Posts filed under ‘Myths and Realities’
| Peter Klein |
The old Keynesian idea that war is good for the economy is not taken seriously by anyone outside the New York Times op-ed page. But much of the discussion still focuses on macroeconomic effects (on aggregate demand, labor-force mobilization, etc.). The more important effects, as we’ve often discussed on these pages, are microeconomic — namely, resources are reallocated from higher-valued, civilian and commercial uses, to lower-valued, military and governmental uses. There are huge distortions to capital, labor, and product markets, and even technological innovation — often seen as a benefit of wars, hot and cold — is hampered.
A new NBER paper by Zorina Khan looks carefully at the microeconomic effects of the US Civil War and finds substantial resource misallocation. Perhaps the most significant finding relates to entrepreneurial opportunity — individuals who would otherwise create significant economic value through establishing and running firms, developing new products and services, and otherwise improving the quality of life are instead motivated to pursue government military contracts (a point emphasized in the materials linked above). Here is the abstract (I don’t see an ungated version, but please share in the comments if you find one):
The Impact of War on Resource Allocation: ‘Creative Destruction’ and the American Civil War
B. Zorina Khan
NBER Working Paper No. 20944, February 2015
What is the effect of wars on industrialization, technology and commercial activity? In economic terms, such events as wars comprise a large exogenous shock to labor and capital markets, aggregate demand, the distribution of expenditures, and the rate and direction of technological innovation. In addition, if private individuals are extremely responsive to changes in incentives, wars can effect substantial changes in the allocation of resources, even within a decentralized structure with little federal control and a low rate of labor participation in the military. This paper examines war-time resource reallocation in terms of occupation, geographical mobility, and the commercialization of inventions during the American Civil War. The empirical evidence shows the war resulted in a significant temporary misallocation of resources, by reducing geographical mobility, and by creating incentives for individuals with high opportunity cost to switch into the market for military technologies, while decreasing financial returns to inventors. However, the end of armed conflict led to a rapid period of catching up, suggesting that the war did not lead to a permanent misallocation of inputs, and did not long inhibit the capacity for future technological progress.
| Peter Klein |
We’ve addressed the widely held, but largely mistaken, view of creative artists and entrepreneurs as auteurs, isolated and misunderstood, fighting the establishment and bucking the conventional wisdom. In the more typical case, the creative genius is part of a collaborative team and takes full advantage of the division of labor. After all, is our ability to cooperate through voluntary exchange, in line with comparative advantage, that distinguishes us from the animals.
Christian Caryl’s New Yorker review of The Imitation Game makes a similar point about Alan Turing. The film’s portrayal of Turing (played by Benedict Cumberbatch) “conforms to the familiar stereotype of the otherworldly nerd: he’s the kind of guy who doesn’t even understand an invitation to lunch. This places him at odds not only with the other codebreakers in his unit, but also, equally predictably, positions him as a natural rebel.” In fact, Turing was funny and could be quite charming, and got along well with his colleagues and supervisors.
As Caryl points out, these distortions
point to a much broader and deeply regrettable pattern. [Director] Tyldum and [writer] Moore are determined to suggest maximum dramatic tension between their tragic outsider and a blinkered society. (“You will never understand the importance of what I am creating here,” [Turing] wails when Denniston’s minions try to destroy his machine.) But this not only fatally miscasts Turing as a character—it also completely destroys any coherent telling of what he and his colleagues were trying to do.
In reality, Turing was an entirely willing participant in a collective enterprise that featured a host of other outstanding intellects who happily coexisted to extraordinary effect. The actual Denniston, for example, was an experienced cryptanalyst and was among those who, in 1939, debriefed the three Polish experts who had already spent years figuring out how to attack the Enigma, the state-of-the-art cipher machine the German military used for virtually all of their communications. It was their work that provided the template for the machines Turing would later create to revolutionize the British signals intelligence effort. So Turing and his colleagues were encouraged in their work by a military leadership that actually had a pretty sound understanding of cryptological principles and operational security. . . .
The movie version, in short, represents a bizarre departure from the historical record. In fact, Bletchley Park—and not only Turing’s legendary Hut 8—was doing productive work from the very beginning of the war. Within a few years its motley assortment of codebreakers, linguists, stenographers, and communications experts were operating on a near-industrial scale. By the end of the war there were some 9,000 people working on the project, processing thousands of intercepts per day.
The rebel outsider makes for good storytelling, but in most human endeavors, including science, art, and entrepreneurship, it is well-organized groups, not auteurs, who make the biggest breakthroughs.
| Peter Klein |
Via Michael Strong, a thoughtful review and critique of Western-style economic development programs and their focus on one-size-fits-all, “big idea” approaches. Writing in the New Republic, Michael Hobbs takes on not only Bono and Jeff Sachs and USAID and the usual suspects, but even the randomized-controlled-trials crowd, or “randomistas,” like Duflo and Banerjee. Instead of searching for the big idea, thinking that “once we identify the correct one, we can simply unfurl it on the entire developing world like a picnic blanket,” we should support local, incremental, experimental, attempts to improve social and economic well being — a Hayekian bottom-up approach.
We all understand that every ecosystem, each forest floor or coral reef, is the result of millions of interactions between its constituent parts, a balance of all the aggregated adaptations of plants and animals to their climate and each other. Adding a non-native species, or removing one that has always been there, changes these relationships in ways that are too intertwined and complicated to predict. . . .
[I]nternational development is just such an invasive species. Why Dertu doesn’t have a vaccination clinic, why Kenyan schoolkids can’t read, it’s a combination of culture, politics, history, laws, infrastructure, individuals—all of a society’s component parts, their harmony and their discord, working as one organism. Introducing something foreign into that system—millions in donor cash, dozens of trained personnel and equipment, U.N. Land Rovers—causes it to adapt in ways you can’t predict.
| Peter Klein |
In the opportunity-discovery perspective, profits result from the discovery and exploitation of disequilibrium “gaps” in the market. To earn profits an entrepreneur needs superior foresight or perception, but not risk capital or other productive assets. Capital is freely available from capitalists, who supply funds as requested by entrepreneurs but otherwise play a relatively minor, passive role. Residual decision and control rights are second-order phenomena, because the essence of entrepreneurship is alertness, not investing resources under uncertainty.
By contrast, the judgment-based view places capital, ownership, and uncertainty front and center. The essence of entrepreneurship is not ideation or imagination or creativity, but the constant combining and recombining of productive assets under uncertainty, in pursuit of profits. The entrepreneur is thus also a capitalist, and the capitalist is an entrepreneur. We can even imagine the alert individual — the entrepreneur of discovery theory — as a sort of consultant, bringing ideas to the entrepreneur-capitalist, who decides whether or not to act.
A scene from Fargo nicely illustrates the distinction. Protagonist Jerry Lundegaard thinks he’s found (“discovered”) a sure-fire profit opportunity; he just needs capital, which he hopes to get from his wealthy father-in-law Wade. Jerry sees himself as running the show and earning the profits. Wade, however, has other ideas — he thinks he’s making the investment and, if it pays off, pocketing the profits, paying Jerry a finder’s fee for bringing him the idea.
So, I ask you, who is the entrepreneur, Jerry or Wade?
Peter invited me to reply to [Warren Miller’s] comment, so I’ll try to offer a defense of formal economic modeling.
In answering Peter’s invitation, I’m at a bit of a disadvantage because I am definitely NOT an IO economist (perhaps because I actually CAN relax). Rather, I’m a strategy guy — far more interested in studying the private welfare of firms than the public welfare of economies (plus, it pays better and is more fun). So, I am in a much better position to comment on the benefits that the game-theoretic toolbox is currently starting to bring to the strategy field than on the benefits that it has brought to the economics discipline over the last four decades (i.e., since Akerlof’s 1970 Lemons paper really jump-started the trend).
Peter writes, “game theory was supposed to add transparency and ‘rigor’ to the analysis.” I have heard this argument many times (e.g., Adner et al, 2009 AMR), and I think it is a red herring, or at least a side show. Yes, formal modeling does add transparency and rigor, but that’s not its main benefit. If the only benefit of formal modeling were simply about improving transparency and rigor then I suspect that it would never have achieved much influence at all. Formal modeling, like any research tool or method, is best judged according to the degree of insight — not the degree of precision — that it brings to the field.
I can’t think of any empirical researcher who has gained fame merely by finding techniques to reduce the amount of noise in the estimate of a regression parameter that has already been the subject of other previous studies. Only if that improved estimation technique generates results that are dramatically different from previous results (or from expected results) would the improved precision of the estimate matter much — i.e., only if the improved precision led to a valuable new insight. In that case, it would really be the insight that mattered, not the precision. The impact of empirical work is proportionate to its degree of new insight, not to its degree of precision. The excruciatingly unsophisticated empirical methods in Ned Bowman’s highly influential “Risk-Return Paradox” and “Risk-Seeking by Troubled Firms” papers provide a great example of this point.
The same general principle is true of theoretical work as well. I can’t think of any formal modeler who has gained fame merely by sharpening the precision of an existing verbal theory. Such minor contributions, if they get published at all, are barely noticed and quickly forgotten. A formal model only has real impact when it generates some valuable new insight. As with empirics, the insight is what really matters, not the precision. (more…)
| Peter Klein |
As a second-year economics PhD student I took the field sequence in industrial organization. The primary text in the fall course was Jean Tirole’s Theory of Industrial Organization, then just a year old. I found it a difficult book — a detailed overview of the “new,” game-theoretic IO, featuring straightforward explanations and numerous insights and useful observations but shot through with brash, unsubstantiated assumptions and written in an extremely terse, almost smug style that rubbed me the wrong way. After all, game theory was supposed to add transparency and “rigor” to the analysis, bringing to light the hidden assumptions of the old-fashioned, verbal models, but Tirole combined math and ad hoc verbal asides in equal measure. (Sample statement: “The Coase theorem (1960) asserts that an optimal allocation of resources can always be achieved through market forces, irrespective of the legal liability assignment, if information is perfect and transactions are costless.” And then: “We conclude that the Coase theorem is unlikely to apply here and that selective government intervention may be desirable.”) Well, that’s the way formal theorists write and, if you know the code and read wisely, you can gain insight into how these economists think about things. Is it the best way to learn about real markets and real competition? Tirole takes it as self-evident that MIT-style theory is a huge advance over the earlier IO literature, which he characterizes as “the old oral tradition of behavioral stories.” He does not, to my knowledge, deal with the “new learning” of the 1960s and 1970s, associated mainly with Chicago economists (but also Austrian and public choice economists) that emphasized informational and incentive problems of regulators as well as firms.
Tirole is one of the most important economists in modern theoretical IO, public economics, regulation, and corporate finance, and it’s no surprise that the Nobel committee honored him with today’s prize. The Nobel PR team struggled to summarize his contributions for the nonspecialist reader (settling on the silly phrase that his work shows how to “tame” big firms) but you can find decent summaries in the usual places (e.g., WSJ, NYT, Economist) and sympathetic, even hagiographic treatments in the blogosphere (Cowen, Gans). By all accounts Tirole is a nice guy and an excellent teacher, as well as the first French economics laureate since Maurice Allais, so bully for him.
I do think Tirole-style IO is an improvement over the old structure-conduct-performance paradigm, which focused on simple correlations, rather than causal explanations and eschewed comparative institutional analysis, modeling regulators as omniscient, benevolent dictators. The newer approach starts with agency theory and information theory — e.g., modeling regulators as imperfectly informed principals and regulated firms as agents whose actions might differ from those preferred by their principals — and thus draws attention to underlying mechanisms, differences in incentives and information, dynamic interaction, and so on. However, the newer approach ultimately rests on the old market structure / market power analysis in which monopoly is defined as the short-term ability to set price above marginal cost, consumer welfare is measured as the area under the static demand curve, and so on. It’s neoclassical monopoly and competition theory on steroids, and hence side-steps the interesting objections raised by the Austrians and UCLA price theorists. In other words, the new IO focuses on more complex interactions while still eschewing comparative institutional analysis and modeling regulators as benevolent, albeit imperfectly informed, “social planners.”
As a student I found Tirole’s analysis extremely abstract, with little attention to how these theories might work in practice. Even Tirole’s later book with Jean-Jacques Laffont, A Theory of Incentives in Procurement and Regulation, is not very applied. But evidently Tirole has played a large personal and professional role in training and advising European regulatory bodies, so his work seems to have had a substantial impact on policy. (See, however, Sam Peltzman’s unflattering review of the 1989 Handbook of Industrial Organization, which complains that game-theoretic IO seems more about solving clever puzzles than understanding real markets.)
| Peter Klein |
That’s the conclusion of a new NBER paper by Andy Young, Matthew Higgins, Don Lacombe, and Briana Sell, “The Direct and Indirect Effects of Small Business Administration Lending on Growth: Evidence from U.S. County-Level Data” (ungated version here). “We find evidence that a county’s SBA lending per capita is associated with direct negative effects on its income growth. We also find evidence of indirect negative effects on the growth rates of neighboring counties. Overall, a 10% increase in SBA loans per capita is associated with a cumulative decrease in income growth rates of about 2%.” As the authors point out, SBA loans represent funds that also have alternative uses, and SBA-sponsored clients may not be the most worthy recipients (in terms of generating economic growth).
The results are largely robust and, perhaps more importantly, we never find any evidence of positive growth effects associated with SBA lending. Even when the estimated effects are statistically insignificant, the point estimates are always negative. Our findings suggest that SBA lending to small businesses comes at the cost of loans that would have otherwise been made to more profitable and/or innovative firms. Furthermore, SBA lending in a given county results in negative spillover effects on income growth in neighboring counties. Given the popularity of pro-small business policies, our findings should give reason for policymakers and their constituents to reevaluate their priors.