The Long FAQ on Liberalism
A Critique of the Chicago School of Economics:
THE METHODOLOGY OF THE CHICAGO SCHOOL
The Chicago School is famous for its math-heavy approach to
economics. At the height of its influence in the 70s and early
80s, Robert Lucas and his followers built one of the greatest
mathematical edifices of all time with the theory of Rational
Expectations. Indeed, calling it the "Taj Mahal of graphs
and equations" would probably be an injustice. The School
also emphasizes research into theory, rather than the practical
application of economics to policy. Finally, the School is famous
for primarily basing its theoretical and mathematical models not
on realistic data, but on imaginary, ideal and incomplete starting
All this is reminiscent of the old saying that "Theoretically,
anything can be proven theoretically." To avoid this pitfall,
most disciplines base their theories primarily on real-world data.
The danger of "theorizing in a vacuum" is that you can
theorize anything you want, even your pet political beliefs. But,
for some reason, most economists have resisted grounding their
theories in the real world. This problem has even been given a
name in economics: the "Ricardian vice." This is not
to say that all economists are guilty of this, or that economics
isn't gradually becoming a more empirical science. Liberals have
been leading reform efforts to bring more realistic assumptions
to economics. New Keynesians, for example, operate on George Akerlof's
key assumption that human beings are not perfectly rational, but
nearly rational. And institutionalists long ago rejected the static
equilibrium models favored by the Chicago School, instead viewing
the economy as a process of continual change. Not surprisingly,
these schools are better at explaining economic events in the
real world, and that is why the Chicago School has lost clout
in academia over the last several years.
To be sure, math is an important tool in economics. Thanks to
the existence of prices, economics is one human activity that
can be measured more mathematically than others. And on some occasions,
math models have proven tremendously helpful in explaining economic
events. But math should be only one tool among many in the economist's
toolbox. The problem with the Chicago School can be found in Abraham
Maslow's famous quip: "When the only tool you own is a hammer,
every problem begins to resemble a nail."
This leads to the critical question of how economists view their
field as a science. Is economics a science more like Newton's?
Newton described a world of physics governed by nice, tidy laws.
An astronomer can predict almost exactly where a planet will be
a year from now. A physicist can predict the paths and final positions
of all the billiard balls on a pool table after hitting the queue
ball. The simplicity of these systems allow physicists to describe
them mathematically -- or, in economic jargon, to build mathematical
Darwin described a world of nature governed by messy, complicated
laws. No biologist can predict what will happen after a new species
of bees is introduced to a Brazilian rain forest. Will they thrive?
Will they die? Will they stay in place or migrate? No mathematical
model can predict their future because the number of factors needed
to be entered into the equation is astronomical. Even worse, the
smallest deviation in the subjects' behavior may set off a completely
different chain of events. If the bees migrate north, finches
might become extinct; if they migrate north-east, endangered irises
might survive. And with so many actors setting off so many chains
of events, the results are better described by chaos theory, not
Newtonian physics. Biologists must therefore use methods other
than math to discern the laws of ecosystems. They do use math,
of course, but not exclusively, and not in the way that physicists
The economy is like the ecosystem -- it is nonlinear, chaotic,
messy and unpredictable. What makes it even more complicated is
that it is a distinctly human activity, meaning that it is impossible
to understand the economy without first understanding the human
animal. True, people are rational, calculating and self-interested,
but they are also unselfish, irrational, mistaken, self-destructive,
ignorant and swept up by herd instincts. That economists would
attempt to understand the economy through math alone, without
studying human psychology, is an approach that will surely bring
smiles to future economic historians.
Nonetheless, today's conservatives try. Economic journalist Robert
Kuttner writes: "As a scholarly discipline, economics has
always suffered from physics envy -- today more than ever."
(1) The desire to be counted among the hard sciences runs astonishingly
deep among economists. Alfred Marshall, who created the first
elaborate math models in economics, was in fact a trained physicist,
and his work was praised in those terms. His student, John Maynard
Keynes, described it as "a whole Copernican system, by which
all the elements of the economic universe are kept in their places
by mutual counterpoise and interaction." (2)
Unfortunately, the physicist/economist can only cram a complex
and exciting world into simplistic equations by doing violence
to the data. First, the physicist/economist must assume away most
of the complexities of the world. Economic actors are assumed
to be 100-percent self-interested, have perfect information about
the market, are perfect calculators, and are perfectly rational.
Their competitors are assumed to sell products that are perfectly
homogenous (that is, exactly the same as everyone else's). There
are zero transaction costs, zero negotiation costs, zero barriers
to market entry and bankruptcy. If an economist wants to study
the costs of market entry, his model will include these costs,
but all the other factors will be assumed away or held to be perfect.
Furthermore, many of these models assume, in effect, that time stands still. The economy
is assumed to be one of static equilibrium, not dynamic change.
There are no inventions, no innovation, and no fluctuation in
supply and demand. Everything is held constant except the variables
Needless to say, these models do not describe the real world.
In order to make them more realistic, economists would have to
fill in the countless variables they're assuming away -- an overwhelming
and impossible task. A good analogy of what they do instead is
the physicist who studies a butterfly flitting across a meadow
and tries to extrapolate where it will land. Of course, anything
could alter its course -- the wind, a predator, a whiff of nectar
-- so he assumes away all these complexities in order to let the
equation give an answer. When the answer turns out to be wrong,
he indulges in apologetics, like "Well, it's good to know
these answers in principle," or "We have to simplify
the equation if we are to study butterflies at all."
Sometimes there are ways to get around these difficulties, such
as studying a problem with only a few variables, or filling in
the most important ones to get a ballpark answer. But in general
these approaches have significant limitations. Economists are
quick to admit these shortcomings, yet they are extraordinarily
reluctant to change their methods. As British economist John Eatwell
once remarked, "If the world is not like the model, so much
worse for the world." (3)
To most scientists, such a statement would be incomprehensible.
The main purpose of science is to explain the real world, not
engage in irrelevant intellectual puzzles. The gap between the
real world and economic theory is why even economists refer to
their field as the "the dismal science." Paul Krugman
describes it this way:
"It's a primitive science
If you want a parallel, think
of medicine at the turn of the century. Medical researchers had,
by that time, accumulated a great deal of information about the
human body and its workings, and were capable of giving some critically
useful advice about how to avoid disease. They could not, however,
cure very much." (4)
But producing inaccurate science is not the only failure of math
models. They also allow political bias. And this is where the
Chicago School comes in for special criticism.
Math and bias
To the general public, both math and statistics carry an air
of scientific authority. The fact that they can be abused for
political ends is therefore disconcerting -- and explains why
books like "How to Lie with Statistics" become classics.
As Benjamin D'Isreali said, "There are only three kinds of
lies: lies, damn lies, and statistics."
Math can be similarly abused. The trick is to select the right
starting assumptions. The analysis flowing from these assumptions
might be logically sound -- indeed, as perfect as math -- but
the conclusions could still be entirely wrong. For example, adding
two molecules of matter to two molecules of anti-matter is not
going to yield four molecules. This conclusion is mathematically
correct but factually wrong, for it is based on an (obvious) false
How do scientists identify the correct starting assumptions? There
are two ways. The present scientific method is the most widely
accepted. Scientists base their theories on real-world data; they
then use these theories to identify yet more data for yet more
theories. The accuracy of this cycle is verified by experiment
and prediction. If data and theory diverge, then scientists know
something is wrong; either the theory is bad, or the data is poorly
collected or interpreted.
The other method is to base theories not on real-world data, but
on axioms, postulates or other knowledge which we believe to be
true without real-world confirmation. A notorious example of this
method is the Austrian School of Economics. Ludwig von Mises claimed
that the idea that "humans act" is a priori knowledge.
He then used this knowledge as the basis for all his theories.
Data from the real world did not inform his theories -- on the
contrary, these theories were used to explain events in the real
This second method (deduction from first principles) is not scientific.
Deduction does occur in science, but it is based primarily on
other data, not first principles. This method is actually a relic
of philosophical history called Rationalism. It was overturned
in the 18th and 19th centuries by Empiricism,
Positivism, and Immanuel Kant's Critique of Pure Reason.
The reason why Rationalism fell out of favor was because philosophers
realized that it turned the search for truth into a game without
rules. Rationalists were free to theorize anything they want without
having to worry about potentially contrary statistics, data, history
or other evidence from the real world. Rationalism is what religions
use when they argue the logical "proof" of their various
beliefs. The fact that there are thousands of different religions
in the world shows the fallibility of this method.
The method currently used by modern economists lies in a gray
area between the scientific method and Rationalism. To be sure,
mathematical models are tools of the scientific method, and economists
would probably howl in protest if accused of conducting anything
other than science. But how economists select and treat their
starting assumptions bears more than a passing resemblance to
For example, take the assumption that economic actors are 100-percent
self-interested. This is a conservative political belief, and
one that defies the evidence: namely, the widespread existence
of charity, favors, parental investment, martyrdom, and other
sacrifices for cause or country. Rather than clarify this matter
through research and hard data, many economists have simply chosen
to treat self-interest as a given, as a first principle. On one
hand, you could call this procedure "simplifying the assumptions,"
which does indeed occur in many scientific models. But when it
becomes extreme enough, you could call it Rationalism.
Another example where "simplification" approaches Rationalism
is the use of perfect starting assumptions. If you are an economist
who believes that the government should not interfere with the
market, then it is up to you to prove that the market does not
fail or commit atrocities. One way of making the market perfect
is to assume that all its participants are perfect. This is accomplished
by endowing actors with perfect knowledge, perfect calculating
ability, and perfect rationality. It is interesting to note that
when economists do study market failures (like monopolies,
pollution, and asymmetrical information), they assume that the
other conditions are perfect, and this usually allows the
market to solve the problem.
Another example occurs when economists select which factors will
be included in their models. In the real world, there are countless
hundreds, even thousands, of factors influencing any economic
event. By picking the right ones to include in the model and leaving
out all the rest (for "simplification" purposes), an
economist can change the result. But why these factors,
and not others? As we shall see, the Coase Theorem is guilty of
just this sin.
The best way to avoid these pitfalls is to base theory not on
idealistic assumptions or first principles, but on real-world
evidence. Economists may object that the world is too complicated
for such an approach. But this is the method that biology uses,
and it has hardly prevented biologists from making scientific
progress. In fact, biology has progressed far beyond economics
-- and its more scientific method is why.
How widespread are these problems in economics? In 1982, at the
height of Rational Expectations, Nobel economist Wassily Leontief
surveyed four years' worth of articles in America's leading economics
journal, The American Economic Review. He discovered that
more than half of the articles were math models without any real-world
data whatsoever, and nearly a fourth analyzed statistics gathered
for some other purpose. Only one article analyzed data collected
by the author -- and that article was about pigeons. (5)
Divorcing economic analysis from the real world actually contains
another danger, other than allowing ideologues to theorize anything
they want. And that is that we have no way to judge the correctness
of these theories. They might be internally consistent, but many
fallacies are internally consistent. The only sure way to verify
them is to compare them to the real world. But if economists reject
this sort of confirmation, or at least don't care very much about
it, then how can we tell if a theory is correct?
This makes for some interesting selections when it comes time
to award the Nobel prize in economics.
Next Section: A Review of Keynesian Theory
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1. Robert Kuttner, "The Visible Hand Guiding the Prize in
Economics," Business Week, November 12, 1990, p. 20.
2. See Robert Heilbroner, The Worldly Philosophers, 3rd
ed. (New York: Simon & Schuster, 1967), p. 188.
3. Quoted in Robert Kuttner, "The Poverty of Economics,"
Atlantic Monthly, February 1, 1985, p. 76.
4. Paul Krugman, Peddling Prosperity, (New York: W.W. Norton
and Company, 1994), p. 9.
5. Michael Rothschild, Bionomics (New York: Henry Holt
and Company, 1990), p. 52-3.