It doesn't have to be this hard.

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Explaining How Economists Explain

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When economists say they can "explain" something, beware: Their understanding of the word might be very different from yours.

Several years ago, in the immediate wake of the financial crisis, economist Ricardo Caballero wrote about what he called the “pretense-of-knowledge syndrome” in academia. Economists, he argued, had become “so mesmerized” with the internal logic of their theories that much of the discipline -- even that part concerned directly with policy making -- had spiraled off into fantasy. Even when they studied issues close to the crisis, such as bubbles, panics and fire sales, they relegated them to the periphery of macroeconomics, which at its core valued mathematical elegance over usefulness.

Not much has changed since then. That, at least, is the conclusion of Itzhak Gilboa and a group of economists who recently tried to understand why their profession operates so differently from most sciences. Academic economists, they say, use the term "explanation" in a way that other scientists never would. Instead of developing realistic and testable theories like those in biology or physics, they often aim only to develop "theoretical cases" -- imaginary mathematical worlds with their own rules of cause and effect.

Suppose, for example, that an economist wants to explain a persistent recession following a financial crisis. He or she may build a mathematical model in which companies and people are perfectly rational, think far into the future and alter their investment and consumption only by choice -- not in response to, say, unemployment or a credit freeze. In such a world, deficit spending meant to create jobs would have no desirable effect: People would cut back in anticipation of the higher future taxes required to pay for the stimulus.

Needless to say, the usefulness of such a model for policy would depend on the extent to which it corresponded to reality. And yet, Gilboa and his colleagues suggest that most economists don't see checking the external validity of models as part of their job. Rather, they like to make whatever assumptions are needed to prove their results, get published in a journal, and then “leave the similarity judgements to practitioners.” If their results are inappropriately applied in the real world, that's not their problem. In no way does it threaten the reputation of the theories they have developed.

What's disturbing is that most scientists would think that risking a theory's reputation is at the core of good practice. Science isn't just about making analogies, but about separating useful analogies from not-so-useful ones. False models are sources of confusion.

Unfortunately, all of this has real consequences. A few years ago, for example, economist George Akerlof observed that a number of theorems coming from the modern mathematical approach to macroeconomics appear to demonstrate remarkable things -- that monetary policy should have no effect on economic output, for example, or that government spending can't spur consumption. These conclusions rest on entirely unrealistic assumptions, as is common for “theoretical cases.” Nonetheless, many economists still cite such theorems as if they should inform the government's actions.

The work of Gilboa and his colleagues sheds a useful light on why economists seem so strange to other scientists: They really do expect far less from their explanations. Of course, building models with unrealistic assumptions can be a useful way to think about difficult problems and get closer to theories that better depict the real world. But it's no way to design policies that affect the livelihoods of millions of people.

This column does not necessarily reflect the opinion of Bloomberg View's editorial board or Bloomberg LP, its owners and investors.

To contact the editor on this story:
Mark Whitehouse at mwhitehouse1@bloomberg.net