More Navel-Gazing from Academic Economists

The abstract of a recent paper by Colander, Föllmer, Haas, Goldberg, Juselius, Kirman, and Sloth:

The economics profession appears to have been unaware of the long build-up to the current worldwide financial crisis and to have significantly underestimated its dimensions once it started to unfold. In our view, this lack of understanding is due to a misallocation of research efforts in economics. We trace the deeper roots of this failure to the profession’s insistence on constructing models that, by design, disregard the key elements driving outcomes in real-world markets. The economics profession has failed in communicating the limitations, weaknesses, and even dangers of its preferred models to the public. This state of affairs makes clear the need for a major reorientation of focus in the research economists undertake, as well as for the establishment of an ethical code that would ask economists to understand and communicate the limitations and potential misuses of their models.

The authors call it a “systemic failure of the economics profession.” Krugman calls it “equilibrium decadence,” but rightly reserves his scolding for the macro tribe.

The claim is that academic macroeconomists have become mired in a particularly fruitless equilibrium, in which each is engaged in the search for ever-greater levels of formal elegance, at the expense of empirical relevance. There’s definitely something to this.

Today’s macroeconomists write for other macroeconomists. If you aren’t using the right tools, you aren’t part of the club. And so yesterday’s approach becomes tomorrow’s approach. Echoing Yogi Berra‘s famous dictate, each time macroeconomists came to a fork in the road, we took it. It doesn’t take a radical to suggest that perhaps trying the road less traveled might have led somewhere more interesting.

Despite this observation, I don’t share the gloom of the naysayers, but my optimism comes from looking beyond macro. As a whole, the economics profession has become more empirically grounded. New large datasets offer the prospect of truly understanding individual behavior in a way that paying lip service to “micro-founded models” doesn’t. Many are engaged in the tricky business of writing more psychologically grounded models that are closely tied to real human behavior. Computational advances allow us now to take differences in people, and how they respond, far more seriously. The absence of available data doesn’t necessarily require more complex theory; we are also learning how better to measure the objects we model.

Now all of this good work won’t mean anything unless it starts to impact the macro tribe; and countless corridor discussions overhead at this year’s annual meetings of the American Economic Association suggest that, finally, the rest of the profession is paying attention, and they are demanding more of their macro brethren.

Formally elegant but empirically irrelevant macroeconomists had a much harder time getting hired this year. Curriculum committees are also paying attention, looking to see classes that speak to real economic issues. Economists beyond the macro tribe are paying greater attention, and they aren’t willing to support the intellectually insolvent.

It may be too early to say that the new macro is already taking shape, but I’m willing to bet there’s going to be a renewed emphasis on imperfect and sticky information; rigorous analysis of micro datasets; plausible approaches to empirical identification; and — I hope — a belief that data, rather than op-ed debates can resolve the big debates. Institutions matter; political economy matters too. Behavior can be imperfect, markets can fail, and the unexpected does, in fact, occasionally happen. Today’s problems are both too real and too big to make ignoring the real world a sustainable equilibrium.

Update: Click through for related commentary from Willem Buiter, Mark Thoma, and Brad DeLong.

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  1. Evan says:

    I must say, that was a breath of fresh air.

    Perhaps this is yet another silver lining to this recession, is the impact it will have on the economics profession. I have heard countless times of the difficulty in measuring success of predictions in economics because of the lack of control on the experiments.

    But one thing is clear – the previous approach of complete abstraction was not working.

    I feel this problem is pervasive in both politics and general public discourse about policy. Getting academics to base their claims on evidence is as good a place to start as any.

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  2. Hunter says:

    Well spoken. It’s always bothered me that the four things that actually count in competitive business:

    -fickle consumers (no rational maximizers)
    -brand loyalty (no fungible goods)
    -time, transport and convenience (lots of transaction costs)
    -ignorance of past, present and future (no perfect information)

    are the first four “ceteris”-es that get “paribus”-ed in Econ 101.

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  3. charles says:

    Very good. The post was bigger than this, but I still get a twinge when we talk of more appropriate ways to forecast.

    Regardless of what is done, these are words destined to be repeated:

    “The economics profession appears to have been unaware of the long build-up to the current worldwide financial crisis and to have significantly underestimated its dimensions once it started to unfold”

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  4. kevin says:

    I found if you copy that blurb, and replace “environmental science” with “economics” it is just as interesting.

    Just saying – environmental models for climate change are presented as Gospel.

    But, I agree with the take on macroeconomics.

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    • Barry says:

      “Just saying – environmental models for climate change are presented as Gospel.”

      If you knew the first thing about it, you’d realize how wrong you are. They get analyzed and tested and re-run by multiple parties, among which are the deep-pocketed denialist ‘think tanks’.

      And they’ve been confirmed by future data, and by hindcasting.

      IOW – the things that the right says about climate change modeling and climatology are *exactly* what is wrong with macro.

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  5. David Chowes, New York City says:

    Firstly, economics is not a science. (Though some aspects of it evidence a semblence of a ‘social science’ — at best.)

    We must never forget this!

    Physics is a science — a ‘hard science.’ We can predict to 1/10,000 of a second how long it will take an object to fall, when we drop it from 10 meters.

    No one, except a totally ignorant or crazy person would disagree with my assertion.

    But, with economics, there is not only variance between experts — some Nobel Prize winners offer antithetical “opinions.”

    We, the lay public should never forget this. Economics simply involves too many variables and each professional
    carries with them different value judgements which they believe are desirable.

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  6. Jayson Virissimo says:

    “There’s going to be a renewed emphasis on imperfect and sticky information.

    Institutions matter; political economy matters too. Behavior can be imperfect, markets can fail, and the unexpected does, in fact, occasionally happen. Today’s problems are both too real and too big to make ignoring the real world a sustainable equilibrium.”

    In other words, we should read more Hayek.

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  7. Caliphilosopher says:

    How refreshing. Perhaps there will be more collaboration between political/sociological economists, as well as psychologists and game-theorists. Perhaps philosophers of economics (as well as philosophers of science generally) will also be viewed as important to the development of better ways to cull significant data and to help create models that bring pertinent information to the forefront.

    I just hope this attempt to quantify everything (based on what I’ve read from this blog) happens to subside quite a bit.

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  8. Richard Green says:

    Computing and IT are bringing thousands of massive data sets every day for every conceivable issue to be studied in micro.

    In macro, any given economy will give up 4 new data points for GDP, 4 new points for unemployment, 4 new points for inflation and as little as one for productivity.

    How far is a researcher going to get mining those datasets again? Particularly when you’re under publish or perish conditions, why take the long shot of thinking you can pull something original out of the limited data sets on the few (but absolutely massive) issues covered by macro?

    Academics respond to relative implicit prices just like everyone else!

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