Adam M. Guren is an associate professor of economics at Boston University.
Fed chair nominee Kevin Warsh has turned to the myth of “the Maestro” to provide an intellectually coherent reason for acceding to President Donald Trump’s demand for lower interest rates. But in arguing that he can cut rates based on anecdotal evidence, Warsh is taking the wrong lessons from Alan Greenspan’s tenure.
Greenspan’s monetary policy as computers and the internet transformed the economy has become the stuff of legend. In September 1996, unemployment had fallen to just over 5 percent — unremarkable now, but at the time, near a 20-year low — and inflation was low and falling. To the monetary policymakers on the Federal Open Market Committee and their advisers, things looked too good to last, especially with wages sharply rising. They believed that the economy was on the brink of overheating in a bout of inflation, as it had when unemployment was nearly as low in the past, and wanted to preemptively raise rates.
Greenspan bucked that consensus. While everyone else in the room saw an overheating economy, he argued that computerization was driving a surge in productivity that was both lowering inflation and raising the rate at which the economy could grow without overheating.
Even though this story was not visible in the aggregate data — one FOMC member noted that “the Chairman’s insight played to an unresponsive audience” — Greenspan had confidence in his analysis. In meeting after meeting, he convinced the committee to hold off on raising rates, allowing unemployment and inflation to fall together. In what is rightly remembered as one of the great feats in the history of central banking, Greenspan saw the productivity gains from the computer revolution years before they showed up in the statistics.
How did Greenspan do it? In Warsh’s telling, through Greenspan’s conversations with businessmen and an intuitive understanding of the economy. Like a mariner reading the swell, Greenspan “believed, based on anecdotes and rather esoteric data, that we weren’t in a position where we needed to raise rates because this technology wave was going to be structurally deflationary … as a result, we had a stronger economy.”
Trump has made clear that he chose Warsh because he sees him as an ally in his desire for lower rates, and in justifying the ability to cut, Warsh frequently turns to Greenspan. Akin to the Greenspan era, Warsh has said, artificial intelligence will drive “the most productivity-enhancing wave of our lifetimes — past, present and future,” which will push down inflation and allow the Fed room to cut. As he told tech CEO Sadi Kahn, the lesson is that “at times of huge consequence, at turning points, if you have a set of data that’s telling you one thing and a set of anecdotes that are telling you the other, listen to the anecdotes.”
“Data dependence,” Warsh said in 2016, may be among the most “dangerous words in the conduct of monetary policy.” He has argued that monetary policymakers “are going to have to make a bet” instead of “breathlessly awaiting trailing data from stale national accounts.”
This is a false and dangerous reading of the Greenspan myth. Greenspan’s edge came from his decades of working as an economic consultant, through which he developed, in the words of biographer Sebastian Mallaby, a “fine-grained and often idiosyncratic knowledge of the U.S. economy.” He drilled deeper into the data than anyone else — understanding its nuances, limitations and construction. The productivity data was “lousy,” he acknowledged, but his answer was not to rely on intuition or anecdote. Instead, as Don Kohn, Greenspan’s chief adviser at the time, has explained, “Greenspan’s hunch was backed up by digging in, digging underneath and finding things that other people hadn’t found.”
Greenspan broke up the productivity data by industry. He noticed that productivity was surging in sectors where it was better measured and appeared to be collapsing in others where measurement was more likely to be unreliable. This made no sense and pointed to mismeasurement as the answer to why the labor market was humming without inflation. It was the data — not his gravitas or his position as chair — that convinced the rest of the FOMC to keep rates steady. As Janet L. Yellen, a Fed governor at the time, put it: “Greenspan did an enormous amount of research on his own. He really tried to make the case using a lot of economic data.”
Making monetary policy bets based on anecdotes is a recipe for disaster. People systematically overweight vivid stories, and anecdotes about transformative technology are particularly tempting. But it is nearly impossible to predict when or how strongly a new technology will affect the economy. Data, even if it is noisy and incomplete, remains the best available signal.
In 1987, economist Robert Solow famously quipped that “you can see the computer age everywhere but in the productivity statistics.” Computers did not boost productivity growth until Greenspan noticed it nearly a decade later. Even then, the gains did not allow Greenspan to reduce rates but only to delay raising them for a few years.
Warsh’s anecdotes may be right, but they may also be wrong — and betting monetary policy on his intuition could be catastrophic. To follow in Greenspan’s footsteps, Warsh should not ignore the data but instead put in the work to learn its secrets.
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