Syverson on productivity mismeasurement and slowdown, plus effect of AI on the slowdown

Interview with Chad Syverson. Aaron Steelman. Federal Reserve Bank of Richmond, June 2018, https://www.blogger.com/blogger.g?blogID=2852546339326384272#editor/target=post;postID=3293709683462798846

EF: Some have argued that the productivity slowdown since the mid-2000s is due to mismeasurement issues — that some productivity growth hasn't been or isn't being captured. What does your work tell us about that?

Syverson: It tells us that the mismeasurement story, while plausible on its face, falls apart when examined. If productivity growth had actually been 1.5 percent greater than it has been measured since the mid-2000s, U.S. gross domestic product (GDP) would be conservatively $4 trillion higher than it is, or about $12,000 more per capita. So if you go with the mismeasurement story, that's the sort of number you're talking about and there are several reasons to believe you can't account for it.

First, the productivity slowdown has happened all over world. When you look at the 30 Organization for Economic Co-operation and Development countries we have data for, there's no relationship between the size of the measured slowdown and how important IT-related goods — which most people think are the primary source of mismeasurement — are to a country's economy.

Second, people have tried to measure the value of IT-related goods. The largest estimate is about $900 billion in the United States. That doesn't get you even a quarter of the way toward that $4 trillion.

Third, the value added of the IT-related sector has grown by about $750 billion, adjusting for inflation, since the mid-2000s. The mismeasure­ment hypothesis says that there are $4 trillion missing on top of that. So the question is: Do we think we're only getting $1 out of every $6 of activity there? That's a lot of mismeasurement.

Finally, there's the difference between gross domestic income (GDI) and GDP. GDI has been higher than GDP on average since the slowdown started, which would suggest that there's income, about $1 trillion cumulatively, that is not showing up in expenditures. But the problem is that was also true before the slowdown started. GDI was higher than GDP from 1998 through 2004, a period of relatively high-productivity growth. Moreover, the growth in income is coming from capital income, not wage income. That doesn't comport with the story some people are trying to tell, which is that companies are making stuff, they're paying their workers to produce it, but then they're effectively giving it away for free instead of selling it. But we know that they're actually making profits. We might not pay directly for a lot of IT services every time we use them, but we are paying for them indirectly.

As sensible as the mismeasurement hypothesis might sound on its face, when you add up everything, it just doesn't pass the stricter test you would want it to survive.

EF: Would you consider artificial intelligence (AI) a general-purpose technology? If so, how do you assess the view that the returns on investment in AI have been disappointing?

Syverson: It's way too early. There are two things creat­ing this lag for AI. First, aggregate AI capital right now is essentially zero. This stuff is really just starting to be used in production. A lot of it is simply experimental at this point. Second, a lot of it has to do with complementarity. People have to figure out what sorts of things AI can aug­ment, and we're not anywhere down that road yet.

Erik, Daniel, and I are going out on a limb a little bit by saying this, but we think AI checks the boxes for a general-purpose technology. And it seems that with some fairly modest applications of AI, the produc­tivity slowdown goes away. Two applications that we look at in our paper are autonomous vehicles and call centers.

About 3.5 million people in the United States make their living as motor vehicle operators. We think maybe 2 million of those could be replaced by autonomous vehi­cles. There are 122 million people in private employment now, so just a quick calculation says that's an additional boost of 1.7 percent in labor productivity. But that's not going to happen overnight. If it happens over a decade, that's 0.17 percent per year.

About 2 million people work in call centers. Plausibly, 60 percent of those jobs could be replaced by AI. So when you do the same kind of calculation, that's an additional 1 percent increase in labor productivity; spread out over a decade, it's 0.1 percent per year. So, from those two applica­tions alone, that's about a quarter of a percent annual accel­eration for a decade. So you only need maybe six to eight more applications of that size and the slowdown is gone.