It didn’t generate a lot of attention, but Federal Reserve Chairman Jerome Powell was asked at a press conference last month about “cutbacks” in federal economic statistics collection, and his response struck me as interesting.
“Having really good data … doesn’t just help the Fed; it helps the government; it helps Congress; it helps the executive branch,” Powell explained. “More importantly, really, it helps businesses. They need to know what’s going on in the economy. The United States has been a leader for many, many years in this whole project of measuring and understanding what’s happening in our very large and dynamic economy — and I hate to see us cutting back on that, because it is a real benefit to the general public.”
These unscripted comments came to mind late last week when the new monthly job numbers came out — over the first six months of 2025, U.S. job growth has slowed to a 15-year low — and I heard from some readers who asked variations on the same question: Can we trust economic data from the Trump administration? In an era in which traditionally apolitical agencies have become overtly partisan, aren’t there reasons to be skeptical about the accuracy of federal statistics, which could be manipulated to advance the White House’s political agenda?
Broadly speaking, there are two angles to this worth keeping in mind.
The first relates to concerns about corruption and political mischief. Perhaps Donald Trump, the argument goes, might use his influence to tell the Labor Department to manipulate the data and deceive the public. To date, there have been instances in which the release of embarrassing government statistics was delayed, but there’s been no evidence of statistics being altered to fit a political narrative (though if you’re a government official who’s received such pressure, I sincerely hope you’ll reach out to us and take advantage of one of the many secure ways to communicate with the show).
The second, however, relates to the process through which data is collected. The New York Times reported:








