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The above chart actually understates how lousy official US productivity numbers have been in recent years. Since the Great Recession, productivity has pretty much flatlined, up just 0.5% annually.
But how do you sync those numbers with what’s been happening in Silicon Valley? One theory is that we’ve been mismeasuring the digital economy and thus overall productivity and economic growth. I’ve written much about the version of this argument coming from Goldman Sachs. Yet respected new research from both the San Francisco Fed and the University of Chicago make a strong case that mismeasurement cannot explain the productivity slowdown.
But there’s more to this story, as presented in the paper “Prices of high-tech products, mismeasurement, and pace of innovation” by AEI’s Stephen Oliner, the Federal Reserve’s David Byrne, and Daniel Sichel of Wellesley College. From the paper (bold is mine):
Nevertheless, available evidence points to considerable mismeasurement of high-tech prices, and this mismeasurement does have important implications. In particular, the evidence that prices of high-tech products are falling more rapidly than is reflected in official statistics implies a reallocation of MFP growth across sectors, with faster growth rates of MFP in high-tech sectors and slower growth rates elsewhere in the economy.
Macroeconomists often use growth rates of MFP as proxies for the pace of innovation so the faster rates of MFP growth in the high-tech sector indicate that rates of innovation in the digital economy have been more rapid than implied by official price measures.
We believe that these faster implied rates of innovation in the tech sector are important for three reasons. First, these results deepen the productivity paradox. If the pace of innovation in the tech sector has been more rapid than implied by official data, then it is perhaps even more of a puzzle that productivity growth has remained so weak. Second, as a rhetorical point, we believe that the sluggish rates of high-tech MFP growth implied by official price measures have improperly supported darker narratives about future prospects for productivity growth. The apparent weak pace of innovation in the tech sector provides fuel for the story that little scope remains for the tech sector to boost aggregate labor productivity growth. Third, we believe that these faster rates of growth in high-tech could presage a second wave of higher productivity growth spurred by the digital revolution.
So, first, the pace of innovation may be faster than official stats show. Second, there is also reason to believe that business investment is also undermeasured somewhat since official measures miss some some sorts of intangible capital, as a new Peterson Institute study notes, including nonscientific product development, brand equity, training, and organizational capital. (I also wrote about this study yesterday.)
So why then is productivity growth so weak if innovation and business investment are stronger than we think? From the Peterson paper, which is also coauthored by Sichel (bold is mine):
History suggests that the macrolevel productivity effects of innovation and investment in new technology often take time to emerge. The basic technologies needed to electrify the manufacturing sector in the United States were in place by 1890, for example, but it took decades before they diffused through the economy as firms learned to use them effectively (David 1990). When the measurable impact of all this investment on productivity finally arrived, it appears to have come in waves rather than in one period of uniformly rapid productivity growth, according to Syverson (2013).
A similar pattern emerged for the digital revolution. In 1987, Robert Solow famously quipped, “We can see computers everywhere but in the productivity statistics.” Just a few years later, this “Solow paradox” had been resolved by a pronounced productivity acceleration—but that acceleration arrived long after computers had become commonplace. Brynjolfsson, Hitt, and Yang (2002) document significant coinvestments in software and skill building that were necessary to realize the benefits of investments in computer hardware. Indeed, they find that firms spent significantly more on these associated coinvestments than on computer hardware itself and argue that it took considerable time for these coinvestments to be made.
It was therefore not surprising that the productivity benefits of the IT revolution arrived long after the fundamental underlying technologies were developed and initially commercialized. In the same way, the rapid innovation and robust investment of recent years will eventually have an impact, but it could take some time for the next wave of productivity growth to become visible at the aggregate level.
And let me quickly add that a more dynamic and competitive economy would help the diffusion of these technologies more broadly. And indeed the Oliner-Byrne-Sichel paper does nod toward this possibility.
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