Graduate education in economics

16 Nov, 2020 at 17:44 | Posted in Economics | 7 Comments

Modern economics has become increasingly irrelevant to the understanding of the real world. In his seminal book Economics and Reality (1997) Tony Lawson traced this irrelevance to the failure of economists to match their deductive-axiomatic methods with their subject.

It is — sad to say — as relevant today as it was twenty-three years ago.


It is still a fact that within mainstream economics internal validity is everything and external validity nothing. Why anyone should be interested in that kind of theories and models is beyond my imagination. As long as mainstream economists do not come up with any export-licenses for their theories and models to the real world in which we live, they really should not be surprised if people say that this is not science, but autism!

Studying mathematics and logics is interesting and fun. It sharpens the mind. In pure mathematics and logics we do not have to worry about external validity. But economics is not pure mathematics or logics. It’s about society. The real world.

Already back in 1991, Journal of Economic Literature published a study by the Commission on Graduate Education in Economics (COGEE) of the American Economic Association (AEA) — chaired by Anne Krueger and including people like Kenneth Arrow, Edward Leamer, Joseph Stiglitz, and Lawrence Summers — focusing on “the extent to which graduate education in economics may have become too removed from real economic problems.” The COGEE members reported from own experience “that it is an underemphasis on the ‘linkages’ between tools, both theory and econometrics, and ‘real world problems’ that is the weakness of graduate education in economics,”  and that both students and faculty sensed “the absence of facts, institutional information, data, real-world issues, applications, and policy problems.” And in conclusion they wrote (emphasis added):

The commission’s fear is that graduate programs may be turning out a generation with too many idiot savants skilled in technique but innocent of real economic issues.

Sorry to say, not much is different today. Economics education is still in dire need of a remake.


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  1. Lars, much of your concerns seem to me to apply just as much, and perhaps more importantly, to pandemics. It would certainly seem useful to have some common conceptual underpinning for both economics and pandemics: how else could we make ‘informed’ decisions?
    I see that “COGEE members from their own experience shared the perception that it is an underemphasis on the “linkages” between tools, both theory and econometric, and “real world problems” that is the weakness of graduate education in economics.
    The weakness is not an excessive use of mathematics. …”
    My own view is that many people in many fields have misguided views about these linkages, which isn’t helped by differences in usage of such terms as ‘random’, ‘stochastic’, ‘probability’ and ‘risk’ between different fields.
    Can you suggest any material on these ‘linkages’ that might both provide a common basis for work on policy-making of all kinds and be accessible to a mathematician?

    • I think Mandelbrot and Taleb are useful in this regard.

      • Click to access work890.pdf

        Understanding gross capital flows is increasingly viewed as crucial for both macroeconomic and financial stability policies, but theory is lagging behind many key policy debates. We fill this gap by developing a two-country DSGE model that tracks domestic and cross-border gross positions between banks and households, with explicit settlement of all transactions through banks. We formalise the conceptual distinction between cross-border saving and
        financing, which often move in opposite directions in response to shocks. This matters for at least four policy debates. First, current accounts are poor indicators of financial vulnerability, because in a crisis, creditors stop financing debt rather than current accounts, and because following a crisis, current accounts are not the primary channel through which balance sheets adjust. Second, we reinterpret the global saving glut hypothesis by arguing
        that US households do not finance current account deficits with foreigners’ physical saving, but with digital purchasing power, created by banks that are more likely to be domestic than foreign. Third, Triffin’s current account dilemma is not in fact a dilemma, because the creation of additional US dollars requires dollar credit creation by US and non-US banks rather than US current account deficits. Finally, we demonstrate that the observed high
        correlation of gross capital inflows and outflows is overwhelmingly an automatic consequence of double entry bookkeeping, rather than the result of two separate sets of economic decisions.

        • Thank you … but I don’t eschew Marginalist views at onset – see Daniel Kahneman per se. I also think that due to computational processing of information/data should be considered in the era of Gates Friction less Capitalism as much if not more influence than post dated theory’s.

      • Quite. At they say “While scalable laws do not yet yield precise recipes, they have become an alternative way to view the world, and a methodology where large deviation and stressful events dominate the analysis instead of the other way around. We do not know of a more robust manner for decision-making in an uncertain world.”

        I’d overlooked the significance of ‘yet’. There does seem to be a clear indication that they believe (contra Keynes) that there will one day be such precise recipes, which does indeed imply that they think that the situations they are studying are actually stochastic. But I tend to accept the view of Keynes that there is no possibility of a strictly logical proof of their conjecture, and the only credible evidence for it would be to produce and demonstrate precise recipes, which they hadn’t done at that point.

        So is there any ‘linkage’ beyond such conjectures?

        • Bank balance sheets keep increasing because they have precise recipes. Their models, implicitly or explicitly, include a Fed put. The policy linkage is that we can fund basic income using the Fed, too.

  2. A good share of the coursework I had to tackle during my master’s and PhD were not structured with sufficient attention to matters of fact. However, I will surprise a few people and point to a course on option pricing I took from a finance department as an extremely good example of something extremely concrete and practical.

    The course started with a summary view of the Black-Scholes-Merton model. You look at the model, then you get to evaluate how it fares when you apply it to option data and you ask “what could be changed to make improvements?” Then you dig into stochastic volatility and GARCH-based models as a means of “building up” fatter tails (and asymmetry) over time and you notice you still suck at pricing, say, deep out of the money puts of relatively short maturities. But, if you could just nudge that risk-neutral density to put more tail weight from the get-go, it might help — welcome jumps or their discrete equivalents in inverse Gaussian GARCH models.

    I’ve also been the TA for a new macroeconomics professor in the department who is currently teaching the master’s course in macroeconomic theory. Since I TA’d for the course in the last two years, I know how he teaches: he starts with stylized facts, then he introduces just enough math to be able to work with DSGE models and log-linearized solutions. Most of the course is then spent debating how to modify the most basic real business cycle model to try to improve your capacity to match stylized facts. He even has a segment on structural vector autoregressions where students have to recover a finite VAR approximation to the VARMA ofthe log-linearized solution of the DSGE model — and he even has a case where shocks would be nonfundamental.

    The moral of the story is that not all courses are born equal. Obviously, as you might imagine, it’s hell of a lot more demanding for both professors and students to engage with economics like this. Going back and forth between theories and facts requires a lot of effort, but it is infinitely more fun to do it this way.

    When I think back to the PhD course in microeconomic theory, it felt like I had to drag myself in class for three hours of torture every week. Pretty much the same was true, albeit to a lesser extent with the course on dynamic programming and the other course on macroeconomic theory. The job could have been done better, faster and made vastly more relevant. There’s no reason not to be more inspired by the two good examples I gave.

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