I want to commend a fascinating post by Jim Manzi to your attention. In the post Jim considers the findings of business prof Anne Marie Knott that American companies are drastically under-spending on R&D:
Knott claims that if just the top 20 American corporations had followed her recommendations, they would have collectively increased their market capitalization by more than $1 trillion. Consider this assertion for a moment The current total market capitalization of the top 20 U.S. public companies is a little over $4 trillion. Knott claims that she has outsmarted the entire system of management teams, investors, equity analysts, hedge funds, large-scale private equity firms and everyone else who is trying to change management practices to increase share price, and knows how to increase the total value of the most-closely followed companies in the world by almost 25 percent….by building a regression model using publicly-available data.
Dr. Knott has found, for example, that Dow Chemical would have maximized its total market capitalization if it had spent an additional 10% on R&D and Apple would have maximized its total market capitalization by spending an eye-popping five times what it actually spent.
Jim correctly responds:
I’m confident that exactly the effect Knott describes is real. Some companies are better at managing research, and they will spend more, all else equal, and create greater returns for it.
But causality also runs in the opposite direction. For example, when management teams rationally foresee a good year coming, they tend to relax spending discipline. So we will see R&D spending go up in year X, and profits rising in year X+1. The expectation of future profits cause R&D spending to rise today This effect is unobserved in Knott’s model, because we have no data on executive anticipations. Some other variables will proxy for it, but the correlation between the actual unobserved variable and the proxy won’t be close to 1.
Further, there are many confounding variables. For example, different firms that are called competitors will actually face different landscapes of potential R&D opportunities, independent of R&D effectiveness. IBM and HP, as examples, have different mixes of end-use markets, different customer bases, different installed technologies and so on that means that each is looking at a different list of potential relevant projects when deciding what to fund. This changes over time. Who were Apple’s competitors in 1995? 2000? 2010? Who will be their competitors in 2015? This is referenced conceptually in Knott’s paper, but how do we segregate this effect from RQ and everything else when the model has no data on it.
However, I think that many, many companies don’t see R&D the way that Jim suggests. So, for example, in an early post here I examined the financial reports of a number of large pharmaceutical companies over a period of several years and found that R&D expenses increased, roughly, at the rate of inflation while marketing and lobbying spending increased with revenues. Or, said another way, R&D is viewed as overhead and restrained.
Dr. Knott’s findings do dovetail nicely with a recurring theme here, that R&D spending in the U. S. is far too low. Imagine, just for the sake of argument, that Dr. Knott’s findings are correct and that big companies were, in fact, behaving correspondingly. Tens or even hundreds of billion more would have been spent on R&D. That would have increased the incentives for students to pursue careers in science and technology rather than, say, finance. Or, to pick a worst-case scenario, art history.