As we enter the Janet Yellen age of Federal Reserve stewardship, I am perhaps most struck by the contrast with her predecessors. I certainly don’t mean that in reference to her sex, rather her career trajectory and professional history. Ben Bernanke, for example, while being a Princeton professor, had established a very distinguished record of scholarship, much of which was ground-breaking (for the orthodox persuasions) and well-known. Alan Greenspan had served in wide variety of policy roles and private business, serving as Richard Nixon’s campaign coordinator for domestic policy, Chairman of President Ford’s Council of Economic Advisors and on the boards of such companies as Alcoa, ADP and, of course, JP Morgan (it’s easy to see why and where he changed from a gold adherent in the 1960’s to the “maestro” of soft central planning through blatant monetarism by the 1980’s).
Yellen was Chairman of Bill Clinton’s Council of Economic Advisors from 1997 to 1999 (bubble time) and was a Fed Governor for three years before that, but the rest of her career was academic. And in that academic period there is, to me, a conspicuous lack of a track record of comparable scholarship or experience. Her most famous paper has little to do with economics at all, a study (co-authored with her husband, Nobel Laureate George Akerlof) of the consequences arising from the increase in single parenting. It essentially looks at the economic exchange underpinning marriage, and how that has evolved in a world with contraception and legalized abortion.
But I think that symbolizes a lot of what economists have become. The popular perception of an economist is an expert on the economy and business (maybe even markets). I don’t know if that was ever true, but the economics profession was certainly far more focused on the economy, business and markets at one point. An economist today is really just a statistician, and therefore, like Yellen and Akerlof’s famous paper, they are “useful” at creating and analyzing the mathematics that underpins “studies” of whatever topic.
To see this in more practical terms, one need only thumb through any of the various trade journals. There is far more math in them, particular regressions of various types (though they make a concerted effort to stay away from the more complex, non-linear tools, such as fractals, that may actually hold some promise). That is not to say there is no use in such statistical focus, after all, correlations lie at the heart of all knowledge, but such is not unique to economics. There should be at least some attempt at qualitative analysis rather than sole dedication toward the attempt to quantify anything and everything. A dynamic world makes such dedication a dangerous proposition, particular as change renders previous correlations moot and new innovations and relationships cannot be quantified before the fact.
To offer an example, I made use of a Federal Reserve study in an article about the college bubble. It provided some quantification of the dilution of the graduate pool as the credit bubble in student debt (and government spending on post-secondary education) widened. It was simply one piece of an overall qualitative analysis. However, it is very useful as an example of what economics has become; this is pretty much standard for what passes in economists’ studies and papers:
From that equation (and others presented), the authors make the finding that,
Thus, taken together, our results provide direct evidence that better job matching of skilled workers in large and dense urban environments acts as a source of urban agglomeration economies, although the contribution of such job matching to aggregate urban productivity appears to be relatively modest.
A whole lot statistical correlation for a relatively modest result. Incidentally, my interest in the study was their finding that 61.2% of college graduates hold a job that does not require a diploma, which, using common sense without need for such complex math, more than suggests a possibility that we produce too many college graduates for the labor market.
Ronald Coase, a Nobel Laureate himself, decried this kind of over-adherence to quantification and uniformly statistical analysis, i.e., econometrics. In 1972, he published an article largely decrying the state of economics in which he posited,
Of course, more recently, the desire to reduce the burden of taxes has become another way of explaining why businesses adopt practices they do. In fact, the situation is such that if we ever achieved a system of limited government (and, therefore, low taxation) and the economic system were clearly seen to be competitive, we would have no explanation at all for the way in which the activities performed in the economic system are divided between firms. We would be unable to explain why General Motors was not a dominant factor in the coal industry, or why A & P did not manufacture airplanes.
In other words, because economists were in a rush to quantify everything into simple variables that could be used in regressions (in his example, tax policy), they lost an element of common sense and “practical” economics; to the point that they would no longer understand why GM was not in the coal business. It’s a powerful critique that has, I think, been realized in the modern strain to some great degree. Janet Yellen, even moreso than Bernanke, embodies that modern trend. They are statisticians first, and are therefore unable to fully understand more practical implications, such as how businesses might actually react to manipulation in inflation expectations (I’ve never seen an econometric model incorporate the propensity toward stock repurchases; it’s as if economists are unaware that it takes place at all). They simply assume they can model human behavior, largely because of this aggregation process (and a heaping helping of efficient market theory).
It really is impressive math, but, as Coase amply identified, there are limits to the extent by which it actually applies to the human systems we actually live inside. An economist has become a statistician, first and foremost, thus the primary dedication, discipline and expertise applies in the math, not the economy.
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