lethal riskiness of the assets they piled into their portfolios.

Top-down and bottom-up models

In order to understand the nature of different macroeconomic models, it is useful to make a distinction between top-down and bottom-up systems.

• In its most general definition, a top-down system is one in which one or more agents fully understand the system. These agents are capable of representing the whole system in a blueprint that they can store in their mind. Depending on their position in the system, they can use this blueprint to take command or to optimise their own private welfare. An example of such a top-down system is a building that can be represented by a blueprint and fully understood by the architect.
• Bottom-up systems are very different in nature. These are systems in which no individual understands the whole picture. Each individual understands only a very small part of the whole. These systems function as a result of the application of simple rules by the individuals populating the system. Most living systems follow this bottom-up logic (see the beautiful description of the growth of the embryo by Dawkins 2009).

The market system is also a bottom-up system. The best description made of this bottom-up system is still the one made by Hayek (1945).

Hayek argued that no individual is capable of understanding the full complexity of a market system. Instead, individuals only understand small bits of the total information. The main function of markets consists in aggregating this diverse information. If there were individuals capable of understanding the whole picture, we would not need markets. This was in fact Hayek’s criticism of the “socialist” economists who took the view that the central planner understood the whole picture and would therefore be able to compute the whole set of optimal prices, making the market system superfluous.

Rational expectations models as intellectual heirs of central planning

My contention is that the rational expectations models are the intellectual heirs of these central-planning models. Not in the sense that individuals in these rational expectations models aim at planning the whole, but in the sense that, as the central planner, they understand the whole picture. These individuals use this superior information to obtain the “optimum optimorum” for their own private welfare. In this sense, they are top-down models.

In a recent paper, I contrast the rational expectations top-down model with a bottom-up macroeconomic model (De Grauwe 2009). The latter is a model in which agents have cognitive limitations and do not understand the whole picture (the underlying model). Instead, they only understand small bits and pieces of the whole model and use simple rules to guide their behaviour. I introduce rationality in the model through a selection mechanism in which agents evaluate the performance of the rule they are following and decide to keep or change their rule depending on how well it performs relative to other rules. Thus agents in the bottom-up model learn about the world in a “trial and error” fashion.

These two types of models produce very different insights. I mention three differences here. First, the bottom-up model creates correlations in beliefs that in turn generate waves of optimism and pessimism. The latter produce endogenous business cycles which are akin to the Keynesian “animal spirits” (see Akerlof and Shiller 2009).

Second, the bottom-up model provides for a very different theory of the business cycle compared to the business cycle theory implicit in the rational expectations (DSGE) models. In the DSGE models, business cycle movements in output and prices arise because rational agents cannot adjust their optimal plans instantaneously after an exogenous disturbance. Price and wage stickiness prevent such instantaneous adjustment. As a result, these exogenous shocks (e.g. productivity shocks, or shocks in preferences) produce inertia and business cycle movements. Thus it can be said that the business cycle in DSGE models is exogenously driven. As an example, in the DSGE model, the financial crisis and the ensuing downturn in economic activity is the result of an exogenous and unpredictable increase in risk premia in August 2007.

In contrast to the rational expectations model, the bottom-up model has agents who experience an informational problem. They do not fully understand the nature of the shock or its transmission. They use a trial-and-error learning process aimed at distilling information. This process leads to waves of optimism and pessimism, which in a self-fulfilling way create business cycle movements. Booms and busts reflect the difficulties of economic agents trying to understand economic reality. The business cycle has a large endogenous component. Thus, in this bottom-up model, the financial crisis and the ensuing economic downturn should be explained by the previous boom.

Finally, the bottom-up model confirms the insight obtained from mainstream macroeconomics (including the DSGE models) that a credible inflation targeting is necessary to stabilise the economy. However, it is not sufficient. In a world where waves of optimism and pessimism (animal spirits) can exert an independent influence on output and inflation, it is in the interest of the central banks not only to react to movements in inflation but also to movements in output and asset prices so as to reduce the booms and busts that free market systems produce quite naturally. ...

What's Wrong with Modern Macroeconomics: Comments

I really hope that the conversation in the comments to the post What's Wrong with Modern Macroeconomics: Conference papers will continue:

Barkley Rosser said... I should have guessed that de Grauwe might have a good paper.

So, Mark, what have you to say to the assembled masses there that you are willing to report back to us about the spot-on paper by Alan Kirman?

reason said in reply to Barkley Rosser... Barkley,
thanks for the tip. The Kirman paper is my reading on the train now.

Mark Thoma said... One thing I learned from it is that I need to read the old papers by Sonnenschein (1972), Mantel (1974), and Debreu (1974) since these papers appear to undermine representative agent models. According to this work, you cannot learn anything about the uniqueness of an equilibrium, whether an equilibrium is stable, or how agents arrive at equilibrium by looking at individual behavior (more precisely, there is no simple relationship between individual behavior and the properties of aggregated variables - someone added the the axiom of revealed preference doesn't even survive aggregating two heterogeneous agents).

I need to learn the full extent to which this work undermines the whole microfoundations approach (hence Kirman's call to study the properties of networks so as to generate endogenous cycles from phase transitions rather than trying to model individual agents from first principles - that's not his sole reason for wanting to turn to this approach, but it's part of it).

I didn't understand that extent to which representative agent models are an analytical convenience to work around this problem (the DSGE theorists who understood this kept quiet about it).

You can get interactions among agents while maintaining identical agents, i.e. network effects do not necessarily require heterogeneity, but most interesting cases, it seems to me, do involve both heterogeneity and agents whose decisions are interdependent.

(Robert Solow said all you get is that excess demands have to sum to zero, i.e. Walras Law and a couple of other properties, but it's not much).

So that was the most important thing I learned, and it's something I should have known already. Once I learn a bit more about the results in these papers, I hope to post something about it. (A small part of my remarks wondered if learning models might not help to overcome this problem since they might give you a path to the equilibrium, and the number of equilibrium paths might be determinate and equal to one giving uniqueness, but it was pure speculation).

Barkley Rosser said... Mark,

The missing man in the critique of the representative agent model is Michael Jerison of SUNY-Albany. In his much-cited JEP paper on "Whom does the representative agent represent?", Kirman used (and cited) an example from a still unpublished paper by the sadly neglected Jerison.

Kirman is an interesting figure in all this as he was originally a general equilibrium theorist (well, originally a game theorist, and then a GE theorist). While he has differences with his old GE comrades, they all respect his critique because it does start with the SMD theorem, which is well known to all of them and very fundamentally disruptive. The DSGE modelers conveniently cover that one up, along with some other major problems.

Of course, in a world of multiple equilibria, learning may lead you anywhere.

Sebastion said... A good reference for anyone with access to the Mas-Colell et al micro textbook is their chapter 4 on aggregate demand and in particular chapter 4D on the existence of a representative consumer. Unfortunately, chapter 4 is usually NOT being taught in standard micro classes

Roberto Cruccolini said... And it might be good after having read chapter 4 to continue with chapter 17E in MasColell, called "Anything goes: The Sonnenschein-Mantel-Debreu Theroem". There you are, even in the advanced-micro bible you can read about those results, which are also at least extremely interesting for modern "microfounded" macro.

I think, this phenomenon of forgetting and/or neglecting former knowledge, e.g. the whole discussion and aspects of aggregation, which is really central to the methodology of modern macro, as the notion of microfoudations via optimizing agents was one of the core-arguments of New Classical Makro Revolution, is deeply unsettling. You could also add the oblivion of coordination & interaction problems, of discontinuities & emergence as probably central aspects of makroeconomics, which seem to be the reasons, why macro was once thought to be necessarily a different approach than micro.

There seem to be two ways to deal with this finding. One is to complain about the way modern macro has developed, and to suggest other/better solutions; this is, what we see most of the time right now.

That is of course worthwhile and understandable, but there remains a strange aspect: nearly all of nowadays criticisms were already mentioned 20 or 30 years before (recall Solow 1978 at the same conference as Lucas & Sargent, or Summers 1986 in response to Prescott, or Blinder 1987, or, which is sort of funny, Kirman 1989 & 1992 and - again - 2009,...)

And this leads to the interesting question, why modern macro/new classical methodology & thinking was so successful in conquering the field:

why did economists think, that Lucas 1976 said something new, given the reflections of Marshall how to theorize given ever changing structures, given Haavelmos ideas on the autonomy of economic relations, given the debates between Keynes and Tinbergen of econometrics and structural instability, and so on?

And why did they follow him in applying a Walrasian program using the representative-agent methodology given all these challenging aggregation results (see for the makro-production-function Fisher 1969 or Fisher&Felipe 2003 & 2006 and the bunch of literature to the Cambridge Capital Controversies and of course the literature interpreting the Sonnenschein-Mantel-Debreu results, f.e. Rizvi 1994 or 2006)

And in what sense does it make sense to describe modern macro models as "microfounded", if at the same time, you need some Friedman-1953-as-if argumentation to justify & make plausible your way of modeling, which is often referred to, that the inner functioning of your model is a black box and unknown, only built to generate predictions, not less. but certainly not more, in the sense that we think the way of modeling is reasonably realistic and corresponding to some mechanisms in the real world. Why should we, or why is this commonly called "microfoundations"??

johnchx said... MT wrote: "One thing I learned from it is that I need to read the old papers by Sonnenschein (1972), Mantel (1974), and Debreu (1974) since these papers appear to undermine representative agent models."

Yes; I have the impression that it's been clear for a very long time that representative agent models lack microfoundations -- that is, that classical micro assumptions are insufficient to support the existence of a representative agent, that the necessary conditions are unknown, and that the known sufficient conditions are extremely restrictive (e.g. perfectly homogeneous agents). I've tended to view this as evidence that many of those advancing the banner of microfoundations simply weren't serious.
If one really cared about understanding macro fluctuations in terms of the behavior of individual households and firms, one would study the behavior of individual households and firms; a representative agent framework would be entirely unsatisfactory.

I think it's also telling that most of the famous names associated with microfoundations in macro have been theoreticians rather than microeconometricians. I suspect that we may be able to learn more about real microfoundations of macro from, say, James Heckman than from Lucas or Barro.

Am I wildly off-base?

Herman said... Mark Thoma,
May I suggest elevating Roberto Cruccolini's very interesting comment above as a separate new post.

reason said in reply to Herman... Or even on Steven Keen's book "Debunking Economics" which covers most of the same territory in a very readable fashion.

Update: More from Alan Kirman: Economic theory and the crisis.

"What Computer Science Can Teach Economics"

Can real world agents actually find the Nash equilibrium that is used to describe their behavior in economic models?:

What computer science can teach economics, by Larry Hardesty, MIT News Office:  Computer scientists have spent decades developing techniques for answering a single question: How long does a given calculation take to perform? Constantinos Daskalakis, an assistant professor in MIT’s Computer Science and Artificial Intelligence Laboratory, has exported those techniques to game theory, a branch of mathematics with applications in economics, traffic management — on both the Internet and the interstate — and biology, among other things. By showing that some common game-theoretical problems are so hard that they’d take the lifetime of the universe to solve, Daskalakis is suggesting that they can’t accurately represent what happens in the real world.

Game theory is a way to mathematically describe strategic reasoning — of competitors in a market, or drivers on a highway or predators in a habitat. ...

In game theory, a “game” is any mathematical model that correlates different player strategies with different outcomes. One of the simplest examples is the penalty-kick game: In soccer, a penalty kick gives the offensive player a shot on goal with only the goalie defending. The goalie has so little reaction time that she has to guess which half of the goal to protect just as the ball is struck; the shooter tries to go the opposite way. In the game-theory version, the goalie always wins if both players pick the same half of the goal, and the shooter wins if they pick different halves. So each player has two strategies — go left or go right — and there are two outcomes — kicker wins or goalie wins.

It’s probably obvious that the best strategy for both players is to randomly go left or right with equal probability; that way, both will win about half the time. And indeed, that pair of strategies is what’s called the “Nash equilibrium” for the game. Named for John Nash..., the Nash equilibrium is the point in a game where the players have found strategies that none has the incentive to change unilaterally. In this case, for instance, neither player can improve her outcome by going one direction more often than the other.

Of course, most games are more complicated than the penalty-kick game, and their Nash equilibria are more difficult to calculate. But the reason the Nash equilibrium is associated with Nash’s name  — and not the names of other mathematicians who, over the preceding century, had described Nash equilibria for particular games — is that Nash was the first to prove that every game must have a Nash equilibrium. Many economists assume that, while the Nash equilibrium for a particular market may be hard to find, once found, it will accurately describe the market’s behavior.

Daskalakis’s doctoral thesis — which won the Association for Computing Machinery’s 2008 dissertation prize — casts doubts on that assumption. Daskalakis, working with Christos Papadimitriou of the University of California, Berkeley, and the University of Liverpool’s Paul Goldberg, has shown that for some games, the Nash equilibrium is so hard to calculate that all the computers in the world couldn’t find it in the lifetime of the universe. And in those cases, Daskalakis believes, human beings playing the game probably haven’t found it either.

In the real world, competitors in a market or drivers on a highway don’t (usually) calculate the Nash equilibria for their particular games and then adopt the resulting strategies. Rather, they tend to calculate the strategies that will maximize their own outcomes given the current state of play. But if one player shifts strategies, the other players will shift strategies in response, which will drive the first player to shift strategies again, and so on. This kind of feedback will eventually converge toward equilibrium: in the penalty-kick game, for example, if the goalie tries going in one direction more than half the time, the kicker can punish her by always going the opposite direction. But, Daskalakis argues, feedback won’t find the equilibrium more rapidly than computers could calculate it.

The argument has some empirical support. Approximations of the Nash equilibrium for two-player poker have been calculated, and professional poker players tend to adhere to it — particularly if they’ve read any of the many books or articles on game theory’s implications for poker. The Nash equilibrium for three-player poker, however, is intractably hard to calculate, and professional poker players don’t seem to have found it.

How can we tell? Daskalakis’s thesis showed that the Nash equilibrium belongs to a set of problems that is well studied in computer science: those whose solutions may be hard to find but are always relatively easy to verify. The canonical example of such a problem is the factoring of a large number: The solution seems to require trying out lots of different possibilities, but verifying an answer just requires multiplying a few numbers together. In the case of Nash equilibria, however, the solutions are much more complicated than a list of prime numbers. The Nash equilibrium for three-person Texas hold ’em, for instance, would consist of a huge set of strategies for any possible combination of players’ cards, dealers’ cards, and players’ bets. Exhaustively characterizing a given player’s set of strategies is complicated enough in itself, but to the extent that professional poker players’ strategies in three-player games can be characterized, they don’t appear to be in equilibrium.

Anyone who’s into computer science — or who read “Explained: P vs. NP” on the MIT News web site last week — will recognize the set of problems whose solutions can be verified efficiently: It’s the set that computer scientists call NP. Daskalakis proved that the Nash equilibrium belongs to a subset of NP consisting of hard problems with the property that a solution to one can be adapted to solve all the others. ...

That result “is one of the biggest yet in the roughly 10-year-old field of algorithmic game theory,” says Tim Roughgarden, an assistant professor of computer science at Stanford University. It “formalizes the suspicion that the Nash equilibrium is not likely to be an accurate predictor of rational behavior in all strategic environments.”

Given the Nash equilibrium’s unreliability, says Daskalakis, “there are three routes that one can go. One is to say, We know that there exist games that are hard, but maybe most of them are not hard.” In that case, Daskalakis says, “you can seek to identify classes of games that are easy, that are tractable.”

The second route, Daskalakis says, is to find mathematical models other than Nash equilibria to characterize markets — models that describe transition states on the way to equilibrium, for example, or other types of equilibria that aren’t so hard to calculate. Finally, he says, it may be that where the Nash equilibrium is hard to calculate, some approximation of it — where the players’ strategies are almost the best responses to their opponents’ strategies — might not be. In those cases, the approximate equilibrium could turn out to describe the behavior of real-world systems.

As for which of these three routes Daskalakis has chosen, “I’m pursuing all three,” he says.

I've got my hands full trying to figure out what's wrong with macroeconomics, so I'll let the microeconomists handle this one. But it does remind me of this passage from "The Economic Crisis is a Crisis for Economic Theory" questioning the use of representative agent macroeconomic models as a shortcut around the problems associated with the aggregation of heterogeneous agents:

The basic market model has been shown to use remarkably little information when functioning at equilibrium. But as Saari and Simon (1978) have shown, if there were a mechanism that would take a General Equilibrium, (Arrow–Debreu) economy to an equilibrium, that mechanism would require an infinite amount of information. Thus,... starting from individuals with standard preferences and adding them up allows one to show that there is an equilibrium but does not permit one to say how it could be attained.

"Demystifying Social Knowledge"

Daniel Little looks at different approaches to "understanding society":

Demystifying social knowledge, by Daniel Little: There seem to be a couple of fundamentally different approaches to the problem of "understanding society." I'm not entirely happy with these labels, but perhaps "empiricist" and "critical" will suffice to characterize them.  We might think of these as styles of sociological thinking.  One emphasizes the ordinariness of the phenomena, and looks at the chief challenges of sociology as embracing the tasks of description, classification, and explanation.  The other highlights the inherent obscurity of the social world, and conceives of sociology as an exercise in philosophical theory, involving the work of presenting, clarifying and critiquing texts and abstract philosophical ideas as well as specific social circumstances.

The first approach looks at the task of social knowing as a fairly straightforward intellectual problem. It could be labeled "empiricist", or it could simply be called an application of ordinary common sense to the challenge of understanding the social world. It is grounded in the idea that the social world is fundamentally accessible to observation and causal discovery.  The elements of the social world are ordinary and visible. There are puzzles, to be sure; but there are no mysteries.  The social world is given as an object of study; it is partially orderly; and the challenge of sociology is to discover the causal processes that give rise to specific observed features of the social world.

This approach begins in the ordinariness of the objects of social knowledge.  We are interested in other people and how and why they behave, we are interested in the relationships and interactions they create, and we are interested in institutions and populations that individuals constitute. We have formulated a range of social concepts in terms of which we analyze and describe the social world and social behavior -- for example, "motive," "interest," "emotion," "aggressive," "cooperative," "patriotic," "state," "group," "ethnicity," "mobilization," "profession," "city," "religion." We know pretty much what we mean by these concepts; we can define them and relate them to ordinary observable behaviors and social formations. And when our attention shifts to larger-scale social entities (states, uprisings, empires, occupational groups), we find that we can observe many characteristics of each of these kinds of social phenomena.  We also observe various patterns and regularities in behavior, institution, and entity that we would like to understand -- the ways in which people from different groups behave towards each other, the patterns of diffusion of information that exist along a transportation system, the features of conflicts among groups in various social settings. There are myriad interesting and visible social patterns which we would like to understand, and sociologists develop a descriptive and theoretical vocabulary in terms of which to describe and explain various kinds of social phenomena.

In short, on this first approach, the social world is visible, and the task of the social scientist is simply to discover some of the observable and causal relations that obtain among social actors, actions, and composites. To be sure, there are hypothetical or theoretical beliefs we have about less observable features of the social world -- but we can relate these beliefs to expectations about more visible forms of social behavior and organization. If we refer to "social class" in an explanation, we can give a definition of what we mean ("position in the property system"), and we can give some open-ended statements about how "class" is expected to relate to observable social and political behavior. And concepts and theories for which we cannot give clear explication should be jettisoned; obscurity is a fatal defect in a theory.  In short, the task of social science research on this approach is to discover some of the visible and observable characteristics of social behavior and entities, and to attempt to answer causal questions about these characteristics.

This is a rough-and-ready empiricism about the social world. But there is another family of approaches to social understanding that looks quite different from this "empiricist" or commonsensical approach: critical theory, Marxist theory, feminist theory, Deleuzian sociology, Foucault's approach to history, the theory of dialectics, and post-modern social theory. These are each highly distinctive programs of understanding, and they are certainly different from each other in multiple ways. But they share a feature in common: they reject the idea that social facts are visible and unambiguous. Instead, they lead the theorist to try to uncover the hidden forces, meanings, and structures that are at work in the social world and that need to be brought to light through critical inquiry. Paul Ricoeur's phrase "the hermeneutics of suspicion" captures the flavor of the approach.  (See Alison Scott-Baumann's Ricoeur and the Hermeneutics of Suspicion for discussion.) Neither our concepts nor our ordinary social observations are unproblematic. There is a deep and sometimes impenetrable difference between appearance and reality in the social realm, and it is the task of the social theorist (and social critic) to lay bare the underlying social realities. The social realities of power and deception help to explain the divergence between appearance and reality: a given set of social relations -- patriarchy, racism, homophobism, class exploitation -- give rise to systematically misleading social concepts and theories in ordinary observers.

Marx's idea of the fetishism of commodities (link) illustrates the point of view taken by many of the theorists in this critical vein: what looks like a very ordinary social fact -- objects have use values and exchange values -- is revealed to mystify or conceal a more complex reality -- a set of relations of domination and control between bosses, workers, and consumers.  With a very different background, a book like Gaston Bachelard's The Psychoanalysis of Fire makes a similar point: the appearance represented by behavior systematically conceals the underlying human reality or meaning.  The word "critique" enters into most of Marx's titles -- for example, "Contribution to a Critique of Political Economy."  And for Marx, the idea of critique is intended to bring forward a methodology of critical reading, unmasking the assumptions about the social world that are implicit in the theorizing of a particular author (Smith, Ricardo, Say, Quesnay).  So Capital: Volume 1: A Critique of Political Economy is a book about the visible realities of capitalism, to be sure; but it is also a book intended to unmask both the deceptive appearances that capitalism presents and the erroneous assumptions that prior theorists have brought into their accounts.

The concepts of ideology and false consciousness have a key role to play in this discussion about the visibility of social reality.  And it turns out to be an ambiguous role.  Here is a paragraph from Slavoj Zizek on the concept of ideology from Mapping Ideology:

These same examples of the actuality of the notion of ideology, however, also render clear the reasons why today one hastens to renounce the notion of ideology: does not the critique of ideology involve a privileged place, somehow exempted from the turmoils of social life, which enables some subject-agent to perceive the very hidden mechanism that regulates social visibility and non-visibility? Is not the claim that we can accede to this place the most obvious case of ideology? Consequently, with reference to today's state of epistemological reflection, is not the notion of ideology self-defeating? So why should we cling to a notion with such obviously outdated epistemological implications (the relationship of 'representation' between thought and reality, etc.)? Is not its utterly ambiguous and elusive character in itself a sufficient reason to abandon it? 'Ideology' can designate anything from a contemplative attitude that misrecognizes its dependence on social reality to an action-orientated set of beliefs, from the indispensable medium in which individuals live out their relations to a social structure to false ideas which legitimate a dominant political power. It seems to pop up precisely when we attempt to avoid it, while it fails to appear where one would clearly expect it to dwell.

Zizek is essentially going a step beyond either of the two positions mentioned above.  The empiricist position says that we can perceive social reality.  The critical position says that we have to discover reality through critical theorizing.  And Zizek's position in this passage is essentially that there is no social reality; there are only a variety of texts.

So we have one style that begins in ordinary observation, hypothesis-formation, deductive explanation, and an insistence on clarity of exposition; and another style that begins in a critical stance, a hermeneutic sensibility, and a confidence in purely philosophical reasoning.  Jurgen Habermas draws attention to something like this distinction in his important text, On the Logic of the Social Sciences (1967), where he contrasts approaches to the social sciences originating in analytical philosophy of science with those originating in philosophical hermeneutics: "The analytic school dismisses the hermeneutic disciplines as prescientific, while the hermeneutic school considers the nomological sciences as characterized by a limited preunderstanding."  (This text as well as several others discussed here are available at AAARG.)  Habermas wants to help to overcome the gap between the two perspectives, and his own work actually illustrates the value of doing so.  His exposition of abstract theoretical ideas is generally rigorous and intelligible, and he makes strenuous efforts to bring his theorizing into relationship to actual social observation and experience. 

A contemporary writer (philosopher? historian? sociologist of science?) is Bruno Latour, who falls generally in the critical zone of the distinction I've drawn here.  An important recent work is Reassembling the Social: An Introduction to Actor-Network-Theory, in which he argues for a deep and critical re-reading of the ways we think the social -- the ways in which we attempt to create a social science. The book is deeply enmeshed in philosophical traditions, including especially Giles Deleuze's writings.  The book describes "Actor-Network-Theory" and the theory of assemblages; and Latour argues that these theories provide a much better way of conceptualizing and knowing the social world.  Here is an intriguing passage that invokes both themes of visibility and invisibility marking the way I've drawn the distinction between the two styles:

Like all sciences, sociology begins in wonder.  The commotion might be registered in many different ways but it's always the paradoxical presence of something at once invisible yet tangible, taken for granted yet surprising, mundane but of baffling subtlety that triggers a passionate attempt to tame the wild beast of the social.  'We live in groups that seem firmly entrenched, and yet how is it that they transform so rapidly?'  ... 'There is something invisible that weights on all of us that is more solid than steel and yet so incredibly labile.'  ...  It would be hard to find a social scientist not shaken by one or more of these bewildering statements.  Are not these conundrums the source of our libido scindi? What pushes us to devote so much energy into unraveling them? (21)

What intrigues many readers of Latour's works is that he too seems to be working towards a coming-together of critical theory with empirical and historical testing of beliefs.  He seems to have a genuine interest in the concrete empirical details of the workings of the sciences or the organization of a city; so he brings both the philosophical-theoretic perspective of the critical style along with the empirical-analytical goal of observational rigor of the analytic style. 

Also interesting, from a more "analytic-empiricist" perspective, are Andrew Abbott, Methods of Discovery: Heuristics for the Social Sciences, and Ian Shapiro, The Flight from Reality in the Human Sciences.  Abbott directly addresses some of the contrasts mentioned here (chapter two); he puts the central assumption of my first style of thought in the formula, "social reality is measurable".  And Shapiro argues for reconnecting the social sciences to practical, observable problems in the contemporary world; his book is a critique of the excessive formalism and model-building of some wings of contemporary political science.

My own sympathies are with the "analytic-empirical" approach.  Positivism brings some additional assumptions that deserve fundamental criticism -- in particular, the idea that all phenomena are governed by nomothetic regularities, or the idea that the social sciences must strive for the same features of abstraction and generality that are characteristic of physics.  But the central empiricist commitments -- fidelity to observation, rigorous reasoning, clear and logical exposition of concepts and theories, and subjection of hypotheses to the test of observation -- are fundamental requirements if we are to arrive at useful and justified social knowledge.  What is intriguing is to pose the question: is there a productive way of bringing insights from both approaches together into a more adequate basis for understanding society?

[Traveling: Scheduled to post at preset time.]

Greed versus Self-Interest

Political philosopher Michael Sandel:

...Citizens generally who looked at this - at the bailouts and the bonuses and have been outraged - they believe there is a difference between greed and self-interest. But here's no way of capturing that intuition in economic analysis because, according to economic analysis, in any case one is deploying self-interest or greed, which is simply self-interest squared, to serve a social purpose. That's what the economic model says. And you have to introduce some normative assumption about what is excessive pursuit of gain in order to make sense of greed as a vice independent of the self-interest that all of the economic models presuppose. So I think there are intuitions in everyday life that people have that the economic models simply don't capture, and greed is one of them."

[Traveling: Preset to post automatically.]

"Let A Hundred Theories Bloom"

Joseph Stiglitz says there's a diverse set of ideas within the economics profession, ideas that go beyond the "self-regulating, fully efficient markets that always remain at full employment" pushed by some economists. These ideas have had trouble finding their way into the mainstream, and that has limited our ability to move beyond the confines of standard approaches to macroeconomic modeling. But that may change (and hopefully will change) for the better since the crisis has "given new impetus to the exploration of alternative strands of thought that would provide better insights into how our complex economic system functions":

Let A Hundred Theories Bloom, by George Akerlof and Joseph Stiglitz, Commentary, Project Syndicate: BUDAPEST – The economic and financial crisis has been a telling moment for the economics profession, for it has put many long-standing ideas to the test. If science is defined by its ability to forecast the future, the failure of much of the economics profession to see the crisis coming should be a cause of great concern.

But there is, in fact, a much greater diversity of ideas within the economics profession than is often realized. This year’s Nobel laureates in economics are two scholars whose life work explored alternative approaches. Economics has generated a wealth of ideas, many of which argue that markets are not necessarily either efficient or stable, or that the economy, and our society, is not well described by the standard models of competitive equilibrium used by a majority of economists.

Behavioral economics, for example, emphasizes that market participants often act in ways that cannot easily be reconciled with rationality. Similarly, modern information economics shows that even if markets are competitive, they are almost never efficient when information is imperfect or asymmetric (some people know something that others do not, as in the recent financial debacle) – that is, always.

A long line of research has shown that even using the models of the so-called “rational expectations” school of economics, markets might not behave stably, and that there can be price bubbles. The crisis has, indeed, provided ample evidence that investors are far from rational; but the flaws in the rational expectations line of reasoning—hidden assumptions such as that all investors have the same information—had been exposed well before the crisis.

Just as the crisis has reinvigorated thinking about the need for regulation, so it has given new impetus to the exploration of alternative strands of thought that would provide better insights into how our complex economic system functions – and perhaps also to the search for policies that might avert a recurrence of the recent calamity.

Fortunately, while some economists were pushing the idea of self-regulating, fully efficient markets that always remain at full employment, other economists and social scientists have been exploring a variety of different approaches. These include agent-based models that emphasize the diversity of circumstances; network models, which focus on the complex interrelations among firms (such as those that enable bankruptcy cascades); a fresh look at the neglected work of Hyman Minsky on financial crises (which have increased in frequency since deregulation began three decades ago); and innovation models, which attempt to explain the dynamics of growth.

Much of the most exciting work in economics now underway extends the boundary of economics to include work by psychologists, political scientists, and sociologists. We have much to learn, too, from economic history. For all the fanfare surrounding financial innovation, this crisis is remarkably similar to past financial crises, except that the complexity of new financial products reduced transparency, aggravating fear about what might happen should there not be a massive public bailout.

Ideas matter, as much or perhaps even more than self-interest. Our regulators and elected officials were politically captured – special interests in the financial markets gained a great deal from rampant deregulation and the failure to adapt the regulatory structure to the new products. But our regulators and politicians also suffered from intellectual capture. They need a wider and more robust portfolio of ideas to draw upon.

That is why the recent announcement by George Soros at the Central European University in Budapest of the creation of a well-funded Initiative for New Economic Thinking (INET) to help support these is so exciting. Research grants, symposia, conferences, and a new journal – all will help encourage new ideas and collaborative efforts to flourish.

INET has been given complete freedom – with respect to both content and strategy – and one hopes that it will draw further support from other sources. Its only commitment is to “new economic thinking,” in the broadest sense. Last month, Soros assembled a remarkable group of economic luminaries, from across the spectrum of the profession –theory to policy, left to right, young and old, establishment and counter-establishment—to discuss the need and prospects for such an initiative, and how it might best proceed.

For the past three decades, one strand within the economics profession was constructing models that assumed that markets worked perfectly. This assumption overshadowed a wide body of research that helped explain why markets often work imperfectly – why, indeed, there are widespread market failures.

The marketplace for ideas also often works in a way that is less than ideal. In a world of human fallibility and imperfect understanding of the complexity of the economy, INET holds out the promise of the pursuit of alternative strands of thought – and thereby at least ameliorating this costly market imperfection.

As I've noted before, I don't think it was the tools or the topics we study that was the problem, it was the questions the leaders in the profession emphasized (or ridiculed as the case may be). The emphasis on particular questions leads to a dominant presence of this line of work in journals, the source of advancement within the profession, and this crowds out alternative approaches. It will be interesting to see which of these ideas, if any, will now "find its time."

"Skyhooks versus Cranes: The Nobel Prize for Elinor Ostrom"

Paul Romer wants to make sure that we understand the importance of Elinor Ostrom's "work on one of the deepest issues in economics":

Skyhooks versus Cranes: The Nobel Prize for Elinor Ostrom. by Paul Romer: Most economists think that they are building cranes that suspend important theoretical structures from a base that is firmly grounded in first principles. In fact, they almost always invoke a skyhook, some unexplained result without which the entire structure collapses. Elinor Ostrom won the Nobel Prize in Economics because she works from the ground up, building a crane that can support the full range of economic behavior.

When I started studying economics in graduate school, the standard operating procedure was to introduce both technology and rules as skyhooks. If we assumed a particular set of rules and technologies,... then we economists could describe what people would do. Sometimes we compared different sets of rules that a “social planner” might impose... Crucially, we never even bothered to check that people would actually follow the rules we imposed.

A typical conclusion was that rules that assign property rights and rules that let people trade lead to good outcomes. What’s the skyhook? That people will follow the rules. Why would they respect the property rights of someone else? ... We might have had in mind something like this: police officers will arrest people who don’t follow the rules. But this is just another skyhook. Who are these police officers? Why do they follow rules? ... Elinor showed that there are lots of important cases where people follow rules about ownership without police officers. One of the central challenges in understanding failures of economic development is that in many places, police officers don’t follow the rules they are meant to enforce.

Elinor’s fieldwork, followed up by her experimental work, pointed us in exactly the right direction. To understand BOTH why we don’t need police officers in some cases AND why police officers don’t follow the rules in other cases, we have to expand models of human preferences to include a contingent taste for punishing others. In reaching this conclusion, she ... spelled out the program that economists should follow. To make the rules ... emerge as an equilibrium outcome instead of a skyhook, economists must extend our models of preferences and gather field and experimental evidence on the nature of these preferences.

Economists who have become addicted to skyhooks ... think that they are doing deep theory but are really just assuming their conclusions... If we fail to explore rules in greater depth, economists will have little to say about the most pressing issues facing humans today – how to improve the quality of bad rules that cause needless waste, harm, and suffering.

Cheers to the Nobel committee for recognizing work on one of the deepest issues in economics. Bravo to the political scientist who showed that she was a better economist than the economic imperialists who can’t tell the difference between assuming and understanding.

Regulators attempting, and failing to impose the correct rules and how that helped to cause the financial crisis comes to mind. Seems like more focus on work like this might have helped.

"The Struggle Ahead"

Robert Solow on the need for further stimulus, the need to shift demand from consumption to investment, and the need for a new macroeconomics:

3 Questions: Robert Solow on the struggle ahead, MIT News: Economist Robert Solow's seminal work in the 1950s and 1960s showed how new technologies create a large portion of economic growth, an achievement for which he was awarded the 1987 Nobel Prize in Economics. With the economy seemingly in need of a technological boost again, the emeritus Institute Professor sat down with MIT News for a talk in his office this week.

Q. What is your assessment of the economy now, and where is it going?
A. Forecasting is hard and dangerous, and I don't do it. But it appears that the worst of the recession is over. However, the economy will be getting better slowly. And saying the economy is getting better is not the same thing as saying the economy is doing well. Real GDP fell by about 3.5 percent during the recession. But capacity increased 2.5 percent. We were producing 6 percent less than we knew how to produce. That gap has to narrow to reduce unemployment. If we rely only on the normal self-curing powers of a market economy, it may take until late in 2010 or early 2011 before we reduce that gap. So for that reason we should not rule out further stimulus in the next six to nine months. It's not easy because we have this enormous deficit. But we should recognize that even if the economy improves on its own, it won't do very much.

"Kuhn's Paradigm Shift"

Daniel Little discusses Thomas Kuhn's contributions to the philosophy of science:

Kuhn's paradigm shift, by Daniel Little: Thomas Kuhn's The Structure of Scientific Revolutions (1962) brought about a paradigm shift of its own, in the way that philosophers thought about science. The book was published in the Vienna Circle's International Encyclopedia of Unified Science in 1962. (See earlier posts on the Vienna Circle; post, post.) And almost immediately it stimulated a profound change in the fundamental questions that defined the philosophy of science. For one thing, it shifted the focus from the context of justification to the context of discovery. It legitimated the introduction of the study of the history of science into the philosophy of science -- and thereby also legitimated the perspective of sociological study of the actual practices of science. And it cast into doubt the most fundamental assumptions of positivism as a theory of how the science enterprise actually works.

"The Anti-History Boys"

Brad DeLong:

The anti-history boys, by J. Bradford DeLong, Commentary, Project Syndicate [earlier version]: If you asked a modern economic historian like me why the world is currently in the grips of a financial crisis and a deep economic downturn, I would tell you that this is the latest episode in a long history of similar bubbles, crashes, crises, and recessions that date back at least to the canal-building bubble of the early 1820s, the 1825-1826 failure of Pole, Thornton & Co, and the subsequent first industrial recession in Britain. We have seen this process at work in many other historical episodes as well – in 1870, 1890, 1929, and 2000.

For some reason, asset prices get way out of whack and rise to unsustainable levels. Sometimes the culprit is lousy internal controls in financial firms that over-reward subordinates for taking risk. Sometimes the cause is government guarantees. And sometimes it is simply a long run of good fortune, which leaves the market dominated by unrealistic optimists.

Then the crash comes. And when it does, risk tolerance collapses; everybody knows that there are immense unrealized losses in financial assets and nobody is sure that they know where they are. The crash is followed by a flight to safety, which is followed by a steep fall in the velocity of money as investors hoard cash. And that fall in monetary velocity brings on a recession.

I will not say that this is the pattern of all recessions; it isn’t. But I will say that this is the pattern of this recession, and that we have been here before.

But if you ask the same question of a modern macroeconomist – for example, the extremely bright Narayana Kocherlakota of the University of Minnesota – you will find that he says that he does not know, and that macroeconomic models attribute economic downturns to various causes. Most, he points out, “rely on some form of large quarterly movements in the technological frontier. Some have collective shocks to the marginal utility of leisure. Other models have large quarterly shocks to the depreciation rate in the capital stock (in order to generate high asset price volatilities)…”

That is, downturns are either the result of a great forgetting of technological and organizational knowledge, a great vacation as workers suddenly develop a taste for extra leisure, or a great rusting as the speed at which oxygen corrodes accelerates, reducing the value of large things made out of metal.

But modern macroeconomists will also say that all these models strike them as implausible stories that are not to be taken seriously. Indeed, according to Kocherlakota, nobody really believes them:

 “Macroeconomists use them only as convenient short-cuts to generate the requisite levels of volatility” in their mathematical models.

This leads me to ask two questions:

First, is it really true that nobody believes these stories? Ed Prescott of Arizona State University really does believe that large-scale recessions are caused by economy-wide episodes of forgetting the technological and organizational knowledge that underpin total factor productivity. One exception is the Great Depression, which Prescott says was caused by real wages far exceeding equilibrium values, owing to President Herbert Hoover’s extraordinary pro-labour, pro-union policies.

Likewise, Casey Mulligan of the University of Chicago really does appear to believe that large falls in the employment-to-population ratio are best seen as “great vacations” – and as the side-effect of destructive government policies like those in place today, which lead workers to quit their jobs so they can get higher government subsidies to refinance their mortgages. (I know; I find it incredible, too.)

Second, regardless of whether modern macroeconomists attribute our current difficulties to causes that are “patently unrealistic” or simply confess ignorance, why do they have such a different view than we economic historians do? Regardless of whether they have rejected our interpretations and understandings or simply have built or failed to build their own in ignorance of what we have done, why have they not used our work?

The second question is particularly disturbing. After all, economic theory should be grappling with economic history. Theory is crystallized history – it can be nothing more. Someone observes some instructive case or some anecdotal or empirical regularity, and says, “This is interesting; let’s build a model of this.” After the initial crystallization, theory does, of course, develop according to its own intellectual imperatives and processes, but the seed of history is still there. What happened to the seed?

This is not to say that the macroeconomic model-building of the past generation has been pointless. But I do think that modern macroeconomists need to be rounded up, on pain of loss of tenure, and sent to a year-long boot camp with the assembled monetary historians of the world as their drill sergeants. They need to listen to and learn from Dick Sylla about Alexander Hamilton’s bank rescue of 1825; from Charlie Calomiris about the Overend, Gurney crisis; from Michael Bordo about the first bankruptcy of Baring brothers; and from Barry Eichengreen, Christy Romer, and Ben Bernanke about the Great Depression.

If modern macroeconomists do not reconnect with history – if they do not realize just what their theories are crystallized out of and what the point of the enterprise is – then their profession will wither and die.

I'm not sure this answers Brad's question in general, but I think the general lack of interest in economic history may come, in part, from the rejection of Keynesian economics. The reason this group ignores economic history and the types of interpretations Brad is placing on it, certain parts of history anyway, may be, as David Laidler notes, that this group believes Keynes was a detour from the true path. Why learn this history when you think it is irrelevant?

People like Robert Lucas, for example, believe that modern models have little to offer when it comes to explaining the Great Depression or our recent experience:

Talking about "My Keynesian Education" at the 2003 HOPE conference on The IS-LM Model: Its Rise, Fall, and Strange Persistence (Michel De Vroey and Kevin Hoover 2004) Lucas pointed, almost as an aside, to

the problem that the new theories, the theories embedded in general equilibrium dynamics of the sort that we know how to use pretty well now – there's a residue of things they don't let us think about. They don't let us think about the U.S. experience in the 1930s or about financial crises and their real consequences in Asian and Latin America, they don't let us think very well about Japan in the 1990s (2004, p 23)

This remark dates from 2003, when the final "Greenspan boom" was in full swing, and the "residue" of problems to which Lucas referred did indeed seem rather remote from the immediate but apparently well-established economic environment in which it was made, and we should judge its offhand tone in this context. Viewed with hindsight, though, the remark was ominous, because those same models now, in 2009, do not help us to think about problems that are dominating the current evolution of the entire world economy - some residue!

But even though Lucas believes that modern models are not very useful, he is unwilling to learn from the past because he thinks it was an unproductive detour from the true path, i.e. there is nothing to learn:

As everyone knows, what was soon labeled new-classical economics emerged largely from Lucas’s own efforts... Initially, it was his replacement of adaptive by rational expectations that attracted most of the attention, but the closely related explicit application to traditionally macro questions of micro general equilibrium analysis marked a much more fundamental change in the then dominant approach to macro-economics, because it broke the area's last remaining intellectual links to Keynes' General Theory. Moreover, though Lucas’s contribution launched a radically new vision of how a market economy functions, he himself thought of it more modestly as involving the exploitation of newly available analytic techniques to deal with age-old problems that the macro-economic theory of the 1960s and the macro-econometrics that went with it had proved unable to address. Viewing the General Theory through the prism created by this macro-economics, he saw it as having created an unhelpful detour in the discipline’s otherwise orderly history, and interpreted its temporary success as a consequence of the historical situation that had prompted its writing.

In Lucas' view, Keynes had not advanced economics but had merely offered an ad hoc political response to the circumstances of the Great Depression, a response which, seen in relation to what Lucas believed to have been the already long-established internal dynamic of economics, was of no lasting scientific value. In 2004, he made this point as follows:

"Keynes's real contribution is . . .not Einstein-level theory, new paradigm, all this . . ..that's just so much hot air. . . [I]n writing the General Theory, Keynes was viewing himself as a spokesman for a discredited profession. . . .in a situation
where people are ready to throw in the towel on capitalism and liberal democracy and go with fascism or corporatism, protectionism, socialist planning. . . . What he hits on is that the government should take on new responsibilities . . . for stabilizing overall spending flows. . . . And . . . for everybody in the post-war period – I'm talking about Keynesians and monetarists both – that's the agreed upon view.. . .

So I think this was a great political achievement. It gave us a lasting image of what we need economists for. I've been talking about the internal mainstream of economics, that's what we researchers live on, but as a group we have to earn our living by helping people diagnose situations that arise and helping them understand what is going on and what we can do about it. That was Keynes's whole life. He was a political activist from beginning to end. (2004, pp 23-24)

The history is not just forgotten, it's intentionally ignored..

"Dark Age in Macroeconomics?"

This is Nick Rowe (it's in response to Paul Krugman and follows up on one of Nick's previous posts):

Dark Age in Macroeconomics? A History of Taught approach, by Nick Rowe: (Or maybe the title should be: "Notes from the Phelps/Lucas Administration"; or "Notes to supplement our fading memories of the late 1970's".)

Is this a Dark Age in macroeconomics? In other words, have we collectively forgotten some (important) stuff that we used to understand?

I want to approach this question by looking at what was taught in the past to economics graduate students, so we can compare what is left out now to what was left out then.

I have a sample of one: my own lecture notes from grad school. I began my MA at UWO in 1977, and continued into the PhD. I took everything in macro/money that was offered. At the time, UWO was arguably the top Canadian department in macro/money (OK, Western grads would argue for; Queens grads would argue against), and would hold up well against anywhere in the world.

Macro 1 (David Laidler). Required course. Review and critique of ISLM (lags, stocks flows and the government budget constraint, are the IS and AD curves really demand curves? [no], the missing AS curve). Crowding out debate. Non-Walrasian macro (Barro and Grossman). Say's Law. Phillips Curve (up to Phelps and Friedman). Consumption function (Friedman/Modigliani). Demand for money. Investment demand.

Macro 2 (Michael Parkin). Required for those continuing to the PhD. (I can't resist quoting from the first page of my notes here: "Economics [is] Understand + Explain Phenomena using Rational models. How could Rational Behaviour [lead to] Disaster? Market Failure."). Review and critique of Neoclassical model of labour market. Lucas and Rapping (from the Phelps volume), and why their model was logically incoherent (Michael Parkin was right on this point). Mortensen's (also from Phelps volume) search theory of unemployment. Theories of implicit wage contracts (sticky wages). Theories of price adjustment (proto New-Keynesian). ISLM plus Phillips Curve (distinction between proto New-Keynesian and New Classical interpretations of Phillips Curve). Adaptive vs. Rational expectations. Policy Irrelevance Proposition ("[deviations of output from y* are] just noise, but obviously false").

Money 1 (Don Patinkin/Peter Howitt). Optional. Hume. Fisher. Lavington. Wicksell. Keynes' Tract, Treatise, and General Theory. Patinkin's Money interest and Prices. Are money and bonds net wealth? Commodity money. Solow/Swan growth model. Money and growth. Optimal quantity of money. Transactions costs. Baumol/Tobin and Miller/Orr models of demand for money.

Money 2 (Joel Fried). Optional. Microfoundations of money, Menger, Ostroy, Jones. Money in general equilibrium theory. Clower constraints. Transactions costs. Financial markets. Tobin. CAPM. Efficient Markets. Modigliani/Miller theorem. Term structure of interest rates. Tobin portfolio choice. Friedman and Monetarism. International finance. Dornbusch overshooting. Exogenous vs endogenous money. Canadian monetary policy.

Advanced Macro (Peter Howitt). Optional. (Lovely quote from the first page: "We are Aristotelian monks, trying to solve anomolies to stop future generations wasting their time doing the same thing.". Non-Walrasian disequilibrium theory (Clower, Leijonhufvud, Barro/Grossman, Malinvaud, Benassy, etc.). Stability. Catastrophe theory(!). Price adjustment under oligopoly. Optimal control theory. Inventories. Phelps/Winter price setting with transient monopoly power (from the Phelps volume, proto New-Keynesian).

(I learned some more money/macro in David Laidler's History of Thought class. But I was the only graduate student in that class, so I'm not going to count it. My colleague Calum Carmichael, who took the same course as an undergraduate, estimates that about one quarter of the Honours economics students took that class.)

I make the follow observations:

1. The Phelps volume was clearly very influential in the late 1970's. This supports Paul Krugman's memory, and my own.

2. The beginnings of the split between New Classical and New Keynesian approaches was already apparent in the late 1970's. I saw several references to the distinction between Fisher and Phelps on the interpretation of the Phillips Curve. (Fisherian market-clearing with misperceptions vs Phelpsian disequilibrium price adjustment). This too supports Paul Krugman's memory.

3. We received a very broad education in short run macroeconomics and monetary theory. Probably much broader than today's students. That tends to support the Dark Age hypothesis.

4. But there is one glaring omission from our education: we did lots of short run business cycle theory but almost no long run growth theory. We briefly covered the Solow growth model, but only as a prelude to money and growth. There was no interest in growth theory per se! If growth theory is important, and it is, that directly contradicts the Dark Age hypothesis. We barely touched on half of macro! The late 1970's were the Dark Age, for growth theory.

Why did we ignore growth theory?

Growth theory wasn't on the agenda. It wasn't that growth was unimportant; just that there seemed to be nothing important to say about it. All the exciting policy debates were about inflation and unemployment, not long run growth. All the exciting theoretical developments were about inflation and unemployment, not long run growth. "Endogenous" growth theories (a stupid misnomer, because growth is endogenous in Solow too, just with an extremely simple functional relationship to the exogenous variables, namely g=n) came later.

Fiscal policy has been off the agenda for much the same reasons, until recently.

(5. We spent surprisingly little time on open economy macroeconomics as well, for a Canadian school.)

OK. Let's compare notes!

This is very similar to my own experience, we also did very little growth theory (nothing beyond Solow-Swan, also as a prelude to looking at whether money was "superneutral"), and I didn't take any international at all - it wasn't part of the macro sequence (the international economy was not considered very important for understanding business cycle fluctuations). The emphasis was on short-run stabilization policy, monetary policy in particular. However, my experience was a bit different in that by the time I got to graduate school in the early 1980s, the split between saltwater and freshwater economists was well underway.

Paul Krugman says:

But by 1980 or 1981 it was basically clear to everyone that the Lucas project – the attempt to explain the evidently Keynesian behavior of the economy in terms of nothing but imperfect information – had failed. So what were macroeconomic theorists supposed to do?

The answer was that they split. One faction said, in effect, “OK: we can’t explain what we think we see in terms of full maximization. So we have to assume that there are some limits to maximization – costs of changing prices, bounded rationality, whatever.” That faction became New Keynesian, saltwater economics.

The other faction said, in effect, “OK: we can’t explain what we think we see in terms of full maximization. So we must be interpreting the data wrong – things like changes in the money supply must not be driving recessions, because theory says they can’t.” That faction became real business cycle, freshwater economics.

Here's what I said about this just under two and a half years ago (edited slightly). As you can see, even though this was written well before Krugman's statement, it basically agrees with his assertion that everyone knew the New Classical model was in trouble by 1980 or 1981 (the Mishkin paper noted below was published, I believe, in 1982, but given the long publication lags the results were well known long before then). It also agrees with his comments that one faction, the New Keynesians, built upon the old Keynesian structure by giving it rational agents and microfoundations who operated in an environment beset with rigidities of one type or another (these rigidities prevent agents from fully neutralizing nominal shocks such as changes in the money supply), and the other faction reemerged as the real business cycle school:

I entered graduate school in 1980. Though it started with a pretty traditional IS-LM framework with some AD-AS thrown in, most of our time was spent learning the New Classical model. Much of the research effort at that time, at least the effort I was made aware of, was to try and punch holes in the result that comes out of the New Classical framework that only unanticipated money can affect real variables like output and employment.

This assault came on both theoretical and empirical fronts. Mishkin, for example, had published an empirical paper in the early 1980s that challenged work by Barro and others from the later 1970s supporting the New Classical model and its implication that only surprise money matters. On the theoretical front, the old Keynesian model -- which had been criticized for, among other things, lacking microeconomic foundations and lacking rational expectations -- was being reconstructed into the New Keynesian model. This model would eventually overcome theoretical objections that plagued the older Keynesian model, and it would also do a better job than the New Classical model of explaining the magnitude and persistence of business cycles and other features of the macroeconomic data. We learned some about Real Business Cycle models - but for the most part that work went on elsewhere and would surface later with more force as an alternative to the New Keynesian framework. But we were certainly made aware of the real business cycle model, e.g. arguments about reverse causality to explain statistical money income correlations. I'd say the same about growth theory - we did the Solow-Swan basics, but very little beyond that. Stabilization policy was the main issue we worried about at the time.

Does money matter? I thought so, that's what my dissertation was all about, it gave theoretical and empirical reasons to doubt the New Classical result that expected money does not affect output, but the issue of whether money matters was not settled until later. We now accept, for the most part, that the Fed can affect real interest rates and also affect the real economy, but at that time there was a very strong split within the profession on this issue. It wasn't until later that a general belief that anticipated monetary policy was a potentially useful stabilization tool surfaced in the profession. It's sometimes surprising to me today how complete the conversion on that issue has been, though it's certainly not 100%.

So, it wasn't generally agreed that money mattered, i.e. that money was a useful policy tool for stabilizing the real economy. But the Keynesian economics I learned at the time, which was in the implicit and explicit labor contracting framework for the most part, did say that money mattered. In fact, since the point was to challenge the New Classical result that money did not matter, the focus was mostly on monetary policy. As for fiscal policy, the Keynesian model we talked about - beyond the simple IS-LM version we learned at first - paid very little attention to fiscal policy, though papers such as Barro's "Are Bonds Net Wealth" were part of the conversation. Thus, when I went to graduate school - and this was partly due to who was teaching the courses - the primary focus was on whether and how changes in monetary policy affected the real economy.

In any case, even though it was a few years later than Nick's experience, we also spent considerable time on the ideas that Krugman notes have since been lost as we entered our recent "Dark Ages."

I have a sample of one: my own lecture notes from grad school. I began my MA at UWO in 1977, and continued into the PhD. I took everything in macro/money that was offered. At the time, UWO was arguably the top Canadian department in macro/money (OK, Western grads would argue for; Queens grads would argue against), and would hold up well against anywhere in the world.

Macro 1 (David Laidler). Required course. Review and critique of ISLM (lags, stocks flows and the government budget constraint, are the IS and AD curves really demand curves? [no], the missing AS curve). Crowding out debate. Non-Walrasian macro (Barro and Grossman). Say's Law. Phillips Curve (up to Phelps and Friedman). Consumption function (Friedman/Modigliani). Demand for money. Investment demand.

Macro 2 (Michael Parkin). Required for those continuing to the PhD. (I can't resist quoting from the first page of my notes here: "Economics [is] Understand + Explain Phenomena using Rational models. How could Rational Behaviour [lead to] Disaster? Market Failure."). Review and critique of Neoclassical model of labour market. Lucas and Rapping (from the Phelps volume), and why their model was logically incoherent (Michael Parkin was right on this point). Mortensen's (also from Phelps volume) search theory of unemployment. Theories of implicit wage contracts (sticky wages). Theories of price adjustment (proto New-Keynesian). ISLM plus Phillips Curve (distinction between proto New-Keynesian and New Classical interpretations of Phillips Curve). Adaptive vs. Rational expectations. Policy Irrelevance Proposition ("[deviations of output from y* are] just noise, but obviously false").

Money 1 (Don Patinkin/Peter Howitt). Optional. Hume. Fisher. Lavington. Wicksell. Keynes' Tract, Treatise, and General Theory. Patinkin's Money interest and Prices. Are money and bonds net wealth? Commodity money. Solow/Swan growth model. Money and growth. Optimal quantity of money. Transactions costs. Baumol/Tobin and Miller/Orr models of demand for money.

Money 2 (Joel Fried). Optional. Microfoundations of money, Menger, Ostroy, Jones. Money in general equilibrium theory. Clower constraints. Transactions costs. Financial markets. Tobin. CAPM. Efficient Markets. Modigliani/Miller theorem. Term structure of interest rates. Tobin portfolio choice. Friedman and Monetarism. International finance. Dornbusch overshooting. Exogenous vs endogenous money. Canadian monetary policy.

Advanced Macro (Peter Howitt). Optional. (Lovely quote from the first page: "We are Aristotelian monks, trying to solve anomolies to stop future generations wasting their time doing the same thing.". Non-Walrasian disequilibrium theory (Clower, Leijonhufvud, Barro/Grossman, Malinvaud, Benassy, etc.). Stability. Catastrophe theory(!). Price adjustment under oligopoly. Optimal control theory. Inventories. Phelps/Winter price setting with transient monopoly power (from the Phelps volume, proto New-Keynesian).

(I learned some more money/macro in David Laidler's History of Thought class. But I was the only graduate student in that class, so I'm not going to count it. My colleague Calum Carmichael, who took the same course as an undergraduate, estimates that about one quarter of the Honours economics students took that class.)

I make the follow observations:

1. The Phelps volume was clearly very influential in the late 1970's. This supports Paul Krugman's memory, and my own.

2. The beginnings of the split between New Classical and New Keynesian approaches was already apparent in the late 1970's. I saw several references to the distinction between Fisher and Phelps on the interpretation of the Phillips Curve. (Fisherian market-clearing with misperceptions vs Phelpsian disequilibrium price adjustment). This too supports Paul Krugman's memory.

3. We received a very broad education in short run macroeconomics and monetary theory. Probably much broader than today's students. That tends to support the Dark Age hypothesis.

4. But there is one glaring omission from our education: we did lots of short run business cycle theory but almost no long run growth theory. We briefly covered the Solow growth model, but only as a prelude to money and growth. There was no interest in growth theory per se! If growth theory is important, and it is, that directly contradicts the Dark Age hypothesis. We barely touched on half of macro! The late 1970's were the Dark Age, for growth theory.

Why did we ignore growth theory?

Growth theory wasn't on the agenda. It wasn't that growth was unimportant; just that there seemed to be nothing important to say about it. All the exciting policy debates were about inflation and unemployment, not long run growth. All the exciting theoretical developments were about inflation and unemployment, not long run growth. "Endogenous" growth theories (a stupid misnomer, because growth is endogenous in Solow too, just with an extremely simple functional relationship to the exogenous variables, namely g=n) came later.

Fiscal policy has been off the agenda for much the same reasons, until recently.

(5. We spent surprisingly little time on open economy macroeconomics as well, for a Canadian school.)

OK. Let's compare notes!

"An Economics of Magical Thinking"

Roman Frydman and Michael Goldberg argue that the behavioral assumptions used to motivate agents in economic models need to change:

An economics of magical thinking, by Roman Frydman and Michael D. Goldberg, Commentary, Economist's Forum: Confidence seems to be returning to markets almost everywhere, but the debates about what caused the worst crisis since the Great Depression show no sign of letting up. Instead, the spotlight has shifted from bankers, financial engineers and regulators to economists and their theories. This is not a moment too soon. These theories continue to shape the debate about fiscal stimulus, financial reform, and, more broadly, the future of capitalism, which means that they remain a danger to all concerned.

Unfortunately, the assumptions that underpin these theories are largely inscrutable to those without a Ph.D. in economics. Indeed, the debate is full of terms that mean one thing to the uninitiated and quite another to economists.

Consider “rationality.”

"Economists Need to Study Bubbles"

Robert Shiller says economists and their models need to take bubbles seriously (compare Dani Rodrik's "Blame Economists, not Economics"):

Economists need to study bubbles, reinvent models, by Robert Shiller, Commentary, Project Syndicate: The widespread failure of economists to forecast the financial crisis ... has much to do with faulty models. This lack of sound models meant that economic policymakers and central bankers received no warning of what was to come. ...

[T]he current financial crisis was driven by speculative bubbles in the housing market, the stock market, energy and other commodities markets. ... You won’t find the word “bubble,” however, in most economics treatises or textbooks. Likewise, a search of working papers produced by central banks and economics departments in recent years yields few instances of “bubbles” even being mentioned. Indeed, the idea that bubbles exist has become so disreputable ... that bringing them up in an economics seminar is like bringing up astrology to a group of astronomers.

"New Models for a New Challenge"

Stephen Cecchetti, Piti Disyatat and Marion Kohler on "whether our macroeconomic models are still relevant," and if not, what needs to change:

Integrating financial stability: new models for a new challenge, by Stephen G Cecchetti, Piti Disyatat and Marion Kohler, September 2009: Introduction Reflecting on the financial crisis that is not yet over, it is natural to ask whether our macroeconomic models are still relevant. For all of their elegance and beauty, with their microeconomic foundations and complex endogenous dynamics, they provided the basis for monetary policy that delivered a quarter of a century of stability. The Great Moderation was great - inflation was low, growth was high, and both were stable. At least, that's what we thought. In retrospect, signs of smugness abounded. Academic journals are filled with papers explaining that this stability was, in large part, a result of good policy. And policymakers listened. The economy was inherently stable, with strong self-correcting forces. The financial crashes that were so common before the mid-20th century were banished by our deep and profound understanding that had been translated into mathematical models.

What a difference a year makes!

The models neither stopped the crisis from happening nor provided guidance on how policies could cushion its impact. They failed utterly in guiding our construction of an institutional framework capable of preventing systemic financial failure. Yes, there were warnings.¹ And yes, there were models that hinted at the sources of the difficulties we now face. And yes, the economic reasoning provides the lens through which we can start to understand what happened and why. But, in the end, we ignored the risks.

In this essay, we begin with a brief review of the pre-crisis consensus that provided the basis for stabilization policy as it has been conducted since around 1980. Our main conclusion is obvious: we need to build economic models that integrate the financial sector in a serious way, accounting for the role of intermediaries with all of their linkages, both with each other and with the real economy. And, most importantly, these models must be capable of endogenously creating financial stress that can build up until the pressure leads to a crisis - that is, models in which booms and busts are normal. ...

... 4. Conclusion For macroeconomics, the biggest lesson of the financial crisis is that our models need to find a more meaningful role for finance. Episodes of financial stress are too frequent, and seem too costly, to be treated just as events that are "bad luck" and therefore of little consequence to forward-looking stabilization policy, as suggested by Lucas (2009). Rather, we should ask whether policy can and should intervene to make financial stress less likely and less damaging when it inevitably comes.

While the New Keynesian workhorse models are built around a role for stabilization policies, they appear to have stopped too soon. Understanding how to deliver economic stability must include an understanding of how to avoid financial instability.

Modeling financial booms and busts requires a model where financial imbalances matter for the real economy. As we have suggested in this essay, this means questioning a number of fundamental assumptions of the current workhorse macroeconomic models, including whether capital markets function properly, whether individuals behave rationally, whether we can really rely on the fiction of a representative agent, and whether markets clear.

As daunting a task as this may seem, prospects for progress are encouraging. Not only is there a clear awareness of the challenge (Bean (2009)), but work is already under way: heterogeneous agent models are being solved, bounded-rationality and learning are being actively explored, agent-based models are being simulated, and incomplete financial markets as well as substantive financial frictions are being introduced.

It is our hope and expectation that successfully integrating financial imbalances into models of real fluctuations will yield a toolkit for policymakers. It will guide us in the creation of new stabilization tools as well in the improved use of old ones. It will help us understand how to measure financial stress in real time and allow for transparency and accountability of policymaking in the same way that price measurement is essential for holding inflation targeting central banks accountable. Getting there will not be easy, but then, the challenge to conventional monetary policymaking 50 years ago surely appeared daunting as well. Hopefully, this time it will not take as long to get things worked out.

"How We Got to Where We are Today"

One more from Paul Krugman on the state of macroeconomics:

Memories of the Carter Administration, by Paul Krugman: One of John Updike’s novels was titled Memories of the Ford Administration; needless to say, it wasn’t about Gerald Ford — basically it was about sex, because Updike remembered the Carter years as the golden age of extramarital affairs. Similarly, this post isn’t about Jimmy Carter – it’s about macroeconomic theory. (Sorry.)

For the late 1970s was when macroeconomics experienced its great divide. It’s a period engrained in the memory of those of us who were young economists at the time, trying to find our own paths. Yet I haven’t seen a clear explanation of what went down at the time. So here’s a sketch, which I hope a serious intellectual historian will fill in someday.

Has Economics Failed Us?

A modest defense of macroeconomics:

A “modest” intellectual discipline, by Gilles Saint‑Paul, Vox EU: The current crisis has spurred a debate on the training and usefulness of economists. Some contend that economists are useless since they failed to forecast the crisis. Others claim that their training is inadequate because it relies heavily on applied mathematics at the expense of a broad view of how the economy works, informed by other disciplines such as psychology, sociology, and political science. Hence, ten British institutional economists have written a letter to the Queen, in response to that of Besley and Hennessy, where they state that “economics has turned virtually into a branch of applied mathematics, and has been become detached from real world institutions and events.”

“Consequently a preoccupation with a narrow range of formal techniques is now prevalent in most leading departments of economics throughout the world, and notably in the UK. The letter by Professors Besley and Hennessy does not consider how the preference for mathematical technique over real-world substance diverted many economists from looking at the vital whole. It does not consider the typical omission of psychology, philosophy or economic history from the current education of economists in prestigious institutions. It mentions neither the highly questionable belief in universal ‘rationality’ nor the ‘efficient markets hypothesis’ – both widely promoted by mainstream economists. It also fails to consider how economists have also been ‘charmed by the market’ and how simplistic and reckless market solutions have been widely and vigorously promoted by many economists. What has been scarce is a professional wisdom informed by a rich knowledge of psychology, institutional structures and historical precedents.”

In France, a similar debate has been going on for years between mainstream economists trained in micro, macro, and econometrics and a variety of critics who usually complain that economics is immoral, too mathematical, not pluridisciplinary enough, or sometimes too right-wing.

While economics is admittedly quite a “dry” discipline, I firmly believe that replacing the training of economists by some soft transdisciplinary melting pot would be a catastrophe.

"Did Economists Ever Get it Right?"

Antonio Fatás on the state of macroeconomics:

Did Economists Ever Get it Right?, by Antonio Fatás, Commentary, MorningStar (originally): Paul Krugman has written a nice essay on the NY Times about How did economists get it wrong?. Other economists have written on the same topic: Eichengreen, Lane, Thoma, DeLong.

My reading of these articles is that there is a good deal of consensus around the following points:

1. Many (economists and non-economists) had expressed concerns prior to the crisis about economic imbalances such as excessive asset price appreciation or current account imbalances. They pointed out to the need of an adjustment, which could come in the form of a recession. As it has always been the case with recessions, forecasting the exact timing is not easy...

2. There were several scenarios that were discussed prior to the crisis that could lead to a significant economic downturn. They involved a crash of the real estate market (which took place) and in some cases a reversal of capital flows as foreigners would stop lending to the US (or they would do so at much higher rates). This second scenario never materialized - the crash in real estate prices was enough.

3. Even among those who were concerned with the possibility of a crisis, very few understood the potential magnitude of the crisis, mainly because they could not foresee the collapse of the financial system that we witnessed a year ago. Here is a quote from Frederic Mishkin...:

The big gains in housing prices we have seen here and in many other countries have raised concerns about what might happen to economic activity if those price gains are reversed. ... Fortunately, the overall financial system appears to be in good health and the US banking system is well positioned to withstand stressful market conditions.

Clearly, our knowledge of what was happening inside the financial system and the associated risk was very limited. This is a failure of regulation and we learned the lesson the hard way.

4. No doubt that some of the research that was done by economists (those in academia) did not provide any clue about what was about to happen. As Phil Lane argues in his article, this is partly a result of specialization, not all researchers are into the business of forecasting economic downturns. But there is also no doubt that some of the research in macroeconomics has been anchored in models that do no recognize enough failures in markets or deviations from rational behavior to produce or understand some of the phenomena that led to the current crisis. Part of this is because of ideological reasons (some want to believe that markets always work), part of this is because the "beauty" of dealing with simple models (the argument made by Krugman in his article).

One thing that I find missing in all those articles is whether there was any difference between the current crisis and the previous ones. I am not sure there is much difference. Prior to the (mild) recession of 2001 we also witnessed very similar dynamics: many expressed concerns about the valuation of stocks (more so for tech stocks). But they called the crisis way before it happened. Once it happened, we all asked the question "How did we get it so wrong?". The difference with the current crisis is that this one is bigger, so more questions are being asked. Also, economic policy has played a much stronger role during the crisis, which has probably led to a stronger debate around economics.

It is also interesting to see that during the boom year, there was as much skepticism of economists' forecasts as today so even if some economists were getting it wrong, it is unclear how much they were driving market expectations or investment and spending decisions.

We will have to wait for the next crisis and see if things have changed or we just need to conclude that economists "will never get it right".

On point 4, I'll just add one thing. Besides the anchoring from ideology and beauty, part of the problem too, as I've argued before, is that we weren't asking the right questions. [I should also add that the reason we didn't ask the right questions may be tied to ideology, and perhaps the elegance of the supporting theoretical structure as well, that led to the belief in self-correction and self-protection from large shocks that made massive meltdown very unlikely if not impossible.] I have criticized regulators for not having plans ready to deal with too big to fail institutions. One thing everyone seems to agree on is that the ad hoc response from regulators made things worse, and we need to be better prepared with plans to dismantle these firms without destabilizing markets next time around (and do our best to prevent problems from developing to begin with, including regulating connectedness). The fact that we were caught without such plans was a big handicap in dealing with the unfolding crisis.

But the same can be said about macroeconomics. We didn't plan for a big crisis either. That is, we didn't take the threat of a large breakdown seriously enough to take the time to develop a theoretical framework that could anticipate these problems and guide us in how to deal with them if they occurred. There were stabs in this direction, but it was by no means a major effort or thought to be one of the more important research questions. We spent a lot of time developing stabilization policy, but it wasn't within a framework that was particularly helpful for the kinds of problems we are facing today. We had no plans on the shelf that we could rely upon when the crisis hit, and what we have seen from macroeconomists is the same kind of ad hoc scramble for an effective response that many of us have criticized regulators for. But if macroeconomists had taken the possibility of a massive meltdown seriously before it happened and developed the theoretical apparatus we are now calling for now that we have seen that such events are, in fact, possible, then perhaps regulators would have been more inclined to think through this possibility and get ready for it. I don't think the blame is all theirs.

What's Wrong with Macroeconomics?

Some recent contributions to "what's wrong with macroeconomics?":

• Nick Rowe: John Cochrane, Paul Krugman, and Say's Law, again
• Brad Delong: In Which Nick Rowe Says John Cochrane Commits "Small But important" Errors...
• Free Exchange: Which caricature do you prefer?
• Mario Rizzo: The Great Moderation in Macroeconomics
• Vernon Smith and David Colander: Causes of the Crisis
• Robert Gordon: Is Modern Macro or 1978-era Macro More Relevant to the Understanding of the Current Economic Crisis? [introduction posted below]
• David Warsh: Economics and Its Discontents [posted below]
• Paul Krugman: Freshwater Rage [posted below]
• Brad DeLong: More Musings on the Total Intellectual Train Wreck that is Today's Chicago School...
• Free Exchange: What the economists aren't explaining
• Barkley Rosser: How Seriously Wrong John Cochrane Is

Added 9/15:

• Brad DeLong: The Chicago School's Intellectual Collapse Continued: Richard Posner Is Uranus...
• Peter Dorman: Did Krugman Get it Right on How the Economists Got it Wrong?
  (part 2)
• Murat Iyigun: Paul: Hand Johnny Some Rope, Will You?
• Justin Wolfers: Lucas on the Shortcomings of Modern Macro

Added 9/16:

• Malin Hu: The Curious Case of Macroeconomics
• Mark Thoma: Who Has All the Answers?
• Murat Iyigun: Johnny B. Goode

Added 9/17:

• John Quiggin: The Macro Wars
• John Quiggin: Micro-based macro-introduction
• Mark Thoma: "Did Economists Ever Get it Right?"
• Narayana Kocherlakota: Some Thoughts on the State of Macro

Added 9/18:

• Arnold Kling: On the state of macro
• Justin Wolfers: The macro wars ... or not
• Michael Roberts: About the Great Marcoeconomic Debate
• Barry Ritholtz: The Mystery of the Awful Economists (3/05 alternate link)
• Barry Ritholtz: Mystery of the Awful Economists, part 2 (4/05)
• Barry Ritholtz: Mystery of the Awful Economists (Part III) (4/05)
• Barry Ritholtz: The Illusory World of Economic Forecasting (9/06)
• Barry Ritholtz: RIP Chicago School of Economics: 1976-2008
• Barry Ritholtz: Read It Here First: “What Good Are Economists?”
• Barry Ritholtz: Why Economists Missed the Crises

Added 9/19:

• David K. Levine: An Open Letter to Paul Krugman
• Gilles Saint‑Paul: A "modest" intellectual discipline
• David Gruen: Outside the Bubble
• Paul Krugman: Memories of the Carter Administration

Added 9/20

• Stephen Cecchetti, Piti Disyatat and Marion Kohler: Integrating financial stability: new models for a new challenge
• Brad DeLong: The Intellectual Bankruptcy of the Chicago School Infests St. Louis...

Added 9/21

• Karl Smith: Krugman vs. Cochrane
• Brad DeLong: The State of Macro: Narayana Kocherlakota Leaves Me Puzzled

Added 9/22

• Leiter Reports: Alex Rosenberg on Cochrane and Economics
• Alex Tabarrok: What it means to predict a crisis
• Brad DeLong: Karl Smith Is Somewhat Bewildered by the State of Modern Macro

Also:

• Paul Krugman: How Did Economists Get It So Wrong?
• Barry Eichengreen: The Last Temptation of Risk
• Philip Lane: Economists and the Crisis
• David Altig: Economists got it wrong, but why?
• John Cochrane: How did Paul Krugman get it so Wrong?
• David Laidler: Lucas, Keynes, and the Crisis
• Mark Thoma: "The Great Multiplier Debate"
• Antonio Fatas and Ilian Mihov: Did economists ever get it right?
• Robert Levine: On Krugman
• The Economist: Lucas Roundtable (Lucas)
• The Economist: What went wrong with economics
• The Economist: The other-wordly philosophers
• The Economist: Efficiency and beyond
• Cosmic Variance: Mistaking Beauty for Truth
• Mark Gertler: The State of Macroeconomics (scroll down)
• Willem Buiter The unfortunate uselessness of most ’state of the art’ academic monetary economics
• Mark Thoma "The Unfortunate Uselessness of Most 'State of the Art' Academic Monetary Economics"
• Tim Harford Are those who sweat the big stuff in meltdown?
• Francesco Caselli: Economists are actively engaged in seeking remedies to the crisis
• Mark Thoma Macroeconomic Meltdown?
• Daniel Kahneman: Irrational everything
• Mark Thoma: Are Macroeconomic Models Useful?
• Roman Frydman: Mathematical Formalism in Economics
• Dani Rodrik: Blame economists, not economics
• Mark Thoma The State of Macroeconomics (scroll down)
• George Waters: "Equilibrium and Meltdown"
• Paul Krugman and Roman Frydman: "The Gratuitous Ignorance of Many of our Economists"
• Mark Thoma: Can Econometricians Tell Us which Macroeconomic Model is Best?
• David Colander, Hans Föllmer, Armin Haas, Michael Goldberg, Katarina Juselius, Alan Kirman, and Thomas Lux The Financial Crisis and the Systemic Failure of Academic Economics
• Kenneth Arrow: Risky business

[This list is incomplete, so please add any I've missed in comments.]

Who Has All the Answers?

Justin Fox:

Hyman Minsky didn't have all the answers, by Justin Fox: Economist Hyman Minsky, who never got much attention while he was alive, has become one of the big celebrities of this financial crisis. In Sunday's Boston Globe, Stephen Mihm has the best account of Minsky's life and significance that I've seen so far. A sample:

Today most economists, it's safe to say, are probably reading Minsky for the first time, trying to fit his unconventional insights into the theoretical scaffolding of their profession. If Minsky were alive today, he would no doubt applaud this belated acknowledgment, even if it has come at a terrible cost. As he once wryly observed, “There is nothing wrong with macroeconomics that another depression [won't] cure.”

But did Minsky really have much to add beyond the crucial insight that financial systems are inherently unstable? His former student Eric Falkenstein, responding on his blog to Mihm's article, isn't so sure:

I was Minsky's TA while a senior at Washington University in St.Louis in 1987, and took a couple of his advanced classes, which regardless of the official name, were all just classes in Minskyism. He was a maverick, but perhaps a bit too much, being a little too dismissive of others, as he hated the traditional Samuelson/Solow Keynesians as much as the Friedmanite Monetarists. He always thought a market collapse was just around the corner. The S&P was 250 when I took his course, it went to 1500 in 2007 and then back to 735 in 2009. Does that prove he was right all along? ...

The problem ... is that his top-down theory is rejected by the data. Aggregate leverage ratios do not closely correspond to business cycles. If Minsky took microeconomics more seriously he could have made his theory more relevant, by noting that crises tend to occur in specific subsets in the economy: in 1990, hotels and Commercial real estate, in 2001, high tech, in 2008, mortgages. The mistake is not one made in aggregate, but in different sectors each recession. By noting these areas, but not the aggregate economy, had too much leverage, and depended on expected future increases in collateral value, he might have been more successful proselytizing his colleagues. But he was a traditional Keynesian, who liked to look at aggregate equations, like Profits=Investment + Deficits + Net Imports.

I think the broader point here is that there is no one Theory That Explains Everything in economics. Neoclassical economics certainly doesn't explain everything. Neither does Minskyism. Nor Austrian business cycle theory. Nor complexity theory. It seems like the best approach would be an eclectic one that takes lots of different economic models into account. But eclecticism doesn't get you far in academia.

There is no grand, unifying theoretical structure in economics. We do not have one model that rules them all. Instead, what we have are models that are good at answering some questions - the ones they were built to answer - and not so good at answering others.

If I want to think about inflation in the very long run, the classical model and the quantity theory is a very good guide. But the model is not very good at looking at the short-run. For questions about how output and other variables move over the business cycle and for advice on what to do about it, I find the Keynesian model in its modern form (i.e. the New Keynesian model) to be much more informative than other models that are presently available (as to how far this kind of "eclecticism" will get you in academia, I'll just note that this is exactly the advice Mishkin gives in his textbook on monetary theory and policy).

But the New Keynesian model has its limits. It was built to capture "ordinary" business cycles driven by price rigidities of the sort that can be captured by the Calvo model model of price rigidity. The standard versions of this model do not explain how financial collapse of the type we just witnessed come about, hence they have little to say about what to do about them (which makes me suspicious of the results touted by people using multipliers derived from DSGE models based upon ordinary price rigidities). For these types of disturbances, we need some other type of model, but it is not clear what model is needed. There is no generally accepted model of financial catastrophe that captures the variety of financial market failures we have seen in the past.

But what model do we use? Do we go back to old Keynes, to the 1978 model that Robert Gordon likes, do we take some of the variations of the New Keynesian model that include effects such as financial accelerators and try to enhance those, is that the right direction to proceed? Are the Austrians right? Do we focus on Minsky? Or do we need a model that we haven't discovered yet?

We don't know, and until we do, I will continue to use the model I think gives the best answer to the question being asked. The reason that many of us looked backward for a model to help us understand the present crisis is that none of the current models were capable of explaining what we were going through. The models were largely constructed to analyze policy is the context of a Great Moderation, i.e. within a fairly stable environment. They had little to say about financial meltdown. My first reaction was to ask if the New Keynesian model had any derivative forms that would allow us to gain insight into the crisis and what to do about it and, while there were some attempts in that direction, the work was somewhat isolated and had not gone through the type of thorough analysis needed to develop robust policy prescriptions. There was something to learn from these models, but they really weren't up to the task of delivering specific answers. That may come, but we aren't there yet.

So, if nothing in the present is adequate, you begin to look to the past. The Keynesian model was constructed to look at exactly the kinds of questions we needed to answer, and as long as you are aware of the limitations of this framework - the ones that modern theory has discovered - it does provide you with a means of thinking about how economies operate when they are running at less than full employment. This model had already worried about fiscal policy at the zero interest rate bound, it had already thought about Says law, the paradox of thrift, monetary versus fiscal policy, changing interest and investment elasticities ina  crisis, etc., etc., etc. We were in the middle of a crisis and didn't have time to wait for new theory to be developed, we needed answers, answers that the elegant models that had been constructed over the last few decades simply could not provide. The Keyneisan model did provide answers. We knew the answers had limitations - we were aware of the theoretical developments in modern macro and what they implied about the old Keynesian model - but it also provided guidance at a time when guidance was needed, and it did so within a theoretical structure that was built to be useful at times like we were facing. I wish we had better answers, but we didn't, so we did the best we could, and the best we could involved at least asking what the Keynesian model would tell us, and then asking if that advice has any relevance today. Sometimes if didn't, but that was no reason to ignore the answers when it did.

"Freshwater Rage"

Paul Krugman responds to the response to his criticism of macroeconomics:

Freshwater rage, by Paul Krugman: I’m still on the road, with only sporadic internet access. So I’ve missed out on much of the outpouring of rage over my magazine article. I gather, though, that the usual suspects are utterly outraged at my suggestion that freshwater macro has spent several decades heading down the wrong path. They’re smart! They work hard, using hard math! How dare I say such a thing?

And all of this, of course, without a hint of irony.

For when freshwater macro took over a good part of the field, its leaders gleefully dismissed all the work Keynesian economists had done over the previous few decades, often with sneers and sniggers.

And that same adolescent quality was evident in the reactions to the Obama administration’s attempts to deal with the crisis — as Brad DeLong points out, people like Robert Lucas and John Cochrane (not to mention Richard Posner, who isn’t a macroeconomist but gets his take from his colleagues) didn’t say that when serious scholars like Christina Romer based policy recommendations on Keynesian economics, they were wrong; the freshwater crowd declared that anyone with Keynesian views was, by definition, either a fool or intellectually dishonest.

So the freshwater outrage over finding their own point of view criticized is, you might think, a classic case of people who can dish it out but can’t take it.

But it’s actually even worse than that.

When freshwater macro came in, there was an active purge of competing views: students were not exposed, at all, to any alternatives. People like Prescott boasted that Keynes was never mentioned in their graduate programs. And what has become clear in the recent debate — for example, in the assertion that Ricardian equivalence rules out any effect from government spending changes, which is just wrong — is that the freshwater side not only turned Keynes into an unperson, but systematically ignored the work being done in the New Keynesian vein. Nobody who had read, say, Obstfeld and Rogoff would have been as clueless about the logic of temporary fiscal expansion as these guys have been. Freshwater macro became totally insular.

And hence the most surprising thing in the debate over fiscal stimulus: the raw ignorance that has characterized so many of the freshwater comments. Above all, we’ve seen the phenomenon of well-known economists “rediscovering” Say’s Law and the Treasury view (the view that government cannot affect the overall level of demand), not because they’ve transcended the Keynesian refutation of these views, but because they were unaware that there had ever been such a debate.

It’s a sad story. And the even sadder thing is that it’s very unlikely that anything will change: freshwater macro will get even more insular, and its devotees will wonder why nobody in the real world of policy and action pays any attention to what they say.

I am not quite as pessimistic about the prospects for change, but many people have their life's work wrapped up in a particular brand of model and they will defend that work aggressively, so I do agree that it's likely to come in spite of rather than because of the old guard.

"Economics and Its Discontents"

David Warsh says economics has served us well in dealing with the aftermath of the crisis and that, while macroeconomics has problems, "The last thing we needs is a civil war in economics":

Economics and Its Discontents, by David Warsh: In the aftermath of the worst scare since the 1930s, economists have identified a new culprit to share the blame for the subsequent mess – themselves, or rather those among their tribe with whom they disagree. No longer are greedy Wall Street bankers, feckless regulators and a blasé Federal Reserve Board the only suspects. The economics profession has joined them in the dock.

“How Did Economists Get It So Wrong?” asked Paul Krugman last week in The New York Times Magazine. “The Financial Crisis and the Systemic Failure of the Economics Profession, “ wrote David Colander, Alan Kirman and several others in Critical Review.

“The Other-Worldly Philosophers,” offered the headline of thoughtful examination in The Economist two months ago. On its cover, a textbook – “Modern Economic Theory” – melted into a puddle. The editorial began, “Of all the bubbles that have been pricked, few have burst more spectacularly than the reputation of economics itself.”

Is that really true? Or is the hubbub another case of what Sigmund Freud, in Civilization and Its Discontents, termed “the narcissism of small differences” – the tendency to exaggerate the dissimilarities of those who resemble us in an effort to buttress our own self-regard?

"Is Modern Macro or 1978-era Macro More Relevant to the Understanding of the Current Economic Crisis?"

Robert Gordon argues that "modern macro neglects the basic sources of both impulses and propagation mechanisms of business cycles," and that we should return to "1978-era macroeconomics." Here's the introduction to his paper:

Is Modern Macro or 1978-era Macro More Relevant to the Understanding of the Current Economic Crisis?, by Robert Gordon: 1. Introduction For more than three decades since 1978 hundreds of U. S. macroeconomists have developed what is often called the "modern macroeconomics" of business cycle fluctuations.[1] Until recently, there was evident self-satisfaction that macroeconomic truth had been discovered, that old errors had been buried, and that a long period of warfare between new classicals and new Keynesians had ended as a consensus had emerged based on Dynamic Stochastic General Equilibrium (DSGE) models that combined new-classical market clearing with the new-Keynesian contribution of sticky prices.[2]

Along the way numerous modern macroeconomists concluded that the U. S. "Great Moderation" of macroeconomic volatility in the 1984-2007 period (as compared to higher volatility in the earlier 1947-84 postwar interval) was a side benefit of modern analysis. However since 2007 the world economy has entered a crisis of sub-prime mortgage defaults, excessive leveraging followed by deleveraging, output and employment meltdowns, and an enormous destruction of wealth. The Great Moderation is dead. Neither the proponents of modern macro nor the adherents of Keynesian ideas anticipated the crisis in advance. But even the most avid supporters of the modern macro camp have thus far failed to provide any intellectual links with their preferred explanations of business cycle downturns based on technology retardation, changes in preferences, or tightness in monetary policy.

This paper is not an endorsement of 1936-era Keynesian thought, but rather revives an alternative intellectual paradigm called "1978-era macroeconomics."

"Rethinking GDP"

Joseph Stiglitz says we need better measures of economic performance:

Rethink GDP fetish, by Joseph E. Stiglitz, Commentary, Project Syndicate: ...Eighteen months ago, French President Nicolas Sarkozy established an international Commission on the Measurement of Economic Performance and Social Progress, owing to his dissatisfaction - and that of many others - with the current state of statistical information about the economy... On Sept. 14, the commission will issue its long-awaited report.

The big question concerns whether GDP provides a good measure of living standards. In many cases, GDP statistics seem to suggest that the economy is doing far better than most citizens' own perceptions. Moreover, the focus on GDP creates conflicts: political leaders are told to maximize it, but citizens also demand that attention be paid to enhancing security, reducing pollution, and so forth - all of which might lower GDP growth.

The fact that GDP may be a poor measure of well-being, or even of market activity, has, of course, long been recognized. But changes in society and the economy may have heightened the problems...

For example,... in one key sector - government - we ... often measure the output simply by the inputs. If government spends more - even if inefficiently - output goes up. In the last 60 years, the share of government output in GDP has increased [substantially]... So what was a relatively minor problem has now become a major one.

Likewise, quality improvements ... account for much of the increase in GDP nowadays. But assessing quality improvements is difficult. ...

Another marked change in most societies is an increase in inequality. ... If a few bankers get much richer, average income can go up, even as most individuals' incomes are declining. So GDP per person statistics may not reflect what is happening to most citizens.

We use market prices to value goods and services. But ... the ... pre-crisis profits of banks - one-third of all corporate profits - appear to have been a mirage.

This realization casts a new light not only on our measures of performance, but also on the inferences we make. Before the crisis, when U.S. growth ... seemed so much stronger than that of Europe, many Europeans argued that Europe should adopt U.S.-style capitalism. Of course, anyone who wanted to could have seen American households' growing indebtedness, which would have gone a long way toward correcting the false impression of success given by the GDP statistic.

Recent methodological advances have enabled us to assess better what contributes to citizens' sense of well-being... These studies, for instance, verify and quantify what should be obvious: the loss of a job has a greater impact than can be accounted for just by the loss of income. They also demonstrate the importance of social connectedness.

Any good measure of how well we are doing must also take account of sustainability..., our national accounts need to reflect the depletion of natural resources and the degradation of our environment.

Statistical frameworks are intended to summarize what is going on in our complex society in a few easily interpretable numbers. It should have been obvious that one couldn't reduce everything to a single number, GDP. The report by the Commission on the Measurement of Economic Performance and Social Progress ... should ... provide guidance for creating a broader set of indicators that more accurately capture both well-being and sustainability...

"Tacit Knowledge"

When I learned econometrics, I was told there is the formal treatment of the topic, and of course you need to know that, but equally important to the practitioner is the "art of econometrics." It wasn't something you could learn from books, it was a different kind of knowledge, perhaps best described (?) as "tacit knowledge" that allows you to make the right choices -- there are more judgment calls than you might imagine involved in actually specifying a model and testing an hypothesis -- and arrive at the empirical specification that will shed the most light on the question of interest. This is from Daniel Little:

Tacit knowledge, by Daniel Little: Scientist and philosopher Michael Polanyi introduced the idea of "tacit knowledge" in his 1958 book, Personal Knowledge: Towards a Post-Critical Philosophy (Google Books link). The book was presented as a critique of the positivist conception of scientific knowledge and the idea of knowledge as a system of logical statements. Polanyi was trained as a physician before World War I; worked as a research chemist between the wars; and found his voice as a philosopher of science subsequently. (Michael Polanyi is the brother of Karl Polanyi, discussed here.)

This is an interesting concept, and one that captures an important dimension of knowledge that is absent in most philosophical treatments of epistemology ("knowledge is a system of true justified beliefs"). The simple idea is that there are domains of knowledge that are not represented propositionally or as a system of statements, but are rather somehow embodied in the knower's cognitive system in a non-propositional form. This aspect of knowledge is more analogous to "knowing how" than "knowing that". Polanyi gives the example of a physician in training learning to "read" an x-ray. What is first perceived simply as an unintelligible alternation of light and dark areas, eventually is perceived by the experienced radiologist as a picture of a lung with a tumor. So the physician has somehow acquired a set of perceptual and conceptual skills that cannot be precisely codified but that permit him/her to gain a much more knowledgeable understanding of the patient's hidden disease than the novice. (This seems to be part of what Malcolm Gladwell is getting at in Blink: The Power of Thinking Without Thinking.)

"Economists and the Crisis"

This is from Philip Lane at the Irish Economy Blog:

Economists and the Crisis, by Philip Lane: As has been tracked by several previous posts on this blog, there is by now a considerable debate on what the crisis teaches us about the role of economists.

One dimension of this debate has focused on the failure by the economics profession to predict the onset of the crisis. A second quasi-related dimension relates to the failure to sufficiently appreciate the instability of the pre-crisis financial system. To some, these failures suggest that those economists who did not accurately predict the crisis should have no role in resolving the crisis and constructing the new post-crisis economic system. This debate is playing out at the global level and also in relation to the domestic situation.

These are all big issues and I do not attempt to provide a comprehensive set of answers here. Nevertheless, it may be useful to make a few points. I also mainly focus on the role of academic/research economists and the field of macroeconomics.

First, there is no doubt that the crisis has underlined a mis-allocation of research resources. Over the last 15-20 years, monetary economics in relation to the advanced economies has focused on the analysis of ‘low-amplitude’ business cycles. While business cycles were certainly shallow during this period, the social costs of rare-but-large crashes are so large that it is clear in retrospect that too few researchers were focused on the economics of large crises. (An exception relates to those who focused on emerging market economies, where a lot of analysis has been conducted of the recurrent crises that have affected these economies.)

One role for the economics profession is to attempt to forecast the future behaviour of the economy. This is mainly done by economists in policy roles (since policymaking requires projections of future economic performance) and economists in financial firms (since the return on financial investments also turn on future economic performance).

In fact, very few academic economists get involved in this task: to do it well really requires a lot of data resources and and the tracking of many variables, such that is a full-time task that is best conducted by large teams of economists. However, even if not involved in day-to-day forecasting, academic economists can play an important role by providing an independent voice and focusing on ‘big picture’ issues such as whether the forecasting models are well designed and taking a longer-term horizon for forecasting (most forecasting is concerned with quarter-by-quarter developments or, at most, a 1-3 year horizon).

In fact, the main contribution may not be in forecasting per se but in detailing possible contrarian scenarios in order to challenge the conventional wisdom. This can be very valuable but the limitation is that such ‘Cassandra’ warnings typically cannot be tied to a specific date and it is tempting for the mainstream to dismiss such warnings if they do not quickly come to pass. Moreover, if a forecaster’s reputation depends on short-term performance, a bearish economist may quickly lose credibility if boom conditions persist and the day of reckoning is postponed.

However, the main goal in outlining low-probability but high-cost risk scenarios is not so much to alter ‘central’ forecasts but to encourage decisionmakers to adopt prudent strategies that are robust to the occurrence of the ‘disaster’ scenario. In fact, the ideal is that decisionmakers are sufficiently prudent that the risk of the disaster scenario recedes and those offering the Cassandra warnings never see their worst fears realised.

Certainly, we can point to several academic and non-academic economists who were assiduous in making warnings about the consequences of the lending boom and these deserve great credit.

For many others in academia, their research activities were directed towards other questions. In relation to policy-relevant macroeconomics, a major focus has been on conducting research on ‘institutional design.’ That is, rather that focusing on macroeconomic forecasting, many have opted to contribute to the design of policy frameworks that can deliver enhanced stability and lower the cost of crises should they occur. This includes work on: the zero-bound problem in interest rate policy; macro-prudential financial regulation; counter-cyclical fiscal policies; tax policies vis-a-vis the property sector; the establishment of insurance devices such as reserve funds and rainy-day funds and other mechanisms to mitigate macroeconomic risks. Work on such issues has intensified since the onset of the crisis, together with much innovative work in areas such as the design of ‘non-orthodox’ monetary interventions.

Accordingly, the state of macroeconomics is in flux. While there is much to regret concerning the course of pre-crisis research, it is also true that many of the technical innovations over the last 20 years are now being applied in exciting ways to design crisis-resolution policies. Indeed, it is somewhat ironic that the crisis has led to a tremendous resurgence in interest in macroeconomics: economics is much more interesting in ‘bad times’ than in ‘good times’.

Another very promising development has been the enhanced integration of macroeconomics and finance. Many leading finance economists have responded very quickly to the crisis and have written superb analyses of various dimensions of the crisis and developed innovative new policies to restore financial stability and reduce the risk of future crises.

Many of these points apply equally to both the global and local economic situations. Regarding academic research on the Irish economy, I will point out a ’scale’ problem - the small size of the Ireland means that it is difficult to build a successful academic career by focusing on the local economy, in view of the limited publication opportunities and the small size of the domestic profession. This is a problem.

Finally, some have argued that the crisis has shown the limitations of economics. At one level, this is certainly true and an important lesson for all types of decisionmakers is to recognise that the future is uncertain and relying on Panglossian forecasts (where no downside risk is ever realised) is not an appropriate risk management strategy. It is also the case that economics needs to learn more from adjacent disciplines (psychology, history and the other social sciences). However, the likely evolution is an ‘adapted/enriched’ economics rather than a fully-symmetric multi-disciplinary approach, since the technical basis for most economic policy analyses is predominantly driven by economic factors.

Beauty and the Macroeconomic Beast

Discover Magazine's blog Cosmic Variance responds to Paul Krugman's essay How Did Economists Get it So Wrong?:

...One part of the essay worth commenting on, or at least musing about, is the punchline. Krugman thinks that a major factor leading to the failures of economics to understand the mess we’re currently in was the temptation to think that beautiful models must be right. ...

Without knowing much of anything about the relevant issues, I nevertheless suspect that this moral might be a bit too pat. Sure, people can fall in love with beautiful theories, to the extent that they overestimate their relationship to reality. But it seems likely to me that the correct way of understanding all this, once it’s properly understood, will look pretty beautiful as well. General relativity is widely held up as an example of a beautiful theory — and it is, when understood in its own language. But if you put the prediction of GR in the Solar System into the language of pre-existing Newtonian physics (which you could certainly do), it would look ugly and ad hoc. Likewise, Newton’s theory itself is quite elegant, when phrased in the language of potentials on a fixed spacetime background; but if you express the theory in terms of differential geometry (which you could certainly do), it looks like a mess. Sometimes the beauty/ugly distinction between theoretical conceptions is more a matter of how well we understand them, and less about their intrinsic qualities.

So my counter-hypothesis would be that it wasn’t beauty that was the problem, it was complacency. If you have a model that is beautiful and works well enough, you’re tempted to take pride in it rather than pushing it to extremes and looking for problems. I suspect that there is a very beautiful theory of economics out there waiting to be developed, one that understands perfectly well that individuals aren’t rational and markets aren’t perfect. One that has even more impressive-looking equations than the current favored models! Beauty isn’t always a cop-out.

I'm not sure if complacency is the right word, there is great credit in the profession for finding anomalies within the existing framework. The problem is that it's almost always possible to tweak the existing model in some way that rationalizes any new regularity discovered in the data that is at odds with existing theory. And without the ability to take the models to the lab and subject them to experiments, there's no way to immediately test the augmented theoretical structures (and as I've noted before, of course the models will pass the test with existing data, they were built to rationalize all the important known regularities in the data). It's only when a lot of time has passed and we have enough data to sort things out, or when we have large shocks of the type we've recently had, that we get the kind of information that we need to subject models to rigorous tests.

In addition, we didn't have "a model," we had competing models all of which claimed to be able to explain the known regularities in the data, and while I think the evidence does point in one direction, the existing pre-crash evidence was not conclusive enough to seal the case for one model over another, or to point in a new direction altogether. As I said once before, "I think what has happened will have a much bigger impact on the profession and the models it uses to describe the world than most economists currently realize," and hopefully this shakeup will move macroeconomic theory in a positive direction. With any luck, "the correct way of understanding all this, once it’s properly understood, will look pretty beautiful."

"John Stuart Mill as a Social Science Founder"

Daniel Little discusses John Stuart Mill's significant contributions to the creation of a positivist science of society:

John Stuart Mill as a social science founder, by Daniel Little: John Stuart Mill was Britain's leading thinker when it came to issues having to do with logic and scientific knowledge in the mid-nineteenth century. His System of Logic was first published in 1843 and was reprinted in numerous editions, and it constituted a comprehensive treatment of scientific knowledge and inference within the empiricist tradition. 

The book devoted an entire section to the logic of what Mill referred to as the "moral sciences" (Book VI, published separately as The Logic of the Moral Sciences). He defined the moral sciences as those areas of study having to do with human dispositions, character, and action, extending from psychology to social science. The conception of social science knowledge that he presents has had a deep impact on subsequent thinking about "scientific" social analysis and is worth examining again. (Here is a link to the Gutenberg etext edition of the System of Logic.)

Mill developed a general vision of science that was derived from the best current examples of progress in the natural sciences, and he then applied this vision to the effort to understand human and social phenomena scientifically. Putting his vision simply, science consists of the discovery of general causal laws based on systematic empirical observation. It lays the framework for a positivist conception of social science, and it prepares a charge of "Not scientific!" to social scientists who deviate from these central positivist tenets.

"Revisiting Popper"

Is it true that "History and society are not law-governed systems for which we might eventually hope to find exact and comprehensive theories"?:

Revisiting Popper, by Daniel Little: Karl Popper's most commonly cited contribution to philosophy and the philosophy of science is his theory of falsifiability (The Logic of Scientific Discovery, Conjectures and Refutations: The Growth of Scientific Knowledge). (Stephen Thornton has a very nice essay on Popper's philosophy in the Stanford Encyclopedia of Philosophy.) In its essence, this theory is an alternative to "confirmation theory." Contrary to positivist philosophy of science, Popper doesn't think that scientific theories can be confirmed by more and more positive empirical evidence. Instead, he argues that the logic of scientific research is a critical method in which scientists do their best to "falsify" their hypotheses and theories. And we are rationally justified in accepting theories that have been severely tested through an effort to show they are false -- rather than accepting theories for which we have accumulated a body of corroborative evidence. Basically, he argues that scientists are in the business of asking this question: what is the most unlikely consequence of this hypothesis? How can I find evidence in nature that would demonstrate that the hypothesis is false? Popper criticizes theorists like Marx and Freud who attempt to accumulate evidence that corroborates their theories (historical materialism, ego transference) and praises theorists like Einstein who honestly confront the unlikely consequences their theories appear to have (perihelion of Mars).

At bottom, I think many philosophers of science have drawn their own conclusions about both falsifiability and confirmation theory: there is no recipe for measuring the empirical credibility of a given scientific theory, and there is no codifiable "inductive logic" that might replace the forms of empirical reasoning that we find throughout the history of science. Instead, we need to look in greater detail at the epistemic practices of real research communities in order to see the nuanced forms of empirical reasoning that are brought forward for the evaluation of scientific theories. Popper's student, Imre Lakatos, makes one effort at this (Methodology of Scientific Research Programmes; Criticism and the Growth of Knowledge); so does William Newton-Smith (The Rationality of Science), and much of the philosophy of science that has proceeded under the rubrics of philosophy of physics, biology, or economics is equally attentive to the specific epistemic practices of real working scientific traditions. So "falsifiability" doesn't seem to have a lot to add to a theory of scientific rationality at this point in the philosophy of science. In particular, Popper's grand critique of Marx's social science on the grounds that it is "unfalsifiable" just seems to miss the point; surely Marx, Durkheim, Weber, Simmel, or Tocqueville have important social science insights that can't be refuted by deriding them as "unfalsifiable". And Popper's impatience with Marxism makes one doubt his objectivity as a sympathetic reader of Marx's work.

Are Macroeconomic Models Useful?

There has been no shortage of effort devoted to predicting earthquakes, yet we still can't see them coming far enough in advance to move people to safety. When a big earthquake hits, it is a surprise. We may be able to look at the data after the fact and see that certain stresses were building, so it looks like we should have known an earthquake was going to occur at any moment, but these sorts of retrospective analyses have not allowed us to predict the next one. The exact timing and location is always a surprise.

Does that mean that science has failed? Should we criticize the models as useless?

No. There are two uses of models. One is to understand how the world works, another is to make predictions about the future. We may never be able to predict earthquakes far enough in advance and with enough specificity to allow us time to move to safety before they occur, but that doesn't prevent us from understanding the science underlying earthquakes. Perhaps as our understanding increases prediction will be possible, and for that reason scientists shouldn't give up trying to improve their models, but for now we simply cannot predict the arrival of earthquakes.

However, even though earthquakes cannot be predicted, at least not yet, it would be wrong to conclude that science has nothing to offer. First, understanding how earthquakes occur can help us design buildings and make other changes to limit the damage even if we don't know exactly when an earthquake will occur. Second, if an earthquake happens and, despite our best efforts to insulate against it there are still substantial consequences, science can help us to offset and limit the damage. To name just one example, the science surrounding disease transmission helps use to avoid contaminated water supplies after a disaster, something that often compounds tragedy when this science is not available. But there are lots of other things we can do as well, including using the models to determine where help is most needed.

So even if we cannot predict earthquakes, and we can't, the models are still useful for understanding how earthquakes happen. This understanding is valuable because it helps us to prepare for disasters in advance, and to determine policies that will minimize their impact after they happen.

All of this can be applied to macroeconomics. Whether or not we should have predicted the financial earthquake is a question that has been debated extensively, so I am going to set that aside. One side says financial market price changes, like earthquakes, are inherently unpredictable -- we will never predict them no matter how good our models get (the efficient markets types). The other side says the stresses that were building were obvious. Like the stresses that build when tectonic plates moving in opposite directions rub against each other, it was only a question of when, not if. (But even when increasing stress between two plates is observable, scientists cannot tell you for sure if a series of small earthquakes will relieve the stress and do little harm, or if there will be one big adjustment that relieves the stress all at once. With respect to the financial crisis, economists expected lots of little, small harm causing adjustments, instead we got the "big one," and the "buildings and other structures" we thought could withstand the shock all came crumbling down. On prediction in economics, perhaps someday improved models will allow us to do better than we have so far at predicting the exact timing of crises, and I think that earthquakes provide some guidance here. You have to ask first if stress is building in a particular sector, and then ask if action needs to be taken because the stress has reached dangerous levels, levels that might result in a big crash rather than a series of small stress relieving adjustments. I don't think our models are very good at detecting accumulating stress, in large part because when we are not at the long-run equilibrium, we model the short-run as though every market clears at every point in time. This means that there are no stresses continuously building in these models, the adjustments always relieve the stress and move us back toward long-run equilibrium. We have to do a better job of allowing for the build up of stress within our models, and then using these models to guide the measurement and monitoring of "stress levels" in particular markets, particularly asset markets, so we can take action when the levels are too high.)

Whether the financial crisis should have been predicted or not, the fact that it wasn't predicted does not mean that macroeconomic models are useless any more than the failure to predict earthquakes implies that earthquake science is useless. As with earthquakes, even when prediction is not possible (or missed), the models can still help us to understand how these shocks occur. That understanding is useful for getting ready for the next shock, or even preventing it, and for minimizing the consequences of shocks that do occur. 

But we have done much better at dealing with the consequences of unexpected shocks ex-post than we have at getting ready for these a priori. Our equivalent of getting buildings ready for an earthquake before it happens is to use changes in institutions and regulations to insulate the financial sector and the larger economy from the negative consequences of financial and other shocks. Here I think economists made mistakes - our "buildings" were not strong enough to withstand the earthquake that hit. We could argue that the shock was so big that no amount of reasonable advance preparation would have stopped the "building" from collapsing, but I think it's more the case that enough time has passed since the last big financial earthquake that we forgot what we needed to do. We allowed new buildings to be constructed without the proper safeguards.

However, that doesn't mean the models themselves were useless. The models were there and could have provided guidance, but the implied "building codes" were ignored. Greenspan and others assumed no private builder would ever construct a building that couldn't withstand an earthquake, the market would force them to take this into consideration. But they were wrong about that, and even Greenspan now admits that government building codes are necessary. It wasn't the models, it was how they were used (or rather not used) that prevented us from putting safeguards into place.

We haven't failed at this entirely though. For example, we have had some success at putting safeguards into place before shocks occur, automatic stabilizers have done a lot to insulate against the negative consequences of the recession (though they could have been larger to stop the building from swaying as much as it has). So it's not proper to say that our models have not helped us to prepare in advance at all, the insulation social insurance programs provide is extremely important to recognize. But it is the case that we could have and should have done better at preparing before the shock hit.

I'd argue that our most successful use of models has been in cleaning up after shocks rather than predicting, preventing, or insulating against them through pre-crisis preparation. When despite our best effort to prevent it or to minimize its impact a priori, we get a recession anyway, we can use our models as a guide to monetary, fiscal, and other policies that help to reduce the consequences of the shock (this is the equivalent of, after a disaster hits, making sure that the water is safe to drink, people have food to eat, there is a plan for rebuilding quickly and efficiently, etc.). As noted above, we haven't done a very good job at predicting big crises, and we could have done a much better job at implementing regulatory and institutional changes that prevent or limit the impact of shocks. But we do a pretty good job of stepping in with policy actions that minimize the impact of shocks after they occur. This recession was bad, but it wasn't another Great Depression like it might have been without policy intervention.

Whether or not we will ever be able to predict recessions reliably, it's important to recognize that our models still provide considerable guidance for actions we can take before and after large shocks that minimize their impact and maybe even prevent them altogether (though we will have to do a better job of listening to what the models have to say). Prediction is important, but it's not the only use of models.

"Why This New Crisis Needs a New Paradigm of Economic Thought"

[More Side of the road blogging - stopped for a moment at the Great Salt Lake.] When I talked to the senate's COP panel, one of many things that I emphasized was the need to develop plans in advance to deal with various contingencies. Without such plans policy actions - even justifiable ones - appear ad hoc and also face resistance that delays their implementation or prevents them from being put into place altogether.

For example, we need a plan on the shelf and ready to go for dismantling large banks that have failed, something that has received a lot of attention. It has received much less attention, but I also think we need a plan for disposing troubled financial assets when the need arises. I still believe that the crisis would have been much less severe if very early, prior to Lehman for sure, the government had moved aggressively to buy bad assets from bank balance sheets. it took far too long, and when they finally decided to do this (i.e. the original Paulson plan), they had no idea how to value the assets, there was considerable political resistance because nobody knew how the program would work (allowing lots of false information to enter the debate), and so on, and this program never really got off the ground. The assets are still there waiting for the miracle of rising asset prices to restore their value.

Having a plan ready in advance that specifies how assets will be valued, how taxpayers will be protected if the government overpays (overpaying can help with recapitalization, but it shouldn't be a gift), and so on, a plan that has been approved in advance by legislators (at least implicitly) so as to reduce political resistance, will overcome many of the technical problems and objections that prevented the bad asset removal programs from being used effectively in this crisis.

Keiichiro Kobayashi believes these toxic assets, many of which are still hidden on bank balance sheets, are still a problem and could result in a Japan style lost decade if the government does not remove them, and he calls for a new macroeconomic paradigm that puts these issues front and center (On his main point about whether financial sector recovery is necessary before the real economy can recover, I think we will recover either way, but agree that recovery would be faster if these assets were removed once and for all - but I should get back on the road...):

Why this new crisis needs a new paradigm of economic thought, by Keiichiro Kobayashi, Commentary, Vox EU: The policies being debated in the US and Europe today are almost identical to those that played out in Japan a decade or so ago. Japan experienced the collapse of its colossal property bubble in 1990 and then a series of crises as major banks and securities companies were overwhelmed by rapidly rising non-performing debts. The conventional wisdom among economists and politicians throughout the 1990s was that massive public expenditure and extraordinary monetary easing would give the necessary boost to market sentiments and prompt an economic recovery. Public opinion in the US and Europe today seems to be the same.

And indeed, throughout the 1990s, Japan did introduce major public works projects and tax cuts, yet the economy failed to stabilise, asset prices continued to fall, and the volume of non-performing debts continued to climb. Far from being dispelled, the sense of insecurity that had permeated the markets actually increased throughout the 1990s, ultimately leading to the collapse of several major financial institutions in 1997 and sparking an outbreak of panic.

Even after this, recovery efforts continued to be channelled through large-scale public expenditure, while the disposal of non-performing debts became bogged down. Only around 2001 did Japanese public opinion finally turn away from the belief that reductions in bad debt and financial system stability would follow an economy recovery. The public came to understand that the financial system had to be stabilised and market insecurity dispelled before any recovery could occur. Special inspections were conducted repeatedly by financial regulators and Japanese megabanks were forced to accept massive capital increases and a new round of mergers. Meanwhile, the Resolution and Collection Corporation and the Industrial Revitalisation Corporation of Japan restructured companies that had collapsed under enormous debt burdens and finally broke the back of the non-performing debt problem. This sparked a recovery of market confidence, and Japan enjoyed a period of economic expansion from 2002 to 2007.

Can Econometricians Tell Us which Macroeconomic Model is Best?

This sounds familiar:

Ecology is one of the hardest branches of biology, possibly of all science. Real ecological communities are fantastically complex ... and hard to dissect and understand. Experiments in the wild are difficult to control, and important variables are often hard to measure. ... Experiments in the laboratory are problematic too. ...

Much of the uncertainty in economics derives from our inability to do laboratory experiments, and that includes uncertainty about which model best describes the macroeconmy. 

When the present crisis is finally over, those who advocated fiscal policy, those who advocated monetary policy, and those who advocated no policy at all will all say "I told you so" based upon their reading of the evidence.

Some New Keynesians will cite fiscal policy as the important policy response, and the timing of the policy relative to the recovery will likely support that argument. Other New Keynesians along with Monetarists (e.g. Lucas and others who believe monetary policy can help, but fiscal policy is ineffective) will insist it was monetary policy that saved us. The timing of the monetary policy response will support their position as well.

Still others, those such as Prescott who believe in Real Business Cycle models, will say the economy recovered despite policy, and would have recovered all that much faster if government hadn't gotten in the way. Without a baseline to refer to showing what would have happened without policy, it would be hard to refute this argument.

Once this is all over, there will be ways to tease this out of the data, e.g. the pattern of the response of key macroeconomic variables may be most consistent with one of the policies, but there will still be considerable uncertainty due to the high correlation in the timing of the monetary and fiscal policy responses (cross-country studies could help too since the policy response varied by country, but other differences across countries that are difficult to control for making these estimates uncertain as well).

Ideally, we would go to the lab and run the economy with the same initial conditions, say, 1,000 times with no policy intervention at all to establish the average non-intervention response (and its variance), i.e. the baseline, an important missing piece of information when all you have is non-experimental data. Then, we would run the economy again with a monetary policy response to the crisis 1,000 times (or do several experiments with different monetary policy responses to see which is best), and yet again 1,000 more times with fiscal policy (or, as with monetary policy, perhaps several fiscal polices involving different levels of spending and taxes), then compare the results to see how well each policy attenuates the cycle. (I would also want to run the economy with several combinations of the two polices in case there are important interaction effects the experiments with individual treatments might miss.)

That would probably give us a pretty good idea about which policy works best. However, without the ability to do experiments, the best we can do is to build a model of the economy based upon historical data, and then use the model to simulate the experiments above. That is, estimate the model based upon actual data, then run it with various combinations of monetary and fiscal policy and see how the outcome varies with differences in policy. Unfortunately, the answers you get are only as good as the model used to get them, and considerable uncertainty remains over which macroeconomic model is best (which is why we have Real Business Cycle, New Keynesian, and Monetarist type macroeconomic models along with all their various sub forms, though more recently questions have arisen over whether any of the existing theoretical structures are satisfactory).

Here's another way to think about it. Macroeconomists know all of the major historical episodes and correlations that a model must explain. We can't do experiments, so there is just one set of data, and of course any model that is built will be able to explain how these data evolve over time. And it's possible to build different models that explain the data equally well. If we could do experiments, we could test these models in ways that would potentially rule some of them out, but with just one set of data and models built specifically to explain the data such testing is not possible.

So we have to wait for time to bring us more data and then see if the model can explain them, test the models across countries, find things we didn't know about when we built the model and test the model against those -- and there are other ways to get at this -- but for the most part it's time that settles these issues. The models either do or do not continue to explain new data as they arrive.

But at any point in time, it will be difficult to distinguish between different models because those models are built to explain everything that is known about the historical macro data. Perhaps some time in the distant future when we have much more data than we have now, it will become more difficult to construct competing models and we will begin to converge on a common theoretical structure -- it seemed like we were headed in that direction prior to the recent crisis -- but for now we are stuck arguing about which model is best without the means to turn to the data and clearly distinguish one from the other.

Lucas Roundtable: Ask the Right Questions

In The Economist, Robert Lucas responds to recent criticism of macroeconomics ("In Defense of the Dismal Science"). Here's my entry at Free Exchange's Robert Lucas Roundtable in response to his essay:

Lucas roundtable: Ask the right questions, by Mark Thoma: In his essay, Robert Lucas defends macroeconomics against the charge that it is "valueless, even harmful", and that the tools economists use are "spectacularly useless".

I agree that the analytical tools economists use are not the problem. We cannot fully understand how the economy works without employing models of some sort, and we cannot build coherent models without using analytic tools such as mathematics. Some of these tools are very complex, but there is nothing wrong with sophistication so long as sophistication itself does not become the main goal, and sophistication is not used as a barrier to entry into the theorist's club rather than an analytical device to understand the world.

But all the tools in the world are useless if we lack the imagination needed to build the right models. Models are built to answer specific questions. When a theorist builds a model, it is an attempt to highlight the features of the world the theorist believes are the most important for the question at hand. For example, a map is a model of the real world, and sometimes I want a road map to help me find my way to my destination, but other times I might need a map showing crop production, or a map showing underground pipes and electrical lines. It all depends on the question I want to answer. If we try to make one map that answers every possible question we could ever ask of maps, it would be so cluttered with detail it would be useless, so we necessarily abstract from real world detail in order to highlight the essential elements needed to answer the question we have posed. The same is true for macroeconomic models.

But we have to ask the right questions before we can build the right models.

"Forecasts vs. Mechanisms in Economics"

Putting up another post all too quickly between conference sessions:

Forecasts vs mechanisms in economics, by Chris Dillow: This discussion between Edmund Conway and Andrew Lilico on the Today programme on the alleged crisis in economics seems to me to rest upon a misunderstanding of what economics is.

Conway says the crisis has been “an earthquake for economic thought” and Lilico says we need “new theories.“  This, though, seems to regard economics as a settled but inadequate body of knowledge and theory. It’s not. It is instead a vast number of diverse insights. What’s more, all of the insights that help explain the current economic crisis were, in truth, well known to economists before 2007, for example:

1. Risk cannot be simply described by a bell curve. But we learnt about tail risk on October 19 1987. And we learnt from the collapse of LTCM in 1998 that correlation risk, liquidity risk and counterparty risk are all significant.
2. Assets can be mispriced. But we’ve known about bubbles for centuries - since at least 1637. Their existence does not disprove the efficient market hypothesis; as I’ve said, the EMH is not the rational investor hypothesis. Nor, contrary to Conway’s implicit claim, is the EMH inconsistent with the possibility that behaviour can be swayed by emotions; the EMH allows for the possibility of time-varying risk premia*
3.  Long periods of economic stability can lead to greater risk-taking. We’ve known this since (at least) Hyman Minsky.
4.  Banks can suffer catastrophic losses - which are correlated across banks. We learnt this - not for the first time - in the Latin American debt crisis of the early 80s and in the crises in Japan and the Nordic countries in the early 90s. Banking crises are a regular feature of even developed economies.
5.  Institutions, such as banks, can be undermined by badly designed incentives.  But there’s a huge literature on the principal-agent problem.
6. The current crisis, then, has not thrown up much that economists didn’t know.
   
   Instead, our problem is a different one. It’s that what we have are lots of mechanisms, capable of explaining why things happen and the links between them. What we don’t have are laws which generate predictions. In his book, Nuts and Bolts for the Social Sciences, Jon Elster stressed this distinction. The social sciences, he said:

Can isolate tendencies, propensities and mechanisms and show that they have implications for behaviour that are often surprising and counter-intuitive. What they are more able to do is to state necessary and sufficient conditions under which the various mechanisms are switched on.

This is precisely the problem economists had in 2007. We knew that there were mechanisms capable of generating disaster. What we didn’t know is whether these were switched on. The upshot is that, although we didn’t predict the crisis, we can more or less explain it after the fact. As Elster wrote:

Sometimes we can explain without being able to predict, and sometimes predict without being able to explain. True, in many cases one and the same theory will enable us to do both, but I believe that in the social sciences this is the exception rather than rule.

The interesting question is: will it remain the exception? My hunch is that it will; economists will never be able to produce laws which yield systemically successful forecasts.  

What’s more, I am utterly untroubled by this. The desire for such laws is as barmy as the medieval search for the philosopher’s stone. If you need to foresee the future, you are doing something badly wrong.

* The basic insight of efficient market theory is that you cannot out-perform the market except by taking extra risk. I am sick and tired of hearing people who still have to work for a living trying to deny this.

I think the statements on prediction are overly broad. If you raise the price of a good, in all but a few cases such as when price is interpreted as a signal of quality, we can predict what will happen, quantity demanded will fall. By exactly how much will quantity demanded fall? In some microeconomic applications, the bounds can be fairly tight. For example, I suspect Hal Varian at Google - who has access to vast amounts of data and the ability to conduct all else equal type experiments - has a fairly tight estimate of important parameters that indicate how, say, changing the price of an ad will impact Google's revenue stream. He has also been doing some interesting work on prediction, e.g. see Predicting Initial Claims for Unemployment Benefits. But in other cases, particularly in macroeconomics and the prediction of turning points, success has been much more modest (or absent altogether). However, I am not as pessimistic as Chris that we will never be able to do predict the course of the economy, but it will require that we begin to better understand how pressures build within the macroeconomic system, how to measure and monitor these pressures (e.g. measures such global and sectoral imbalances or price rent ratios, but those are hardly sufficient in and of themselves), and ultimately how to relieve the pressures when they begin to build to threatening levels.

Macroeconomic Models

Robert Skidelsky doesn't always get things completely right. For example, he often talks about "New Classical economics" as if that is the dominant paradigm today, but that term has a very specific meaning and refers to a class of models that is no longer popular in macroeconomics.

Let's back up. The New Classical model had four important elements, the assumption of rational expectations, the assumption of the natural rate hypothesis, the assumption of continuous market clearing that Skidelsky refers to below, and an assumption that agents have imperfect information. The imperfect information assumption was quite clever in that it allowed proponents of this model to explain correlations between money and income without acknowledging that systematic, predictable policy based upon something like a Taylor rule would have any effect at all (put another way, only unexpected changes in monetary policy matter, expected changes are fully neutralized by private sector responses to the policy).

The New Classical model did contribute to the movement in macroeconomics toward microeconomic foundations and to the use of rational agents within macro models, but the model itself could not simultaneously explain both the duration and magnitude of actual cycles, it had difficulty explaining some key correlations among macroeconomic variables, and it was difficult to understand why a market for the absent information did not develop if the consequences of imperfect information were as large as the New Classical model implied. In addition, one of the model's key results that only unexpected changes in money can affect real variables did not hold up when taken to the data (though there are still a few die-hards on this). So the profession moved on.

The New Classical model had replaced the old Keynesian model after it became widely believed that the models' shortcomings were partly responsible for the problems we had in the 1970s, and for the theoretical reasons that will be described in a moment. But while the New Classical economists were having their day in the sun, the Keynesians were quietly working behind the scenes to fix the problems that caused the old Keynesian model to go out of favor (or not so quietly in a few cases). The old Keynesian model had a poor model of expectations - if expectations were considered at all they were usually modeled as a naive adaptive process - and in addition, it was not clear that the relationships embedded within the old Keynesian model were consistent with optimizing behavior on behalf of households and firms. The New Keynesian model solved this by deriving macroeconomic relationships from microeconomic optimizing behavior, and by adopting the rational expectations framework. And they made one other change important change. In order for systematic monetary policy such as following a Taylor rule to affect real variables such as output and employment, there must be some type of friction that prevents the economy from immediately moving to it long run equilibrium value. The friction in the New Classical model is informational, agents optimize given the information that they have, but because the information is imperfect the decisions they make take the economy away from its optimal long-run path.

In the New Keynesian model the friction that gives monetary policy its power to affect output and employment is sluggish movement of prices and wages (generally modeled through something called the Calvo pricing rule, a source of controversy because there are questions about the extent to which this rule is consistent with micro-founded optimizing behavior, though others assert there are rationales for the Calvo structure that are sufficient - to some - to overcome these concerns).

To me, the New Keynesian model is about as mainstream as they get, so I'm puzzled by the opening to this column that claims modern macro completely embraces fully flexible prices. I think what he has in mind is some version of a Real Business Cycle model where prices are, in fact, assumed to be fully flexible, agents are rational etc. so that actual output is always equal to potential (so there's no need for policy to do anything but maximize the growth of potential output, hence the supply-side orientation of advocates of this approach). And I'm sure we could have a lively debate about which model has more proponents, but to say that mainstream economics subscribes fully to the notion of continuous market clearing when price rigidities are at the heart of a major class of modern models seems to miss the mark (and the assertion that agents are assumed to have perfect information is equally puzzling, they optimize given what they know, but they are not assumed to know everything and the efficient market hypothesis he discusses does not require this).

I don't disagree with the main message of the column that prevention of financial crashes through regulation is better than trying to cure them with policy, though I might quibble with particulars, but as someone who has been an advocate of the New Keynesian model, and quite resistant to pure Real Business Cycle approaches, I wanted to make clear that not all of us believe that assuming fully flexible prices and continuous market clearing is the proper way to model the economy. (A synthesis of the New Keynesian and Real Business Cycle models is what I have pushed in the past, though I'm now reconsidering the types of frictions that ought to be embedded in these models given recent events, and whether the mechanisms for generating bubbles in these structures are sufficient. I am also quite sympathetic to learning models as a replacement for the assumption of strict rationality):

Risky Risk Management, by Robert Skidelsky, Project Syndicate: Mainstream economics subscribes to the theory that markets "clear" continuously. The theory's big idea is that if wages and prices are completely flexible, resources will be fully employed, so that any shock to the system will result in instantaneous adjustment of wages and prices to the new situation.

This system-wide responsiveness depends on economic agents having perfect information about the future, which is manifestly absurd. Nevertheless, mainstream economists believe that economic actors possess enough information to lend their theorizing a sufficient dose of reality.

The aspect of the theory that applies particularly to financial markets is called the "efficient market theory," which should have blown sky-high by last autumn's financial breakdown. But I doubt that it has. Seventy years ago, John Maynard Keynes pointed out its fallacy. When shocks to the system occur, agents do not know what will happen next.

In the face of this uncertainty, they do not readjust their spending; instead, they refrain from spending until the mists clear, sending the economy into a tailspin. It is the shock, not the adjustments to it, that spreads throughout the system. The inescapable information deficit obstructs all those smoothly working adjustment mechanisms ― i.e., flexible wages and flexible interest rates ― posited by mainstream economic theory.

An economy hit by a shock does not maintain its buoyancy; rather, it becomes a leaky balloon. Hence Keynes gave governments two tasks: to pump up the economy with air when it starts to deflate, and to minimize the chances of serious shocks happening in the first place.

Today, that first lesson appears to have been learned... But, judging from recent proposals in the United States, the United Kingdom, and the European Union to reform the financial system, it is far from clear that the second lesson has been learned.

Admittedly, there are some good things in these proposals. For example, the U.S. Treasury suggests that originators of mortgages should retain a "material" financial interest in the loans they make, in contrast to the recent practice of securitizing them. This would, among other things, reduce the role of credit rating agencies. ... The underlying problem, though, is that both regulators and bankers continue to rely on mathematical models that promise more than they can deliver for managing financial risks.

Although regulators now place their faith in "macro-prudential" models to manage "systemic" risk, rather than leaving financial institutions to manage their own risks, both sides lumber on in the untenable belief that all risk is measurable (and therefore controllable), ignoring Keynes's crucial distinction between "risk" and "uncertainty."

Salvation does not lie in better "risk management" by either regulators or banks, but, as Keynes believed, in taking adequate precautions against uncertainty.

As long as policies and institutions to do this were in place, Keynes argued, risk could be let to look after itself. Treasury reformers have shirked the challenge of working out the implications of this crucial insight.

The State of Macroeconomics

Because of these three articles in The Economist, everyone seems to be weighing in on the state of macroeconomics. I haven't because I've already said everything I want to say about this, and I didn't want to be repetitive. The links are:

• Macroeconomic Meltdown?,
• "The Great Multiplier Debate"
• "The Unfortunate Uselessness of Most 'State of the Art' Academic Monetary Economics"

Here's one bit:

Let me a bit more specific, and add something more to problems with macroeconomics I discussed in The Great Multiplier Debate and "The Unfortunate Uselessness of Most 'State of the Art' Academic Monetary Economics". The main mechanism generating fluctuations and policy effects in modern New Keynesian models is Calvo type sluggish price adjustment. I think this model is useful for “normal” times as a way of understanding economic fluctuations, and for learning about optimal policy, and it represents a step forward in understanding monetary policy in particular. But do people really think that all would be fine right now if prices – and they must have housing prices in mind when they think about sticky prices as an explanation for the current episode – had only adjusted faster? If housing prices had dropped even faster than they have already, all would be well in the world?

Okay, so maybe they don’t have housing prices in mind. Still, do we really think that sluggish price adjustment is the main mechanism at work in the present crisis? If not, then what use is the evidence from those models? Why do we keep hearing about theoretical simulations that give values for the multiplier that are small, large, zero, less than one, whatever? Do we really think that sluggish price adjustment captures the essence of the factors driving the present crisis? I don't.

That is, the mechanism driving real effects in the standard versions of these models is sluggish price adjustment, but do we really believe this is the main mechanism through which real effects are being generated in the current crisis? if not, how much faith should we put in estimates derived from these models?

Update: Mark Gertler emails a response to this post (this also appeared at Free exchange earlier today):

The current crisis has naturally led to scrutiny of the economics profession. The intensity of this scrutiny ratcheted up a notch with the Economist’s interesting cover story this week on the state of academic economics.

I think some of the criticism has been fair. The Great Moderation gave many in the profession the false sense that we had handled the problem of the business cycle as well as we could. Traditional applied macroeconomic research on booms and busts and macroeconomic policy fell into something of a second class status within the field in favor of more exotic topics.

At the same time, from the discussion thus far, I don’t think the public is getting the full picture of what has been going on in the profession. From my vantage, there has been lots of high quality “middle ground” modern macroeconomic research that has been relevant to understanding and addressing the current crisis.

Here I think, though, that both the mainstream media and the blogosphere have been confusing a failure to anticipate the crisis with a failure to have the research available to comprehend it. Predicting the crisis would have required foreseeing the risks posed by the shadow banking system, which were missed not only by academic economists, but by just about everyone else on the planet (including the ratings agencies!).

But once the crisis hit, broadly speaking, policy-makers at the Federal Reserve made use of academic research on financial crises to help diagnose the situation and design the policy response. Research on monetary and fiscal policy when the nominal interest is at the zero lower bound has also been relevant. Quantitative macro models that incorporate financial factors, which existed well before the crisis, are rapidly being updated in light of new insights from the unfolding of recent events. Work on fiscal policy, which admittedly had been somewhat dormant, is now proceeding at a rapid pace.

Bottom line: As happened in both the wake of the Great Depression and the Great Stagflation, economic research is responding. In this case, the time lag will be much shorter given the existing base of work to build on. Revealed preference confirms that we still have something useful to offer: Demand for our services by the ultimate consumers of modern applied macro research – policy makers and staff at central banks – seems to be higher than ever.

Mark Gertler,
Henry and Lucy Moses Professor of Economics
New York University

I have also posted a link to his Mini-Course, "Incorporating Financial Factors Within Macroeconomic Modelling and Policy Analysis" in the daily links for tomorrow. This course looks at recent work on integrating financial factors into macro modeling, and is a partial rebuttal to the assertion above that New Keynesian models do not have mechanisms built into them that can explain the financial crisis. We still have work to do, but as Mark Gertler notes, "economic research is responding," and as I noted at the end of one of the posts linked above, "The models will be built - I guarantee you they are being built presently."

"Trumped by Darwin?"

Robert Frank returns to the point he made in Alpha Markets, i.e. that Charles Darwin provides the "true intellectual foundation" for economics. Though the example this time is male elk rather than bull elephant seals, the central point - and it's one worth giving more thought to - is that "Individual and group interests are almost always in conflict when rewards to individuals depend on relative performance." In these situations, which occur frequently in economic and social relationships, the assumption in neoclassical economic models that the maximization of self-interest is consistent with the maximization of social interest does not hold, and failure to recognize this has " undermined regulatory efforts ... causing considerable harm to us all":

The Invisible Hand, Trumped by Darwin?, by Robert Frank, Commentary, NY Times: If asked to identify the intellectual founder of their discipline, most economists today would probably cite Adam Smith. But that will change. ... Charles Darwin ... tracks economic reality much more closely. ...

Smith’s basic idea was that business owners ... have powerful incentives to introduce improved product designs and cost-saving innovations. These moves bolster innovators’ profits in the short term. But rivals respond by adopting the same innovations, and the resulting competition gradually drives down prices and profits. In the end, Smith argued, consumers reap all the gains.

The central theme of Darwin’s narrative was that competition favors traits and behavior according to how they affect the success of individuals, not species or other groups. As in Smith’s account, traits that enhance individual fitness sometimes promote group interests. For example, a mutation for keener eyesight in hawks benefits not only any individual hawk that bears it, but also makes hawks more likely to prosper as a species.

In other cases, however, traits that help individuals are harmful to larger groups. For instance, a mutation for larger antlers served the reproductive interests of an individual male elk, because it helped him prevail in battles ... for access to mates. But as this mutation spread, it started an arms race that made life more hazardous for male elk over all. The antlers of male elk can now span five feet or more. And despite their utility in battle, they often become a fatal handicap when predators pursue males into dense woods.

In Darwin’s framework, then,... [c]ompetition, to be sure, sometimes guides individual behavior in ways that benefit society as a whole. But not always.

Individual and group interests are almost always in conflict when rewards to individuals depend on relative performance, as in the antlers arms race. In the marketplace, such reward structures are the rule, not the exception. The income of investment managers, for example, depends mainly on the amount of money they manage, which in turn depends largely on their funds’ relative performance. Relative performance affects many other rewards in contemporary life. ...

In cases like these, relative incentive structures undermine the invisible hand. To make their funds more attractive to investors, money managers create complex securities that impose serious, if often well-camouflaged, risks on society. But when all managers take such steps, they are mutually offsetting. No one benefits, yet the risk of financial crises rises sharply. ...

It’s the same with athletes who take anabolic steroids. ...

If male elk could vote to scale back their antlers by half, they would have compelling reasons for doing so, because only relative antler size matters. Of course, they have no means to enact such regulations.

But humans can and do. ... Darwin has identified the rationale for much of the regulation we observe in modern societies — including steroid bans in sports, safety and hours regulation in the workplace, product safety standards and the myriad restrictions typically imposed on the financial sector.

Ideas have consequences. The uncritical celebration of the invisible hand by Smith’s disciples has undermined regulatory efforts to reconcile conflicts between individual and collective interests in recent decades, causing considerable harm to us all. ...

[And, again, for those who might be interested, see also Paul Krugman's: What Economists Can Learn from Evolutionary Theorists Synopsis.]

Economists are Seeking Remedies to the Crisis

Francesco Caselli of the London School of Economics rebuts a recent attack on the economics profession:

Economists are actively engaged in seeking remedies to the crisis, by Francesco Caselli, CIF: Larry Elliott's claim that "as a profession, economics not only has nothing to say about what caused the world to come to the brink of financial collapse last autumn, but also a supreme lack of interest", deserves a rebuttal.

The alleged lack of interest is belied by the outpouring of commentary and discussion that has swept the profession over the last couple of years and shows no sign of abating. I can think of few of the top academic stars in macroeconomics who have not been busy editorialising, blogging, and participating in discussions and policy events.

The evidence for the lack-of-interest charge is that "if, for example, you scroll down the list of papers scheduled for publication by the Review of Economic Studies, one of the prestigious UK journals, there is not the slightest sense that the world of general equilibrium and real business cycle models has been turned upside down in the past two years".

Whats New in Econometrics: Time Series

This won't interest many of you I don't think, but I didn't realize these lectures were available on Google Video so I thought I'd pass them along to anyone - like me - who is interested (if you click through to the Google Video page and select "original size" for viewing, the picture is a bit better):

"The Economic Crisis and Its Implications for The Science of Economics"

Sound/video from a recent conference:

The Perimeter Institute conference on economics is being organized in an effort to better evaluate the state of economics as a predictive and descriptive science in light of the current market crisis. We believe that this requires careful, dispassionate discussion, in an atmosphere governed by the modesty and open mindedness that characterizes the scientific community. To do this we aim to bring leading economists and theorists of finance together with physicists, mathematicians, biologists and computer scientists to evaluate current theories of markets, and identify key issues that can motivate new directions for research. ...

Collection URL:http://pirsa.org/C09006

"Rats Outperform Humans in Interpreting Data"

Bill Easterly sent me a link to the post The Vortex of Vacuousness that I posted the other day, but I like this one better:

Maybe we should put rats in charge of foreign aid research, by William Easterly: Laboratory experiments show that rats outperform humans in interpreting data... The amazing finding on rats is described in an equally amazing book by Leonard Mlodinow. The experiment consists of drawing green and red balls at random, with the probabilities rigged so that greens occur 75 percent of the time. The subject is asked to watch for a while and then predict whether the next ball will be green or red. The rats followed the optimal strategy of always predicting green (I am a little unclear how the rats communicated, but never mind). But the human subjects did not always predict green, they usually want to do better and predict when red will come up too, engaging in reasoning like “after three straight greens, we are due for a red.” As Mlodinow says, “humans usually try to guess the pattern, and in the process we allow ourselves to be outperformed by a rat.”

Unfortunately, spurious patterns show up in some important real world settings, like research on the effect of foreign aid on growth. Without going into any unnecessary technical detail, research looks for an association between economic growth and some measure of foreign aid, controlling for other likely determinants of economic growth. Of course, since there is some random variation in both growth and aid, there is always the possibility that an association appears by pure chance. The usual statistical procedures are designed to keep this possibility small. The convention is that we believe a result if there is only a 1 in 20 chance that the result arose at random. So if a researcher does a study that finds a positive effect of aid on growth and it passes this “1 in 20” test (referred to as a “statistically significant” result), we are fine, right?

Alas, not so fast. A researcher is very eager to find a result, and such eagerness usually involves running many statistical exercises (known as “regressions”). But the 1 in 20 safeguard only applies if you only did ONE regression. What if you did 20 regressions? Even if there is no relationship between growth and aid whatsoever, on average you will get one “significant result” out of 20 by design. Suppose you only report the one significant result and don’t mention the other 19 unsuccessful attempts. You can do twenty different regressions by varying the definition of aid, the time periods, and the control variables. In aid research, the aid variable has been tried, among other ways, as aid per capita, logarithm of aid per capita, aid/GDP, logarithm of aid/GDP, aid/GDP squared, [log(aid/GDP) - aid loan repayments], aid/GDP*[average of indexes of budget deficit/GDP, inflation, and free trade], aid/GDP squared *[average of indexes of budget deficit/GDP, inflation, and free trade], aid/GDP*[ quality of institutions], etc. Time periods have varied from averages over 24 years to 12 years to to 8 years to 4 years. The list of possible control variables is endless. One of the most exotic I ever saw was: the probability that two individuals in a country belonged to different ethnic groups TIMES the number of political assassinations in that country. So it’s not so hard to run many different aid and growth regressions and report only the one that is “significant.”

This practice is known as “data mining.” It is NOT acceptable practice, but this is very hard to enforce since nobody is watching when a researcher runs multiple regressions. It is seldom intentional dishonesty by the researcher. Because of our non-rat-like propensity to see patterns everywhere, it is easy for researchers to convince themselves that the failed exercises were just done incorrectly, and that they finally found the “real result” when they get the “significant” one. Even more insidious, the 20 regressions could be spread across 20 different researchers. Each of these obediently does only one pre-specified regression, 19 of whom do not publish a paper since they had no significant results, but the 20th one does publish their spuriously “significant” finding (this is known as “publication bias.”)

But don’t give up on all damned lies and statistics, there ARE ways to catch data mining. A “significant result” that is really spurious will only hold in the original data sample, with the original time periods, with the original specification. If new data becomes available as time passes you can test the result with the new data, where it will vanish if it was spurious “data mining”. You can also try different time periods, or slightly different but equally plausible definitions of aid and the control variables.

So a few years ago, some World Bank research found that “aid works {raises economic growth} in a good policy environment.” This study got published in a premier journal, got huge publicity, and eventually led President George W. Bush (in his only known use of econometric research) to create the Millennium Challenge Corporation, which he set up precisely to direct aid to countries with “good policy environments.”

Unfortunately, this result later turned out to fail the data mining tests. Subsequent published studies found that it failed the “new data” test, the different time periods test, and the slightly different specifications test.

The original result that “aid works in a good policy environment” was a spurious association. Of course, the MCC is still operating, it may be good or bad for other reasons.

Moral of the story: beware of these kinds of statistical “results” that are used to determine aid policy! Unfortunately, the media and policy community don’t really get this, and they take the original studies at face value (not only on aid and growth, but also in stuff on determinants of civil war, fixing failed states, peacekeeping, democracy, etc., etc.) At the very least, make sure the finding is replicated by other researchers and passes the “data mining” tests. ...

I saw Milton Friedman provide an interesting example of avoiding data mining. I was at a SF Fed conference where he was a speaker, and his talk was about a paper he had written 20 years earlier on "The Plucking Model." From a post in January 2006, New Support for Friedman's Plucking Model:

Friedman found evidence for the Plucking Model of aggregate fluctuations in a 1993 paper in Economic Inquiry. One reason I've always liked this paper is that Friedman first wrote it in 1964. He then waited for more than twenty years for new data to arrive and retested his model using only the new data. In macroeconomics, we often encounter a problem in testing theoretical models. We know what the data look like and what facts need to be explained by our models. Is it sensible to build a model to fit the data and then use that data to test it to see if it fits? Of course the model will fit the data, it was built to do so. Friedman avoided this problem since he had no way of knowing if the next twenty years of data would fit the model or not. It did.

The other thing I'll note is that there is a literature on how test statistics are affected by pretesting, but it is ignored for the most part (e.g. if you run a regression, then throw out an insignificant variable, anything you do later must take account of the fact that you could have made a type I or type II error during the pretesting phase). The bottom line is that the test statistics from the final version of the model are almost always non-normal, and the distribution of the test statistics is not generally known.

[One more note. I wrote a paper on Friedman's Plucking Model, and had a revise and resubmit at a pretty good journal. I satisfied all the referee's objections, at least I thought I had, and it was all set to go. I had sent the first version of the paper to Friedman, and he wrote back with a long, multi-page letter that was very encouraging, and I incorporated his suggestions into the revision (a reason I'll always have a soft spot for him, his time was valuable, yet he took the time to do this). But the final results weren't robust, and had come about through trying different specifications until one worked. The final specification worked well, very well in fact, but the results were pretty fragile. As a result, I pulled the paper and did not resubmit it. The paper was completely redone and rewritten, but after thinking it over I decided it wasn't robust enough to publish. I find myself regretting that sometimes, the referees would have probably taken the paper since the final version satisfied all their objections, and it was a good journal - I told myself I had simply done what everyone else does, etc. But, hard as it was for an assistant professor in need of publications to pull a paper, especially one Friedman himself had endorsed - this was just before going up for tenure so it could have mattered a lot - pulling the paper was the right thing to do. The only way to solve this problem - and data mining in economics is a problem - is for the people involved in the research to self-police the integrity of the process.]

Update: Seems like a good time to rerun this graph on publications in political science journals:

Lies, Damn Lies, and....: Via Kieran Healy, ...It is, at first glance, just what it says it is: a study of publication bias, the tendency of academic journals to publish studies that find positive results but not to publish studies that fail to find results. ...

The chart on the right shows G&M's basic result. In statistics jargon, a significant result is anything with a "z-score" higher than 1.96, and if journals accepted articles based solely on the quality of the work, with no regard to z-scores, you'd expect the z-score of studies to resemble a bell curve. But that's not what Gerber and Malhotra found. Above a z-score of 1.96, the results fit the bell curve pretty well, but below a z-score of 1.96 there are far fewer studies than you'd expect. Apparently, studies that fail to show significant results have a hard time getting published.

So far, this is unsurprising. Publication bias is a well-known and widely studied effect, and it would be surprising if G&M hadn't found evidence of it. But take a closer look at the graph. In particular, take a look at the two bars directly adjacent to the magic number of 1.96. That's kind of funny, isn't it? They should be roughly the same height, but they aren't even close. There are a lot of studies that just barely show significant results, and there are hardly any that fall just barely short of significance. There's a pretty obvious conclusion here, and it has nothing to do with publication bias: data is being massaged on wide scale. A lot of researchers who almost find significant results are fiddling with the data to get themselves just over the line into significance. ... Message to political science professors: you are being watched. And if you report results just barely above the significance level, we want to see your work....

"The Failure of the Economy & the Economists"

A much shortened version of Benjamin Friedman review of Akerlof and Shiller's Animal Spirits: and Shiller's The Subprime Solution:

The Failure of the Economy & the Economists, by Benjamin M. Friedman, NYRB, Review of Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism by George A. Akerlof and Robert J. Shiller; and The Subprime Solution: How Today's Global Financial Crisis Happened, and What to Do About It by Robert J. Shiller: By now there are few people who do not acknowledge that the major American financial institutions and the markets they dominate turn out to have served the country badly in recent years. ...

But despite the universal agreement that no one wants any more such failures once this one has passed, there is a troubling lack of attention to reforms that might prevent such a crisis from recurring. ... As in past financial declines, what is sorely missing in this discussion is attention to what function the financial system is supposed to perform in the economy and how well it has been doing it. ... Another fundamental issue that the current discussion has overlooked almost entirely is the distinction between the losses to banks and other lenders that reflect genuine losses of wealth to the economy, and other losses that don't. ...

Why has there been so little discussion of fundamental issues like this distinction among losses? Why is so little said about the trade-off between the goal of allocating the economy's capital efficiently and the need to shrink the enormous costs of the financial industry in doing so? One obvious reason is political. There is a long arc from Roosevelt's acceptance of a useful role for government institutions and government regulation to the conviction of Reagan and Thatcher that the government is never the solution but actually the problem. A second, closely related reason is ideological: the faith, personified by Alan Greenspan with his early dedication to the writings of Ayn Rand and his staunch opposition to regulations while chairman of the Federal Reserve, that private, profit-driven economic activity is self-regulating and, when necessary, self-correcting.

"Austrian Business Cycle Theory"

John Quiggin explains why Austrian business cycle theory "hasn’t developed in any positive way" since the 1920's, and has since become "ossified dogma":

Austrian Business Cycle Theory, by John Quiggin: I’ve long promised a post on Austrian Business Cycle Theory, and here it is. For those who would rather get straight to the conclusion, it’s one I share in broad terms with most of the mainstream economists who’ve looked at the theory, from Tyler Cowen, Bryan Caplan and Gordon Tullock at the libertarian/Chicago end of the spectrum to Keynesians like Paul Krugman and Brad DeLong.

To sum up, although the Austrian School was at the forefront of business cycle theory in the 1920s, it hasn’t developed in any positive way since then. The central idea of the credit cycle is an important one, particularly as it applies to the business cycle in the presence of a largely unregulated financial system. But the Austrians balked at the interventionist implications of their own position, and failed to engage seriously with Keynesian ideas.

The result (like orthodox Marxism) is a research program that was active and progressive a century or so ago but has now become an ossified dogma. Like all such dogmatic orthodoxies, it provides believers with the illusion of a complete explanation but ceases to respond in a progressive way to empirical violations of its predictions or to theoretical objections. To the extent that anything positive remains, it is likely to be developed by non-Austrians such as the post-Keynesian followers of Hyman Minsky. ... [...continue reading...]