Archive for October, 2015
| 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 |
Angus Deaton has won the 2015 Nobel Prize for his work on measuring consumption and inequality. You can find lots of discussion in the usual places; Lynne has a nice summary here. I don’t know Deaton’s work well but he has been on the unofficial short list for the last several years and his work is important and influential for economic growth and development, poverty and health, and related areas.
I can’t help poking a little fun at the economics profession, however. You may have heard the joke that economists used to win the Nobel prize for explaining to the general public something that previously only economists understood, but now they win it for explaining to their fellow economists something that the general public has always known, e.g.:
- Politicians care about themselves (Buchanan).
- Don’t put all your eggs in one basket (Markowitz, Miller,and Sharpe).
- You can’t fool all of the people all of the time (Lucas).
- Some people know more than others (Akerlof, Spence, Stiglitz).
Deaton’s major insight: aggregate measures of consumption and inequality conceal important differences among individuals.