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 assumptions.

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? Or Darwin'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 models.

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 use it.

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 being studied.

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: 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 assumption.

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 world.

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 Rationalism.

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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Endnotes:

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.