A year ago, economists were carefully drawing comparisons between the current American economy and the mid-1990s, when a surprise burst of productivity growth allowed the economy to run hot without igniting inflation. They were hopeful but hedging their bets.
They may be done hedging.
When the dust settled, the annualized productivity growth rate for the entire current business cycle — dating from the fourth quarter of 2019 — was revised up from 2.0 percent to 2.2 percent. That matches the long-run average going back to 1947, and far exceeds the 1.5 percent rate of the sluggish 2007-to-2019 cycle that spawned a decade of productivity pessimism.
“Are we finally seeing AI in the productivity data?” asked Jason Furman, a Harvard economist and former chairman of the Council of Economic Advisers under President Obama, on Thursday. He noted that productivity is now running 2.2 percent above the Congressional Budget Office’s pre-pandemic forecast, meaning the economy has not merely recovered lost ground but surpassed where it was supposed to be on the old trajectory. Furman, who had been skeptical of the productivity gains, acknowledged he had been wrong.
“The Productivity Thing”
Back in the late 1990s, Federal Reserve officials had a name for the phenomenon that was confounding their models. They called it “The Productivity Thing” — a mix of awe and uncertainty about forces they could observe but not fully explain.
What made the late-cycle productivity surge so striking then — as now — was that it was not supposed to happen. Conventional economic theory held that productivity growth slows in the late stages of an expansion, as the easiest efficiency gains have already been captured. Eight years into the 1990s expansion, productivity instead jumped 4.6 percent in the fourth quarter of 1998. Fed officials marveled at it publicly. We are in a similar position today.
The 1990s boom had a clear catalyst: computers and the early internet, which had been embedded in workplaces for years without generating measurable efficiency gains — what the economist Robert Solow captured in his famous 1987 observation that “you can see the computer age everywhere but in the productivity statistics.” Around 1994, the gains arrived. Semiconductor manufacturing improved, businesses that had spent years learning how to use information technology began reaping the rewards, and productivity boomed for the better part of a decade.
Today’s equivalent is artificial intelligence. Like computers in the late 1980s, AI tools have been widely available for several years without generating obvious gains in aggregate productivity data. The question economists have been asking — and that the new data is beginning to answer — is whether we are now at the 1994 moment: the point where adoption becomes broad enough and deep enough to show up in the numbers.
Tariffs and Immigration Restrictions Pushing Productivity Up
Here is where the current story diverges from the 1990s script in an unexpected way.
Critics of the Trump administration’s trade and immigration policies predicted that tariffs would make the economy less efficient, distorting resource allocation and raising costs. Tighter immigration enforcement, the argument went, would reduce the labor supply and slow growth. The productivity data, so far, tells the opposite story.
Alan Greenspan identified a version of this dynamic during the inflation comedown of the early 1990s, when companies losing pricing power were compelled to cut costs. “Of necessity we will tend to get an increase in productivity because it is being forced on the system,” he theorized at a Federal Reserve meeting. The mechanism today is different — it is input costs rather than lost pricing power doing the forcing — but the logic is the same.
This is not the story most economists told when the policies were implemented. It may, however, be the story the data is telling now.
The Fed May Have To Abandon Its Fear of Rapid Growth
If the productivity boom is real and durable, it has significant implications for the Federal Reserve.
A more productive economy can grow faster without generating inflation. It means that strong nominal growth, rather than being a warning sign of overheating, may simply reflect a higher speed limit on the economy. And it creates room for faster wage growth without pushing up prices.
That is precisely the situation Greenspan navigated in the late 1990s. After raising interest rates aggressively in 1994 and 1995 to preempt inflation — taking enormous political heat for doing so — Greenspan became convinced by the mid-1990s that the productivity data meant the economy could sustain faster growth without sparking price increases. He held off on further tightening and was vindicated. Fed Chair Jerome Powell has spoken admiringly of Greenspan’s “fortitude” in navigating that period.
There are caveats. Productivity data is notoriously noisy and heavily revised, a fact this very report illustrates, since the upward revisions are themselves part of the story. Skeptics have argued that AI’s gains may be narrower than computers’ were, concentrated in office work rather than sweeping across the whole economy. And as Kelley cautioned in 1999, even in a strong new era, “there are limits out there somewhere.”
But it took until 1999 for economists to broadly accept that the 1990s productivity boom was real, even though it had been underway for years. The current boom has been building just as quietly — and the data keeps coming in strong, and keeps getting revised stronger. At some point, caution becomes its own form of error.
About the Author
Discover more from USA NEWS
Subscribe to get the latest posts sent to your email.
