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Technological innovation drives productivity which drives economic growth and rising living standards. And the more rapid the increases in the capability of computing equipment, the more rapid the decline in the price of that equipment, given a fixed capability. An average computer today sells for about what it did a decade ago, say, $1000. But today’s version is way more powerful. An average 2003-era computer would sell for far less today. And during the 1990s, computer prices, adjusted for quality, fell sharply.
So here’s the problem: Prices for information technology equipment are declining at the slowest pace in over a generation. And to the economic team at JPMorgan, this suggests the pace of technological advance is also slowing. If they’re right, this phenomenon would have a big impact on the US economy and workers.
Two charts from the bank’s new report, “Is I.T. Over?,” display the slowing pace of price declines:
Economist Michael Feroli:
Gains in information technology are routinely credited with the strong growth in the supply side of the US economy in the 1995-2005 period. If that technological growth is slowing—as indicated by the earlier observation on tech prices—then this could have quite significant implications for the US economy’s potential growth rate.
Note that this is not an exercise in futurology. Northwestern University economist Robert Gordon’s recent claim that US economic growth is over has attracted a fair bit of attention. This note, however, does not speculate on whether growth in information technology has reached some natural limit, or whether further revolutionary advances are coming. Instead, by looking at tech prices—and incorporating some economic reasoning—we can infer what is currently occurring on the tech frontier.
While we are not qualified to speculate on the future of technological change, we do observe that the growth in tech prices is positively serially correlated, which is another way of saying the pace of innovation in the near future is likely to resemble that in the recent past.
The downside here is that a slowdown in price declines has been accompanied by a slowdown in tech investment. And less tech investment, Feroli explains, “means less capital deepening, which could help explain why productivity growth has been soft in recent years.”
But there might be an upside to all this, he argues. Slower gains in technology and productivity, at least for a bit, might make it easier to absorb workers — labor in place of capital — back into the economy.
Then there’s the income inequality issue. To the extent that its increase over the past few decades has been driven by the increased return to high-skilled, highly educated workers, “then workforce skills may be better able to catch up with the level of technology. That, combined with the rise in college enrollment, “suggests the march toward increasing income inequality could soon reverse itself.”
Perhaps. But if Feroli’s version of “the great stagnation” argument is correct, I would rather increase opportunity and absolute incomes by better educating workers and creating more entrepreneurs to supply the next wave of innovation. More smart research investment by government, too. Or it could be this data is not accurately capturing either ongoing IT innovation and its diffusion throughout the economy. Indeed, perhaps the third industrial revolution has only just begun. As Kevin Kelly wrote in Wired recently:
Right now it seems unthinkable: We can’t imagine a bot that can assemble a stack of ingredients into a gift or manufacture spare parts for our lawn mower or fabricate materials for our new kitchen. We can’t imagine our nephews and nieces running a dozen workbots in their garage, churning out inverters for their friend’s electric-vehicle startup. We can’t imagine our children becoming appliance designers, making custom batches of liquid-nitrogen dessert machines to sell to the millionaires in China. But that’s what personal robot automation will enable.
Everyone will have access to a personal robot, but simply owning one will not guarantee success. Rather, success will go to those who innovate in the organization, optimization, and customization of the process of getting work done with bots and machines. Geographical clusters of production will matter, not for any differential in labor costs but because of the differential in human expertise. It’s human-robot symbiosis. Our human assignment will be to keep making jobs for robots—and that is a task that will never be finished. So we will always have at least that one “job.”
Is the tech revolution over? Maybe. Or maybe it is just paused. Maybe not even that. But we should act as if it has slowed and take whatever policy steps we can to hit the gas pedal.
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