(Scum rises to the top, that's it, nothing wrong with neo-classical
theory, says Barry Eichengreen: economics profs "do not object to the
occasional high-paying consulting gig. They don’t mind serving as the
entertainment at beachside and ski-slope retreats hosted by investment
banks for their important clients. Generous speaker’s fees were thus
available to those prepared to drink the Kool-Aid. Not everyone
indulged. But there was nonetheless a subconscious tendency to embrace
the arguments of one’s more “successful” colleagues in a discipline
where money, in this case earned through speaking engagements and
consultancies, is the common denominator of success.")
The Last Temptation of Risk
by Barry Eichengreen
04.30.2009
From the May/June 2009 issue of The National Interest.
THE GREAT Credit Crisis has cast into doubt much of what we thought we
knew about economics. We thought that monetary policy had tamed the
business cycle. We thought that because changes in central-bank policies
had delivered low and stable inflation, the volatility of the pre-1985
years had been consigned to the dustbin of history; they had given way
to the quaintly dubbed “Great Moderation.” We thought that financial
institutions and markets had come to be self-regulating—that investors
could be left largely if not wholly to their own devices. Above all we
thought that we had learned how to prevent the kind of financial
calamity that struck the world in 1929.
We now know that much of what we thought was true was not. The Great
Moderation was an illusion. Monetary policies focusing on low inflation
to the exclusion of other considerations (not least excesses in
financial markets) can allow dangerous vulnerabilities to build up.
Relying on institutional investors to self-regulate is the economic
equivalent of letting children decide their own diets. As a result we
are now in for an economic and financial downturn that will rival the
Great Depression before it is over.
The question is how we could have been so misguided. One interpretation,
understandably popular given our current plight, is that the basic
economic theory informing the actions of central bankers and regulators
was fatally flawed. The only course left is to throw it out and start
over. But another view, considerably closer to the truth, is that the
problem lay not so much with the poverty of the underlying theory as
with selective reading of it—a selective reading shaped by the social
milieu. That social milieu encouraged financial decision makers to
cherry-pick the theories that supported excessive risk taking. It
discouraged whistle-blowing, not just by risk-management officers in
large financial institutions, but also by the economists whose
scholarship provided intellectual justification for the financial
institutions’ decisions. The consequence was that scholarship that
warned of potential disaster was ignored. And the result was global
economic calamity on a scale not seen for four generations.
SO WHERE were the intellectual agenda setters when the crisis was
building? Why did they fail to see this train wreck coming? More than
that, why did they consort actively with the financial sector in setting
the stage for the collapse?
For economists in business schools the answer is straightforward.
Business schools see themselves as suppliers of inputs to business. Just
as General Motors provides its suppliers with specifications for the
cold-rolled sheet it needs for fabricating auto bodies, J. P. Morgan
makes clear the kind of financial engineers it requires, and business
schools deem to provide. In the wake of the 1987 stock-market crash,
Morgan’s chairman, Dennis Weatherstone, started calling for a daily
“4:15 Report” summarizing how much his firm would lose if tomorrow
turned out to be a bad day. His counterparts at other firms then adopted
the practice. Soon after, business schools jumped to supply graduates to
write those reports. Value at Risk, as that number and the process for
calculating it came to be known, quickly gained a place in the
business-school curriculum.
The desire for up-to-date information on the risks of doing business was
admirable. Less admirable was the belief that those risks could be
reduced to a single number which could then be estimated on the basis of
a set of mathematical equations fitted to a few data points. Much as
former–GM CEO Alfred Sloan once sought to transform automobile
production from a craft to an engineering problem, Weatherstone and his
colleagues encouraged the belief that risk and return could be reduced
to a set of equations specified by an MBA and solved by a machine.
Getting the machine to spit out a headline number for Value at Risk was
straightforward. But deciding what to put into the model was another
matter. The art of gauging Value at Risk required imagining the severity
of the shocks to which the portfolio might be subjected. It required
knowing what new variables to add in response to financial innovation
and unfolding events. Doing this right required a thoughtful and
creative practitioner. Value at Risk, like dynamite, can be a powerful
tool when in the right hands. Placed in the wrong hands—well, you know.
These simple models should have been regarded as no more than starting
points for serious thinking. Instead, those responsible for making key
decisions, institutional investors and their regulators alike, took them
literally. This reflected the seductive appeal of elegant theory.
Reducing risk to a single number encouraged the belief that it could be
mastered. It also made it easier to leave early for that weekend in the
Hamptons.
Now, of course, we know that the gulf between assumption and reality was
too wide to be bridged. These models were worse than unrealistic. They
were weapons of economic mass destruction.
For some years those who relied on these artificial constructs were not
caught out. Episodes of high volatility, like the 1987 stock-market
crash, still loomed large in the data set to which the model was fit.
They served to highlight the potential for big shocks and cautioned
against aggressive investment strategies. Since financial innovation was
gradual, models estimated on historical data remained reasonable
representations of the balance of risks.
WITH TIME, however, memories of the 1987 crash faded. In the data used
by the financial engineers, the crash became only one observation among
many generated in the course of the Great Moderation. There were echoes,
like the all-but-failure of the hedge fund Long-Term Capital Management
in 1998. (Over four months the company lost $4.6 billion and had to be
saved through a bailout orchestrated by the Federal Reserve Bank of New
York.) But these warning signs were muffled by comparison. This
encouraged the misplaced belief that the same central-bank policies that
had reduced the volatility of inflation had magically, perhaps through
transference, also reduced the volatility of financial markets. It
encouraged the belief that mastery of the remaining risk made more
aggressive investment strategies permissible. It made it possible, for
example, to employ more leverage—to make use of more borrowed
money—without putting more value at risk.
Meanwhile, deregulation was on the march. Memories of the 1930s disaster
that had prompted the adoption of restrictions like the Glass-Steagall
Act, which separated commercial and investment banking, faded with the
passage of time. This tilted the political balance toward those who, for
ideological reasons, favored permissive regulation. Meanwhile, financial
institutions, in principle prohibited from pursuing certain lines of
business, found ways around those restrictions, encouraging the view
that strict regulation was futile. With the elimination of regulatory
ceilings on the interest rates that could be paid to depositors,
commercial banks had to compete for funding by offering higher rates,
which in turn pressured them to adopt riskier lending and investment
policies in order to pay the bill. With the entry of low-cost brokerages
and the elimination of fixed commissions on stock trades, broker-dealers
like Bear Stearns, which had previously earned a comfy living off of
such commissions, now felt compelled to enter riskier lines of business.
But where the accelerating pace of change should have prompted more
caution, the routinization of risk management encouraged precisely the
opposite. The idea that risk management had been reduced to a mere
engineering problem seduced business in general, and financial
businesses in particular, into believing that it was safe to use more
leverage and to invest in more volatile assets.
Of course, risk officers could have pointed out that the models had been
fit to data for a period of unprecedented low volatility. They could
have pointed out that models designed to predict losses on securities
backed by residential mortgages were estimated on data only for years
when housing prices were rising and foreclosures were essentially
unknown. They could have emphasized the high degree of uncertainty
surrounding their estimates. But they knew on which side their bread was
buttered. Senior management strongly preferred to take on additional
risk, since if the dice came up seven they stood to receive megabonuses,
whereas if they rolled snake eyes the worst they could expect was a
golden parachute. If an investment strategy that promised high returns
today threatened to jeopardize the viability of the enterprise tomorrow,
then this was someone else’s problem. For a junior risk officer to warn
the members of the investment committee that they were taking undue risk
would have dimmed his chances of promotion. And so on up the ladder.
WHY CORPORATE risk officers did not sound the alarm bells is thus clear
enough. But where were the business-school professors while these events
were unfolding? Answer: they were writing textbooks about Value at Risk.
(Truth in advertising requires me to acknowledge that the leading such
book is by a professor at the University of California.) Business
schools are rated by business publications and compete for students on
the basis of their record of placing graduates. With banks hiring
graduates educated in Value at Risk, business schools had an obvious
incentive to supply the same.
But what of doctoral programs in economics (like the one in which I
teach)? The top PhD-granting departments only rarely send their
graduates to positions in banking or business—most go on to other
universities. But their faculties do not object to the occasional
high-paying consulting gig. They don’t mind serving as the entertainment
at beachside and ski-slope retreats hosted by investment banks for their
important clients.
Generous speaker’s fees were thus available to those prepared to drink
the Kool-Aid. Not everyone indulged. But there was nonetheless a
subconscious tendency to embrace the arguments of one’s more
“successful” colleagues in a discipline where money, in this case earned
through speaking engagements and consultancies, is the common
denominator of success.
Those who predicted the housing slump eventually became famous, of
course. Princeton University Press now takes out space ads in
general-interest publications prominently displaying the sober visage of
Yale University economics professor Robert Shiller, the maven of the
housing crash. Not every academic scribbler can expect this kind of
attention from his publisher. But such fame comes only after the fact.
The more housing prices rose and the longer predictions of their decline
looked to be wrong, the lonelier the intellectual nonconformists became.
Sociologists may be more familiar than economists with the psychic costs
of nonconformity. But because there is a strong external demand for
economists’ services, they may experience even-stronger economic
incentives than their colleagues in other disciplines to conform to the
industry-held view. They can thus incur even-greater costs—economic and
also psychic—from falling out of step.
WHY BELABOR these points? Because it was not that economic theory had
nothing to say about the kinds of structural weaknesses and conflicts of
interest that paved the way to our current catastrophe. In fact, large
swaths of modern economic theory focus squarely on the kind of generic
problems that created our current mess. The problem was not an inability
to imagine that conflicts of interest, self-dealing and herd behavior
could arise, but a peculiar failure to apply those insights to the real
world.
Take for example agency theory, whose point of departure is the
observation that shareholders find it difficult to monitor managers, who
have an incentive to make decisions that translate into large
end-of-current-year bonuses but not necessarily into the long-term
health of the enterprise. Risk taking that produces handsome returns
today but ends in bankruptcy tomorrow may be perfectly congenial to CEOs
who receive generous bonuses and severance packages but not to
shareholders who end up holding worthless paper. This work had long
pointed to compensation practices in the financial sector as encouraging
short-termism and excessive risk taking and heightening conflicts of
interest. The failure to heed such warnings is all the more striking
given that agency theory is hardly an obscure corner of economics. A
Nobel Prize for work on this topic was awarded to Leonid Hurwicz, Eric
Maskin and Roger Myerson in 2007. (So much for the idea that it is only
the financial engineers who are recognized by the Nobel Committee.)
Then there is information economics. It is a fact of life that borrowers
know more than lenders about their willingness and capacity to repay.
Who could know better what motivation lurks in the mind of the borrower
and what opportunities he truly possesses? Taking this observation as
its starting point, research in information economics has long
emphasized the existence of adverse selection in financial markets—when
interest rates rise, only borrowers with high-risk projects offering
some chance of generating the high returns needed to service and repay
loans will be willing to borrow. Indeed, if higher interest rates mean
riskier borrowers, there may be no interest rate high enough to
compensate the lender for the risk that the borrower may default. In
that case lending and borrowing may collapse.
These models also show how borrowers have an incentive to take on more
risk when using other people’s money or if they expect to be bailed out
when things go wrong. In the wake of recent financial rescues, the name
for this problem, “moral hazard,” will be familiar to even the casual
newspaper reader. Again this is hardly an obscure corner of economics:
George Akerlof, Michael Spence and Joseph Stiglitz were awarded the
Nobel Prize for their work on it in 2001. Here again the potential
problems of an inadequately regulated financial system would have been
quite clear had anyone bothered to look.
Finally there is behavioral economics and its applications, including
behavioral finance. Behavioral economics focuses on how cognition,
emotion, and other psychological and social factors affect economic and
financial decision making. Behavioral economists depart from the
simpleminded benchmark that all investors take optimal decisions on the
basis of all available information. Instead they acknowledge that
decision making is not easy. They acknowledge that many decisions are
taken using rules of thumb, which are often formed on the basis of
social convention. They analyze how, to pick an example not entirely at
random, decision making can be affected by the psychic costs of
nonconformity.
It is easy to see how this small step in the direction of realism can
transform one’s view of financial markets. It can explain herd behavior,
where everyone follows the crowd, giving rise to bubbles, panics and
crashes. Economists have succeeded in building elegant mathematical
models of decision making under these conditions and in showing how such
behavior can give rise to extreme instability. It should not be a
surprise that people like the aforementioned George Akerlof and Robert
Shiller are among the leaders in this field.
Moreover, what is true of investors can also be true of regulators, for
whom information is similarly costly to acquire and who will similarly
be tempted to follow convention—even when that convention allows
excessive risk taking by the regulated. Indeed, these theories suggest
that the attitudes of regulators may be infected not merely by the
practices and attitudes of their fellow regulators, but also by those of
the regulated. Economists now even have a name for this particular
version of the intellectual fox-in-the-henhouse syndrome: cognitive
regulatory capture.
And what is true of investors and regulators, introspection suggests,
can also be true of academics. When it is costly to acquire and
assimilate information about how reality diverges from the assumptions
underlying popular economic models, it will be tempting to ignore those
divergences. When convention within the discipline is to assume
efficient markets, there will be psychic costs if one attempts to buck
the trend. Scholars, in other words, are no more immune than regulators
to the problem of cognitive capture.
What got us into this mess, in other words, were not the limits of
scholarly imagination. It was not the failure or inability of economists
to model conflicts of interest, incentives to take excessive risk and
information problems that can give rise to bubbles, panics and crises.
It was not that economists failed to recognize the role of social and
psychological factors in decision making or that they lacked the tools
needed to draw out the implications. In fact, these observations and
others had been imaginatively elaborated by contributors to the
literatures on agency theory, information economics and behavioral
finance. Rather, the problem was a partial and blinkered reading of that
literature. The consumers of economic theory, not surprisingly, tended
to pick and choose those elements of that rich literature that best
supported their self-serving actions. Equally reprehensibly, the
producers of that theory, benefiting in ways both pecuniary and psychic,
showed disturbingly little tendency to object. It is in this light that
we must understand how it was that the vast majority of the economics
profession remained so blissfully silent and indeed unaware of the risk
of financial disaster.
WITH THE pressure of social conformity being so powerful, are we
economists doomed to repeat past mistakes? Will we forever follow the
latest intellectual fad and fashion, swinging wildly—much like investors
whose behavior we seek to model—from irrational exuberance to excessive
despair about the operation of markets? Isn’t our outlook simply too
erratic and advice therefore too unreliable to be trusted as a guide for
policy?
Maybe so. But amid the pervading sense of gloom and doom, there is at
least one reason for hope. The last ten years have seen a quiet
revolution in the practice of economics. For years theorists held the
intellectual high ground. With their mastery of sophisticated
mathematics, they were the high-prestige members of the profession. The
methods of empirical economists seeking to analyze real data were
rudimentary by comparison. As recently as the 1970s, doing a statistical
analysis meant entering data on punch cards, submitting them at the
university computing center, going out for dinner and returning some
hours later to see if the program had successfully run. (I speak from
experience.) The typical empirical analysis in economics utilized a few
dozen, or at most a few hundred, observations transcribed by hand. It is
not surprising that the theoretically inclined looked down, fondly if a
bit condescendingly, on their more empirically oriented colleagues or
that the theorists ruled the intellectual roost.
But the IT revolution has altered the lay of the intellectual land. Now
every graduate student has a laptop computer with more memory than that
decades-old university computing center. And she knows what to do with
it. Just like the typical twelve-year-old knows more than her parents
about how to download data from the internet, for graduate students in
economics, unlike their instructors, importing data from cyberspace is
second nature. They can grab data on grocery-store spending generated by
the club cards issued by supermarket chains and combine it with
information on temperature by zip code to see how the weather affects
sales of beer. Their next step, of course, is to download securities
prices from Bloomberg and see how blue skies and rain affect the
behavior of financial markets. Finding that stock markets are more
likely to rise on sunny days is not exactly reassuring for believers in
the efficient-markets hypothesis.
The data sets used in empirical economics today are enormous, with
observations running into the millions. Some of this work is admittedly
self-indulgent, with researchers seeking to top one another in applying
the largest data set to the smallest problem. But now it is on the
empirical side where the capacity to do high-quality research is
expanding most dramatically, be the topic beer sales or asset pricing.
And, revealingly, it is now empirically oriented graduate students who
are the hot property when top doctoral programs seek to hire new faculty.
Not surprisingly, the best students have responded. The top young
economists are, increasingly, empirically oriented. They are concerned
not with theoretical flights of fancy but with the facts on the ground.
To the extent that their work is rooted concretely in observation of the
real world, it is less likely to sway with the latest fad and fashion.
Or so one hopes.
The late twentieth century was the heyday of deductive economics.
Talented and facile theorists set the intellectual agenda. Their very
facility enabled them to build models with virtually any implication,
which meant that policy makers could pick and choose at their
convenience. Theory turned out to be too malleable, in other words, to
provide reliable guidance for policy.
In contrast, the twenty-first century will be the age of inductive
economics, when empiricists hold sway and advice is grounded in concrete
observation of markets and their inhabitants. Work in economics,
including the abstract model building in which theorists engage, will be
guided more powerfully by this real-world observation. It is about time.
Should this reassure us that we can avoid another crisis? Alas, there is
no such certainty. The only way of being certain that one will not fall
down the stairs is to not get out of bed. But at least economists,
having observed the history of accidents, will no longer recommend
removing the handrail.
Barry Eichengreen is the George C. Pardee and Helen N. Pardee Professor
of Economics and Political Science at the University of California,
Berkeley.
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