“The Flexible Unity of Economics,” M. J. Reay (2012)

Michael Reay recently published this article on the economics profession in the esteemed American Journal of Sociology, and as he is a sociologist, I hope the econ navel-gazing can be excused. What Reay points out is that critical discourse about modern economics entails a paradox. On the one hand, economics is a unified, neoliberal-policy-endorsing monolith with great power, and on the other hand, in practice economists often disagree with each other and their memoirs are filled with sighs about how little their advice is valued by policymakers. In my field, innovation policy, there is a wonderful example of this impotence: the US Patent and Trademark Office did not hire a chief economist until – and this is almost impossible to believe – 2010. Lawyers with hugely different analytic techniques (I am being kind here) and policy suggestions both did and still continue to run the show at every important world venue for patent and copyright policy.

How ought we explain this? Reay interviews a number of practicing economists in and out of academia. Nearly all agree on a core of techniques: mathematical formalism, a focus on incentives at the level of individuals, and a focus on unexpected “general equilibrium” effects. None of these core ideas really has anything to do with “markets” or their supremacy as a form of economic organization, of course; indeed, Reay points out that roughly the same core was used in the 1960s when economists as a whole were much more likely to support various forms of government intervention. Further, none of the core ideas suggest that economic efficiency need be prioritized over concerns like equity, as the technique of mathematical optimization says very little about what is to be optimized.

However, the choice of which questions to work on, and what evidence to accept, is guided by “subframes” that are often informed by local contexts. To analyze the power of economists, it is essential to focus on existing local power situations. Neoliberal economic policy enters certain Latin American countries hand-in-hand with political leaders already persuaded that government involvement in the economy must decrease, whereas it enters the US and Europe in a much more limited way due to countervailing institutional forces. That is, regardless of what modern economic theory suggests on a given topic, policymakers have their priors, and they will frame questions such that they advice their economic advisers gives is limited in relation to those frames. Further, regardless of the particular institutional setup, the basic core ideas about what is accepted as evidence to all economists means that the set of possible policy advice is not unbounded.

One idea Reay should have considered further, and which I think is a useful way for non-economists to understand what we do, is the question of why mathematical formalism is so central a part of the economics core vis-a-vis other social sciences. I suggest that it is the economists’ historic interest in counterfactual policy that implies the mathematical formalism rather than the other way around. A mere collection of data a la Gustav Schmoller can say nothing about counterfactuals; for this, theory is essential. Where theory is concerned, limiting the scope for gifted rhetoricians to win the debate by de facto obfuscation requires theoretical statements to be made in a clear way, and for deductive consequences of those statements to be clear as well. Modern logic, roughly equivalent to the type of mathematics economists use in practice, does precisely that. I find that focusing on “quantitative economics” meaning “numerical data” misleading, as it suggests that the data economists collect and use is the reason certain conclusions (say, neoliberal policy) follow. Rather, much of economics uses no quantitative data at all, and therefore it is the limits of mathematics as logic rather than the limits of mathematics as counting that must provide whatever implicit bias exists.

Final July 2012 AJS version (Note: only the Google Docs Preview allows the full article to be viewed, so I’ve linked to that. Sociologists, get on the open access train and put your articles on your personal websites! It’s 2012!

“Innovation: The History of a Category,” B. Godin (2008)

What is innovation? What, indeed, is invention? I am confident that the average economist could not answer these questions. Is invention merely a novel process or idea? A novel process or idea for a given person? A new way of combining real resources like capital and labor? A new process which allows more of something to be created using a given amount of real resources? Does the new process need to be used, or embodied in technology, or is the idea enough?

None of these definitions seem satisfactory. A poem is a “new idea”, but we wouldn’t call it an invention. Novelty for a given person without technological embodiment, as a definition, doesn’t seem to distinguish between diffusion and simple learning. The idea of technology as a Solow residual means that merely using different mixtures of capital and labor to make the same product doesn’t qualify, and further the Solow residual includes things like Bowles-style adaptations to a more cooperative or trusting culture, which we generally don’t think of as innovation. Was Schumpeter correct that invention is a mere act of creativity “without importance to economic analysis”, or does the sequential nature of ideas mean that even non-embodied ideas are economically important?

In an interesting “genealogy of an idea”, Benoit Godin examines the history of how the terms invention and innovation were used in the Western World. The term invention goes back to Cicero, who listed the development of new argumentative concepts as one of the five tools of rhetoric. From the 15th to 19th centuries, invention was used occasionally to mean novel thoughts, but also novel recombinations (as in painting) or simple imitation (such as the patents given to importers in 18th century England).

It is really quite late in the game – well into the twentieth century – that something like “innovation is the invention, embodiment and diffusion of a commercial product” begins to be accepted as a definition. Part of this involves the shift from the individual inventor, the lone genius, to commercial firm R&D, as well as a recognition that simultaneous discovery and ex-post construction of credit meant that the lone genius inventor probably never existed. The terms discovery and invention began to separate. Science policy began to focus much more on the quantifiable, inventions as discoveries embodied in products or countable as patents. The word innovation became identified with an economic sense rather than an artistic sense which it previously possessed.’

Even the economic definition that would eventually be adopted is not the only one that could have developed. Schumpeter is often recognized as the father of economic studies of technological change, but his definition of innovation includes many concepts no longer covered by that term. For Schumpeter, innovation was tightly linked to creative destruction, or the dynamic ability of economic change to remake the commercial sphere. The opening of new commercial markets, for example, was an important part of innovation, whereas pure science was not.

http://www.csiic.ca/PDF/IntellectualNo1.pdf (2008 Working Paper – this is still unpublished, as far as I can tell).

“The Credit Crisis as a Problem in the Sociology of Knowledge,” D. Mackenzie (2011)

(Tip of the hat for pointing out Mackenzie’s article to Dan Hirschman)

The financial crisis, it is quite clear by now, will be the worst worldwide economic catastrophe since the Great Depression. There are many explanations involving mistaken or misused economic theory, rapaciousness, political decisions, ignorance, and many more; two interesting examples here are Alp Simsek’s job market paper from a couple years ago on the impact of overly optimistic potential buyers who need to get loans from sedate lenders (one takeaway for me was that financial problems can’t be driven by the ignorant masses, as they have no money), and Coven, Jurek and Stafford’s brilliant 2009 AER on catastrophe bonds (summary here) which points out how ridiculous it is to legally define risk in terms of default risk, since we have known for decades in theory that Arrow-Debreu securities’ values depend both on the payoffs in future states and on the relative prices in those states. A bond whose default occurs in catastrophic states ought be much more expensive than the same bond whose default is negatively correlated with background risk.

But the catastrophe also involves a sociological component. Markets are made: they don’t arise from thin air. Certain markets don’t exist for reasons of repulsion, as Al Roth has mentioned in the context of organ sales. Other markets don’t exist because the value of the proposed good in that market is not clear. Removing uncertainty and clarifying the nature of a good is a important precondition, and one that economic sociologists, including Donald Mackenzie, have discussed at great length in their work. The evaluation of new products, perhaps not surprisingly, depends both on analogies to forms a firm has seen before, and on the particular parts of the firm who handle the evaluation.

Consider the ABS CDO – a collateralized debt obligation where the underlying debt are securitized assets, most commonly mortgages. The ABS CDO market grew enormously in the 2000s, and was not understood at nearly the same level as traditional CDO or ABS evaluation, topics on which there are hundreds of research papers. ABS and CDO teams tended to be quite separate in investment banks and ratings agencies, with the CDO team generally well trained in derivatives and the highly quantitative evaluation procedures of such products. For ABSs, particularly US mortgages, the implicit government guarantee against default meant that prepayment risk was the most important factor when pricing such securities. CDOs, often based on corporate debt, were used to treating correlation between various corporations in a given CDO as the most important metric.

Mackenzie gives exhaustive individual detail, but roughly, he does not blame the massive default rates on even AAA-rated ABS CDOs on greed or malfeasance. Rather, he describes how evaluation of ABS CDOs by ratings agencies used to dealing with either an ABS or a CDO, but not both, could lead to a utter misunderstanding of risk. While it is perfectly possible to “drill down” a complex derivative into its constituent parts, then subject the individual derivative to a stress test against some macroeconomic hypothetical, this was rarely done, particularly by individual investors. Mackenzie also gives a brief story of why these assets, revealed in 2008 to be superbly high risk, were being held by the banks at all instead of sold off to hedge funds and pensions. Apparently, the assets held were generally ones with very low return and very low perceived risk which were created as a byproduct of the bundling that created the ABS CDOs. That is, arbitrage was created when individual ABSs were bundled into an ABS CDO, the mezzanine and other tranches aside from the most senior AAA tranche were sold off, and the “basically risk-free” senior tranches were held by the bank as they would be difficult to sell directly. The evaluation of the risk, of course, was mistaken.

This is a very interesting descriptive presentation of what happened in 07 and 08.

http://www.socialwork.ed.ac.uk/__data/assets/pdf_file/0019/36082/CrisisRevised.pdf (Final version from the May 2011 American Journal of Sociology)

“737-Cabriolet: The Limits of Knowledge and the Sociology of Inevitable Failure,” J. Downer (2011)

Things go wrong. Nuclear power plants melt down. Airplanes fall from the sky. Wars break out even when both parties mean only to bluff. Financial shocks propagate in unexpected ways. There are two traditional ways of thinking about these events. First, we might look for the cause and apportion blame for such an unusual event. Company X used cheap, low-quality glue. Bureaucrat Y was poorly trained and made an obviously-incorrect decision. In these cases, we learn from our mistakes, and the mistakes are often not simply problems of engineering, but sociological problems: Why did the social setup of a group fail to catch the mistake? The second type of accident, the “normal accident” described famously by Charles Perrow, offers no lessons and is uncatchable in hindsight because it is too regular. That is, if a system is suitably complex, and if minor effects all occur roughly simultaneously, then the one-in-a-billion combination of minor effects can cause a serious problem. Another way to put this is that even if disasters are one-in-a-billion events, a system which throws out billions of possible disasters of this type is likely to produce one. The most famous case here is Three Mile Island, where among the many failsafes which simultaneously went awry was an indicator light that happened, on the fateful day, to have been blocked by a Post-It note.

John Downer proposes a third category, the “epistemic accident,” which is perhaps well-understood by engineers and scientists, but not by policymakers. An epistemic accident is when a problem occurs due to an error or a gap in our understanding of the world when we designed the system. Epistemic accidents are not normal, since once they happen we can correct them in the future, and since they do not depend on a rare concordance of events. But they also do not lend themselves to blame, since at the time they happen, the scientific knowledge necessary to prevent them was not yet known. This is a fundamentally constructivist way of viewing the world. Constructivism says, roughly, that there is no Platonic Ideal for science to reach. Experiments are theory-laden and models are necessarily abstract. This does mean science is totally relative or pointless, but rather that it is limited, and we will always be, on occasion, surprised by how our models (and this is true in social science as well!) perform in the “real world”. Being cognizant of the limits of scientific knowledge is important for evaluating accidents: particularly innovative systems will be more prone to epistemic accidents, for one.

Downer’s example is the famous Aloha Airlines 243 accident in 1988. On a routine flight from Hilo to Honolulu, the fuselage ripped right off of a 737, exposing a huge chunk of the passenger cabin while the plane was traveling at full speed. Luckily, the plane was not far from Maui, and managed to land with only one death – passengers had to, while themselves strapped in, lean over and hold down a stewardess who was lying down in the aisle in order to keep her from flying out of the plane. This was shocking since the 737 was built with multiple failsafes to ensure that such a rupture did not happen; roughly, the rupture would only happen, it was believed, if a crack many feet long developed on the airplane skin, and this would have been caught at a much smaller stage by regular maintenance.

It turns out that testing of the plane was missing two concepts. First, a combination of the glue being used with salt-heavy air made cracks more likely, and second, the way the rivets were lined up happens to make metal fatigue compound as minor cracks near each rivet connect with each other. And indeed, even in the minor world of massive airplane decompression, this was not the first “epistemic accident”. The reason airplane windows are oval and not square is to avoid almost exactly the same problem: some British-made Comets in the 50s crashed and the impact of their square windows with metal fatigue was found to be the culprit.

What does this mean for economics? I think it means quite a bit for policy. Complicated systems will always have problems that are beyond the bounds of designers to understand, at least until the problem arises. New systems, rather than existing systems, will tend to see these problems, as we learn what is important to include in our models and tests, and what is not. That is, the “flash crash” looks a lot like a “normal accident”, whereas the financial crisis has many aspects that look like epistemic accidents. New and complicated systems, such as those introduced in the financial world, should be handled in a fundamentally conservative way by policymakers in order to deal with the uncertainty in our models. And it’s not just finance: we know, for instance, of many unforeseen methods of collusion that have stymied even well-designed auctions constructed by our best mechanism designers. This is not strange, or a failure, but rather part of science, and we ought be upfront with it.

Google Docs Link (The only ungated version I can find is the Google Docs Quick View above which happens to sneak around a gate. Sociologists, my friends, you’ve got to tell your publishers that it’s no longer acceptable in 2012 to not have ungated working papers! If you have JSTOR access, and in case the link above goes dead, the final version in the November 2011 AJS is here)

“Note on the Theory of the Economy of Research,” C. S. Peirce (1879)

Though this site is devoted generally to new research, the essay discussed in this post, I trust, will be new enough to the vast majority of readers. Charles Sanders Peirce is a titan of analytic philosophy, and there is certainly a case to be made that he is the greatest American philosopher of all time. He also has had a fairly well-known indirect influence on economics: Peirce was in some ways rediscovered by the great mathematician Alfred Tarski, who then taught Kenneth Arrow, and in doing so may have introduced Peirce’s relational algebra to the field of economics. (You may be thinking, relational algebra, what is that? But you certainly know what it is: take a set, apply a perhaps partial, often binary ordering with certain properties, then prove results. This surely describes every modern introduction to the theory of preferences, does it not?) But Peirce also has an essay more directly on economics that is fascinating to see in retrospect. This Peirce essay is reprinted in Phil Mirowski’s book “Science Bought and Sold” along with notes on the essay by James Wible which I shall also draw from.

Two final things. First, I note, if only to myself, the following quote from Peirce to be used in a future research paper of my own: “Economical science is particularly profitable to science; and that of all the branches of economy, the economy of research is the most profitable.” Second, check out where this essay was published: the annual report of the U.S. government Coast Survey of 1879! No wonder it has been overlooked. If you know anything of the biography of Peirce, though, there is not much surprising in this odd location. Peirce was supposedly such a nut that, despite obvious brilliance, he was repeatedly blackballed from academic appointments by future colleagues around the country!

Wible claims, and I also know of no earlier such work, that this Peirce essay is the earliest mathematical work on the theory of invention. And given the intellectual history, this seems almost certain to be so. The essay was written right at the cusp of the marginal revolution and mathematical political economy, Peirce is known to have been familiar with the few scraps of earlier mathematical economics like Cournot’s famous 1838 essay, and Peirce is the father of a philosophical school for which selecting the best line of research to examine in order to learn inductively was a pressing concern. If you’ve ever read economics articles from the middle of the 19th century, this one will shock you: in style, I think it is essentially publishable today. It looks like 21st century economics. There are marginal tradeoffs. There is social science done by mathematical manipulation of heavily abstracted concepts. There is even a Marshallian diagram! It’s phenomenal. Since this looks like modern economics, let’s discuss it like modern economics; what does Peirce’s theory say?

As he introduced it, “I considered this problem. Somebody furnished a fund to be expended upon research without restrictions. What sort of researches should it be expended upon?” Essentially, there are some scientific problems which we understand only vaguely; you may think of this purely qualitatively, or as meaning something is measured to within some confidence interval. There are diminishing returns to science, so that while decreasing error can be done at linear cost, the utility gained from such reduction is concave (the inverse is quadratic in Peirce’s formulation). There is a total fixed research budget. What should be worked on first? Note that this paper was first written in 1876: there is no stochastic learning or any such thing, as the mathematics to discuss bandits and related objects was not yet developed. Learning is purely deterministic here.

Solving that constrained maximization problem gives the now-familiar, but then-nonexistent, result that we should compare ratios of MU/MC across different projects. Peirce called this ratio of marginal utility to marginal cost the “economic urgency” of a given line of research. He notes that, given that functional form assumptions, new research fields where we know very little are particularly worthwhile investments: the gains from increasing our knowledge are exponential in ignorance, whereas the cost is linear. As an example, an early chemist with simple vials is able to provide results with more social utility than a thousand chemists working in Peirce’s day with all sorts of modern equipment. Peirce also derives a result concerning sampling which is a bit opaque for modern readers given that it is couched in terms of “accidental probable error” rather than confidence intervals; nonetheless, it is very Wald-esque in that it explicitly argues that optimal sample size in experiments depends crucially on the budget, the costs of sampling and the utility of learning inferences from that sampling. Such considerations are absolutely ignored in a lot of research design even today!

http://books.google.com/books?id=ux79s_IhpFYC (Both Peirce’s original essay and Wible’s commentary appear in “Science Bought and Sold,” edited by Mirowski and Sent. The Google Books Preview is generous enough here for you to read the entirety of both essays; I do not see any other ungated copies of either online.)

“What Does it Mean to Say that Economics is Performative?,” M. Callon (2007)

With the last three posts being high mathematical-economic theory, let’s go 180 degrees and look at this recent essay – the introduction of a book, actually – by Michel Callon, one of the deans of actor-network theory (along with Bruno Latour, of course). I know what you’re thinking: a French sociologist of science who thinks objects have agency? You’re probably running away already! But stay; I promise it won’t be so bad. And as Callon mentions, sociologists of science and economic theory have a long connection: Robert K. Merton, legendary author of The Sociology of Science, is the father of Robert C. Merton, the Nobel winning economist.

The concept here is performativity in economics. An essay by William Baumol and a coauthor in the JEL tried to examine whether economic theory had made any major contributions. Of the 9 theories they studied (marginalism, Black-Scholes, etc.), only a couple could reasonably be said to be invented and disseminated by academic economists. But performativity is not so sanguine. Performativity suggests that, rather than theories being true or false, they are accepted or not accepted, and there are many roles to be played in this acceptance process by humans and non-humans alike. For example, the theory of Black-Scholes could be accepted in academia, but to be performed by a broader network, certain technologies were needed (frequent stock quotes), market participants needed to believe the theory, regulators needed to be persuaded (that, for one, options are not just gambling); this process is reflexive and the way the theory is performed feeds back into the construction of novel theories. A role exists for economists as scientists across this entire performance.

The above does not simply mean that beliefs matter, or that economic theories are “performed” as self-fulfilling prophecies. Callon again: “The notion of expression is a powerful vaccination against a reductionist interpretation of performativity; a reminder that performativity is not about creating but about making happen.” Not all potential self-fulfilling prophecies are equal: traders did in fact use Black-Scholes, but they never began to use sunspots to coordinate. Sometimes theories outside academia are performed in economics: witness financial chartism. It’s not about “truth” or “falsehood”: Callon’s school of sociology/anthropology is fundamentally agnostic.

There is an interesting link between the jargon of the actor-network theory literature and standard economics. I think you can see it in the following passage:

“In the paper world to which it belongs, marginalist analysis thrives. All it needs are some propositions on decreasing returns, the convexity of utility curves, and so forth. Transported into an electricity utility (for example Electricité de France), it needs the addition of time-ofday meters set up wherever people consume electricity and without which calculations are impossible; introduced into a private firm, it requires analytical accounting and a system of recording and cost assessment that prove to be hardly feasible. This does not mean that marginalist analysis has become false. As everyone knows, it is still true in (most) universities.”

Economists surely see a quote like the above and think, surely there is something more to this theory of performance than information economics and technological constraints. But really there isn’t. Rather, we economists generally do model why information is the way it is, or why certain agents get certain signals. A lot of this branch of sociology should be read as an investigation into how agents (including nonhumans, such as firms) get, or search for, information, particularly to the extent that such a search is reflexive to a new economic theory being proposed.

http://halshs.archives-ouvertes.fr/docs/00/09/15/96/PDF/WP_CSI_005.pdf (July 2006 working paper – final version published in McKenzie et al (Eds.), Do Economists Make Markets by Princeton Univ. Press)