Posts filed under ‘Myths and Realities’
| Dick Langlois |
Further to Peter’s post on government science funding: I just received, hot off the (physical) press, a copy of Shane Greenstein’s new book How the Internet Became Commercial (Princeton, 2015). Among the myths that Greenstein — now apparently at Harvard Business School — debunks is the idea that the internet was in any sense a product of government industrial policy. Although government had many varied and uncoordinated influences on the development of the technology, the emergence of the Internet was ultimately an example of what the economic historian Robert Allen called collective invention. It was very much a spontaneous process. And it was not fundamentally different from other episodes of technological change in history.
| Peter Klein |
This piece by Matt Ridley builds on Terence Kealey’s critique of government science funding, and also echoes Nathan Rosenberg’s critique of the linear model of science and technology. We have pointed out similarly that arguments for public science funding are usually not very scientific.
When you examine the history of innovation, you find, again and again, that scientific breakthroughs are the effect, not the cause, of technological change. It is no accident that astronomy blossomed in the wake of the age of exploration. The steam engine owed almost nothing to the science of thermodynamics, but the science of thermodynamics owed almost everything to the steam engine. The discovery of the structure of DNA depended heavily on X-ray crystallography of biological molecules, a technique developed in the wool industry to try to improve textiles.
Technological advances are driven by practical men who tinkered until they had better machines; abstract scientific rumination is the last thing they do. As Adam Smith, looking around the factories of 18th-century Scotland, reported in “The Wealth of Nations”: “A great part of the machines made use in manufactures…were originally the inventions of common workmen,” and many improvements had been made “by the ingenuity of the makers of the machines.”
It follows that there is less need for government to fund science: Industry will do this itself. Having made innovations, it will then pay for research into the principles behind them. Having invented the steam engine, it will pay for thermodynamics.
I have argued repeatedly against the “laundry list” rationale for public funding, the listing of technologies and products that came out of government programs, as if that were justification for these programs. Ridley agrees:
Given that government has funded science munificently from its huge tax take, it would be odd if it had not found out something. This tells us nothing about what would have been discovered by alternative funding arrangements.
And we can never know what discoveries were not made because government funding crowded out philanthropic and commercial funding, which might have had different priorities.
| Peter Klein |
Because we’ve been somewhat skeptical of randomized-controlled trials — not the technique itself, but the way it is over-hyped by its proponents — you may enjoy Angus Deaton’s critique of RCTs in development economics. I learned of Deaton’s arguments from this excellent piece by Chris Blattman in Foreign Policy. Here is the key paper, Deaton’s 2008 Keynes Lecture at the British Academy.
Instruments of Development: Randomization in the Tropics, and the Search for the Elusive Keys to Economic Development
There is currently much debate about the effectiveness of foreign aid and about what kind of projects can engender economic development. There is skepticism about the ability of econometric analysis to resolve these issues, or of development agencies to learn from their own experience. In response, there is movement in development economics towards the use of randomized controlled trials (RCTs) to accumulate credible knowledge of what works, without over-reliance on questionable theory or statistical methods. When RCTs are not possible, this movement advocates quasi-randomization through instrumental variable (IV) techniques or natural experiments. I argue that many of these applications are unlikely to recover quantities that are useful for policy or understanding: two key issues are the misunderstanding of exogeneity, and the handling of heterogeneity. I illustrate from the literature on aid and growth. Actual randomization faces similar problems as quasi-randomization, notwithstanding rhetoric to the contrary. I argue that experiments have no special ability to produce more credible knowledge than other methods, and that actual experiments are frequently subject to practical problems that undermine any claims to statistical or epistemic superiority. I illustrate using prominent experiments in development. As with IV methods, RCT-based evaluation of projects is unlikely to lead to scientific progress in the understanding of economic development. I welcome recent trends in development experimentation away from the evaluation of projects and towards the evaluation of theoretical mechanisms.
Blattman says Deaton has a new paper that presents a more nuanced critique, but it is apparently not online. I’ll share more when I have it.
| Peter Klein |
Justin Fox reports on a recent high-powered behavioral economics conference featuring Raj Chetty, David Laibson, Antoinette Schoar, Maya Shankar, and other important contributors to this growing research stream. But he refers also to the “Summers critique,” the idea that key findings in behavioral economics research sound like recycled wisdom from business practitioners.
Summers [in 2012] told a story about a college acquaintance who as a cruel prank signed up another classmate for 60 different subscriptions of the Book-of-the-Month-Club ilk. The way these clubs worked is that once you signed up, you got a book in the mail every month and were charged for it unless you (a) sent the book back within a certain period of time or (b) went through the hassle of extricating yourself from the club altogether. Customers had to opt out in order to not keep buying books, so they bought more books than they otherwise would have. Book marketers, Summers said, had figured out the power of defaults long before economists had.
More generally, Fox asks, “Have behavioral economists really discovered anything new, or have they simply replaced some wrong-headed notions of post-World War II economics with insights that people in business have understood for decades and maybe even centuries?”
I took exactly the Summers line in a 2010 post, observing that behavioral economics “often seems to restate common, obvious, well-known ideas as if they are really novel insights (e.g., that preferences aren’t stable and predictable over time). More novel propositions are questionable at best.” I used a Dan Ariely column on compensation policy as an example:
He claims as a unique insight of behavioral economics that when people are evaluated according to quantitative measures of performance, they tend to focus on the measures, not the underlying behavior being measured. Well, duh. This is pretty much a staple of introductory lectures on agency theory (and features prominently in Steve Kerr’s classic 1975 article). Ariely goes on to suggest that CEOs should be rewarded not on the basis of a single measure of performance, but multiple measures. Double-duh. Holmström (1979) called this the “informativeness principle” and it’s in all the standard textbooks on contract design and compensation structure (e.g., Milgrom and Roberts, Brickley et al., etc.) (Of course, agency theory also recognizes that gathering information is costly, and that additional metrics are valuable, on the margin, only if the benefits exceed the costs, a point unmentioned by Ariely.)
Maybe Larry and I should hang out.
| Peter Klein |
As Joel Mokyr notes, one of Nathan Rosenberg’s important contributions was to debunk the “linear model” in which basic science begets applied science, which begets useful technology.
Technology in his view is not the mechanical “application of science” to production; it is a field of knowledge by itself, quite different in its incentives, its modes of transmission, and its culture. It is affected by science, but in turn provides “pure research” with its instruments and much of its agenda. In many cases, [Rosenberg] noted, scientists were confronted by the fact that things they had previously declared to be impossible were actually carried out by engineers and mechanics and had to admit somewhat sheepishly that were possible after all.
The same issue is raised in Margaret Jacob’s book The First Knowledge Economy: Human Capital and the European Economy, 1750-1850 (Cambridge University Press, 2014), which “argues persuasively for the critical importance of knowledge in Europe’s economic transformation during the period from 1750 to 1850, first in Britain and then in selected parts of northern and western Europe.” In other words, as noted by Erik Hornung:
She especially focusses on the marriage between theoretical sciences and applied mechanical knowledge which helped creating many technological innovations during the Industrial Revolution. She, thus, aims at rectifying the prevalent hypothesis that technological progress resulted from tinkering of skilled but science-ignorant engineers. An impressive set of new archival sources supports her argument that English engineers were, indeed, well aware of and heavily influenced by recent advances in natural sciences.
| Peter Klein |
I just wanted to bring your attention to a PDW I am organizing for the upcoming AoM meeting, where we will engage in frank and in-depth discussions about the problems and merits of the popular notion of “entrepreneurial opportunity”. We have been fortunate to gather a collection of very strong scholars and independent thinkers as presenters and discussants in this PDW: Richard J. Arend, Dimo Dimov, Denis Grégoire, Peter G. Klein, Moren Lévesque, Saras Sarasvathy, and Matthew Wood. . This illustrious group of colleagues will make sure the deliberations do not focus on a “beauty contest” between “discovery” and “creation” views but instead reach beyond limitations of both.
I encourage you to join us for this session, and to make absolutely sure I won’t send you to the wrong place at the right time I have copied the details straight from the online program:
Title: Entrepreneurial Opportunity: The Oxygen or Phlogiston of Entrepreneurship Research? (session #365)
Date & Time: Saturday, August 08, 2015, 12:30:00 PM – 3:00:00 PM
Hotel & Room: Vancouver Convention Centre, Room 012
Further elaboration follows below. Heartily welcome!
| Peter Klein |
Jeffrey Selingo raises an important point about the distinction between “public” and “private” universities, but I disagree with his analysis and recommendation. Selingo points out that the elite private universities have huge endowments and land holdings, the income from which, because of the universities’ nonprofit status, is untaxed. He calls this an implicit subsidy, worth billions of dollars according to this study. “Such benefits account for $41,000 in hidden taxpayer subsidies per student annually, on average, at the top 10 wealthiest private universities. That’s more than three times the direct appropriations public universities in the same states as those schools get.”
I agree that the distinction between public and private universities is blurry, but not for the reasons Selingo gives. First, a tax break is not a “subsidy.” Second, there are many ways to measure the “private-ness” of an organization — not only budget, but also ownership and governance. In terms of governance, most US public universities look like crony capitalists. The University of Missouri’s Board of Curators consists of a handful of powerful local operatives, all political appointees (and all but one lawyers) and friends of the current and previous governors. At some levels, there is faculty governance, as there is at nominally private universities. In terms of budget, we don’t need to invent hidden subsidies, we need only look at the explicit ones. If we include federal research funding, the top private universities get a much larger share of their total operating budgets from government sources than do the mid-tier public research universities. (I recently read that Johns Hopkins gets 90% of its research budget from federal agencies, mostly NIH and NSF.) And of course federal student aid is relevant too.
So, what does it mean to be a “private” university?