Tyler Cowen did a matched pair of posts on what he believes to be the common mistakes of left-wing and right-wing economists. What seems so striking to me is not the differences between mistakes made by different kinds of economists, but rather what I believe to be the set of misconceptions that are endemic to the profession.
To begin, let me provide two caveats. First, everything that follows is a generalization about what I take to be the dominant tendencies of professional economists (and especially American economists working in academia and government). It is possible to cite counterexamples to each statement; in fact, every criticism I will make has been anticipated by canonical economists including Hayek, Coase, Knight, Schumpeter, North, and Smith (Adam and Vernon), among others. Second, I am observing the economics profession from the outside as an entrepreneur and business executive engaged in the economy directly. I think that the last formal economics training I had was Evsey Domar’s Comparative Economic Systems seminar at MIT in the 1980s.
I’ve grouped these observations into two broad themes to provide some structure; but in order to get beyond very general abstractions, I’ve also tried to give some of what I think are the most important examples of each theme. At the conclusion of each theme, I’ve highlighted what I see as the negative result of these specific problems, from the point of view of a consumer of the outputs of professional economics.
1. Strategic elision between economics as predictive science, and economics as informed advocacy. Economists will sometimes make explicit claims that “the economic science says X,” and will more frequently make implicit claims for scientific knowledge by flatly asserting the known truth of some predictive assertion. This is normally a statement made around some specific policy question – we should (or should not) execute the following stimulus program; we should (or should not) raise the minimum wage right now, etc.
When pushed to provide the scientific evidence, they will normally reference some combination of empirical analysis of naturally occurring phenomena and mathematical models derived from axiomatic statements about human decision-making. In scientific terms, this is all sophisticated theory-building. What’s lacking is dispositive evidence of the accuracy of the predictive rule that allows the statement about this specific case to be an example of a more general rule that has scientific provenance. Otherwise, all we have is an informed opinion of the type we might have from an expert historian rendering an opinion about something the likelihood that Libya would revert to an authoritarian government within ten years if it overthrew Qaddafi
Among the most important manifestations of this problem are:#more#
a. Lack of focus on controlled experiments as falsification trials. Theory and experiment are to science as inhalation and exhalation are to breathing. Even in scientific fields in which experiments are infeasible, our knowledge of causal relationships is underwritten by traditional controlled experiments. Astrophysics, for example, relies in part on physical laws verified through terrestrial and near-Earth experiments. Economics has traditionally been a consciously non-experimental science (though this is slowly starting to change). This creates a very weak feedback loop to weed out false belief. One can argue that controlled experiments cannot be done for many important economics questions. Fair enough, but then the claim to scientific status for these beliefs is hard to sustain, and leads to the next problem…
b. Ad hoc retreat to non-falsifiable “all else equal” arguments when confronted with apparently disconfirming evidence. The attempt to use predictions about future non-experimental events as falsification tests for beliefs tends to founder on what I have called the “causal density” of society. There is always some plausible excuse for why the prediction was wrong, but the theory is still right. The lack of control in most so-called “natural experiments” is deadly, but is often obscured by the next problem…
c. Hiding behind pseudo-technical jargon. All scientific fields have jargon, but not all fields with jargon create scientific knowledge. Normally, jargon must exist so that statements can be made precise enough to be falsifiable. As one layer of insight builds upon another in a given scientific specialty, jargon comes to include the role of explaining an ever-widening scope of phenomena with reference to prior insights. Jargon is a bug, not a feature: it is a necessary evil for specialists to make progress within a paradigm, but has the disadvantage of preventing non-experts from contributing meaningfully to the discussion. Without the discipline of experimental verification, however, this becomes more like philosophy than science. For economists, this can be a feature, not a bug, if it can be used to intimidate non-experts (generally those who are less comfortable with mathematics).
Result: The lack of a body of useful, reliable and non-obvious rules to predict the impact of proposed government interventions. As somebody who sits outside the profession, debates among economists are a means to an end. All I want is output: tell me the value-creating rules to predict the results of potential courses of action on the major issues of the day that your collective enterprise has produced that I would not have in the absence of your work.
Greg Mankiw, an economics professor at Harvard, is the author of one of the most widely used economics textbooks in the world. In a chapter specifically devoted to arguing for the scientific nature of economics, he presented “a table of propositions to which most economists subscribe.” I found this pretty underwhelming as an argument for economics as predictive science: about 10 to 20 percent of economists apparently disagree with the central results of the field; half of the propositions are value statements concerning the way the world should be run (literally using words like “should”), rather than the kind of predictive rules produced by science; and even the other half, which are theoretically-falsifiable predictive rules, are mostly neither practically testable, nor specific enough to guide rational action. That doesn’t sound like any scientific field that I know about.
I’m not arguing that economics has produced nothing of value, but rather that its most useful outputs are more like those of historians than those of biologists. Draping the cloak of “science” over its findings can often be a rhetorical strategy designed to increase the leverage of economists in policy debates.
2. Use of a model for human mind, and by extension human society, that is simplified to the point of caricature. All rational disciplines, of course, must use abstractions that ignore some of the complexity of the real world. The question as a consumer of the work of the discipline is whether this abstraction supports or precludes the development of practically-useful guidance. The point of the first half of this post is that economics mostly hasn’t done this. It is my view that an over-simplified view of human mind and society is a key reason why not.
a. Ignoring the “irrational” psychological importance of group affiliation, and therefore under-emphasis on the role of institutions in promoting self-image rather than merely material self-interest. At the timescale of biological evolution, an extended commercial republic is brand new invention. While we have more just-so stories than legitimate scientific knowledge about the role of evolution in shaping human nature, one should expect that the faster pace of social change than of biological evolution is likely to create profound conflicts.
Any sustainably great collective — IBM, the Berlin Philharmonic Orchestra, the Pittsburgh Steelers, the U.S. Marine Corps, the University of Cambridge, or the United States of America — appeals to the rational self-interest of its members, but also creates a sense of irrational identification with the enterprise. Individuals within each will, to some extent and in some circumstances, sacrifice narrowly-construed perceived self-interest for the good of the whole. This kind of motivation is far more central to the lives of most real people than it is to most economic theories.
b. Many other examples include the mercurial nature of “utility,” the difficulty of forming commitment bonds across kin lines, and the central role of culture in creating economic outcomes. As is sometimes said in business “the soft stuff is the hard stuff.” Though not amendable to analysis, and especially not to quantification, the weird crevices of the human mind manifest themselves powerfully in our daily lives. In combination with the prior example, this tends to scale up to the dizzying nature of the institutions (in the broad sense of the formal and informal “rules of the game”) that determine the economic success and failure of societies. This is not ignored by economics, just radically under-emphasized.
c. Treating uncertainty as if it were risk. Under-emphasizing the complexities of the kind highlighted in the first two examples tends to lead to the problem of excessive belief in that previously-observed patterns are reliable predictors of future behavior. Repeated coin flips are complicated in the sense that we can’t normally predict heads vs. tails on a specific flip; but the series is still subject to probabilistic regularities, such as fair coin tosses should come up heads almost exactly half of the time. Human society is yet more complex, and patterns that seem reliable can suddenly change. This is frustrating to analysts, and therefore often ignored, or given only lip service, en route to making recommendations that rely on the assumption that these patterns will persist.
d. Ignoring the resulting complexities of the evolution of institutions over historical time. Our institutions are often mechanisms for organizing human behavior in light of human complexities, and for making decisions in light of true uncertainty. In an environment of true uncertainty, they have often evolved though trial-and-error, and therefore resist analysis. This is also frustrating to analysts.
Result: Excessive focus on allocative efficiency, at the expense of adaptive efficiency. I really can’t say this any better than Douglass North in his 1993 Nobel lecture: “Neo-classical theory is simply an inappropriate tool to analyze and prescribe policies that will induce development. … It is adaptive rather than allocative efficiency which is the key to long run growth. Successful political/economic systems have evolved flexible institutional structures that can survive the shocks and changes that are a part of successful evolution.”
In sum, academic and government economists routinely overstate their actual degree of reliable, non-obvious knowledge about the answer to the practical question “What will happen if we execute policy X,” because it serves the class interest of economists to do so. Economists follow incentives like everybody else, rather than somehow sitting outside and above the process of buying and selling. Buyer beware.