In a reasonably interesting Guardian article, Larry Elliott argues that the macroeconomists of yesteryear were superstars, but the current crop have lost sight of what macroeconomics is supposed to be about: describing the macroeconomy, not writing down fancy mathematical models.
The current crop of macroeconomists would argue that fancy mathematical models are the best way to understand the macroeconomy. That claim might even be proven correct in the long run, but I can’t say that I think it’s the most likely outcome.
In my opinion, the fundamental problem is this: from a modern academic perspective, the sorts of skills that accompany having a good working knowledge of the macroeconomy are not easily measured by, and are not rewarded in, the current incentive schemes for economists. In microeconomics, at least there is an abundance of good data, so people who are good at measuring and describing things can succeed. But in macro there is not much data, so most of the rewards are for the mathematics, not the empirics.
The single easiest way to make a mark in a modern macro paper is to solve a problem that is really, really hard mathematically. Even if it is not that relevant to anything, it is seen as a sign that the author has “impressive skills,” which is enough to get a job — and even tenure sometimes — at top universities.
You might think that macro forecasting would be an important part of what academic economists would do, but in practice there is almost nothing of that sort being done. That sort of thing is left for economists at places like the Federal Reserve or private banks to do. You might think that the models that most successfully explain economic patterns would rise to the top, but in the current regime, if they are not meticulously constructed from “micro foundations,” they aren’t allowed to be considered.
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“Together we hammered out a series of technical papers that presented Markov as a revolutionary method for forecasting the impact of infrastructure investment on economic development. It was exactly what we wanted: a tool that scientifically “proved” we were doing countries a favor by helping htem incur debts they would never be able to pay off. In addition, only a highly skilled econometrician with lots of time an money could possibly comprehend the intricacies of Markov or question its conclusions. The papers were published… presented… we became famous…”
John Perkins, from “Confessions of an Economic Hit Man” (p. 118)
Part of this is the ongoing story/influence of the Lucas Critique. Lucas had observed that the more descriptive models of his day, lacking a basis in individual decision-making (what we call micro-foundations), were incapable of modeling a change in the agents’ expectations for the future. He also correctly anticipated that, if the Fed tried to hold unemployment down with an inflationary policy, people would change their expectations about future inflation. There were some serious policy implications to this critique–and those implications were borne out in the 1970s.
Microfoundations aren’t useless. In principle, they let you design a system that is more robust to external changes (such as a major shift in the policy regime). What you need, though, is someone sorting through the insights of the complicated models, figuring out which ones are most important to a given setting, and constructing a simpler, more useable version for that setting.
Andy is exactly right. Furthermore, the emphasis on micro foundations comes from the complete failure (empirically and in actual policy implementations) of models that appeared to fit the data, had heuristic stories to back them up but did not have rigorous micro foundations.
The really good macro guys are very serious and care only about understanding the real macro economy, they generally care little for fancy math for its own sake.
I mean seriously, have you really never met Woodford or Sims? (Surely you won’t respond that their work is not highly techincal?)
So we should rely instead on the virtues of prudence, temperance, justice, and fortitude?
“In microeconomics, at least there is an abundance of good data, so people who are good at measuring and describing things can succeed. But in macro there is not much data, so most of the rewards are for the mathematics, not the empirics”
Isn’t it the other way around? In microeconomics, there’s a limited amount of data (facts, quantities, variables), so you can (sometimes) formulate a good, predictive mathematical model. Whereas, it seems to me, in macroeconomics, there is simply too much data (facts, variables, etc.) to put together a reliable predictive formula?
Macroeconomics deals with such a complex and volatile world, where the variables are constantly changing that mathematical models will inevtably become obsolete. Better to focus on roughly hewn relationships that we know such as the simplicity of the Taylor rule or the Fisher effect. The more complex the math in a macro model, the faster it will breakdown in the real world, or the less it will have to do with the real world.
Bravo! That is, bravo for speaking the truth. Academic freedom in economics has long been ‘dis-allowed’. This bizarre culture, anti-liberal attitude is something I experienced and have not forgotten. It still appears that this “culture of control” has stunted the potential of economic theory to evolve beyond the Keynesian versus Monetarist debates of the 80′s.
Your article was too short – I wanted to read more!
MC Morley
Levitt is quite correct in his assessment. My field is “health” which is front and center for politicians today –and yet there are perhaps only a half-dozen economists in the US who seriously try to understand and forecast US health care spending (2 or 3 at CMS, 1 or 2 at CBO, 2 elsewhere). Good “Microfoundations” are particularly hard to come by in health because these markets do not function particularly well, and are constrained by institutions (which leads to inertia and long lags). In response to comment by Andy about the legitimacy of “observations with no explanations” — these observations (such as the lag between GDP and spending, or the lag between CPI and hc wages) are where one can begin to develop an understanding of the dynamics –which is almost impossible to get from looking at cross-sectional microdata.
TEG, Exec. Director, International Health Econ Assoc.