My problem is that I believe in the slow diffusion of technology, the importance of incremental improvements, the usefulness of the incentives provided by the fact that it is easy to make a lot of money by figuring out a cheaper way to produce and supply things that people are willing to pay a lot of money for, and the law of large numbers. These make me think that–modulus the business cycle and measurement error–total factor productivity should be smooth in the level and smooth in the growth rate as well: whatever processes were going on last year that led to invention, innovation, deployment, and thus higher productivity in a potential-output sense ought to be almost as strong or only a little stronger this year.
The entire post is interesting. I think that the view that there are no sudden economic regime changes is difficult to shake. Probably my best argument against a post-2003 productivity slowdown is that we are seeing the continued expansion of education and health care, two sectors where there is essentially no reasonable way to measure productivity to begin with. Also, quality-adjustment in the goods sector is getting harder to measure, because goods tend to overlap with services (is Amazon Kindle really mostly a good, as opposed to a service)>
Pointer from Tyler Cowen.
Measurement error dominates, not just mechanically (are the figures accurate and precise?) but conceptually as well (do the figures represent what they purport to?).
Consider labor productivity, conventionally measured as Output Produced / Hours Worked. First, we’re measuring a real phenomenon via a proxy ratio, having convinced ourselves that LP = Output / Hours is an identity (it’s not). So whenever LP moves it *must* be because Output or Hours moved; we haven’t measured anything directly. Why does LP go up during a recession? It’s obvious: marginal workers get laid off. This result is implicit in the way we’ve defined the measure so I don’t understand how it surprises anyone. Second, what makes us confident we’ve measured either of the components of the ratio accurately? I don’t even know how many hours *I* work and I’m the one working them! My employer doesn’t keep track either since I’m salaried. If economists are assuming that salaried = 40 hours/week, all I can say is they’re being laughably naive. So that gives us a second order of hidden movement in the figures.
Brad also fails to account for the fact that we live in a Garrett Jones economy. Knowledge workers do not produce output, they produce organizational capital. The firm as a whole produces output but attributing proportions of that output to individual workers is a conceptual error. Furthermore the quantity and value of that output can fluctuate wildly, driven more by extrinsic factors (what is this output worth to other firms and consumers? are there social trends or network effects in play?) than intrinsic factors (are workers or management more “efficient”? have new capital goods been invented or bought?).
“Perhaps it is simply that I spend too much time down in Silicon Valley and so cannot believe that the fervor of invention and innovation that I see there does not have large positive macroeconomic consequences.”
Brad should know that much of what goes on in Silicon Valley is value-destroying. The vast majority of venture-funded startups fail. A company that burns through 10 or 20 million dollars and then goes up in smoke because they couldn’t figure out how to cover their costs doesn’t “have large positive macroeconomic consequences”.
I can’t help but think that this kind of lazy thinking on productivity arises from the fact that most academic economists have never held a real job.
I disagree that VC batting averages being lower than you’d like imply net value destruction. Yes, entrepreneurship is messy, and the count of entrepreneurial failures overwhelms the success count. These facts don’t refute that SV characteristics, practice, and economic activity are overwhelmingly beneficial on DeLong’s terms.