By David Enna, Tipswatch.com
One of the most frequent questions I’ve heard from readers over the last year goes something like this: “Can we trust the government inflation numbers?” Or more bluntly: “Is the U.S. government lying to us?”
My usual answers are: “Maybe” on trust and “probably not” on outright lying.
This sense of fear and doubt erupted on Aug. 1, 2025, when President Trump fired the head of the Bureau of Labor Statistics, Erika McEntarfer, and accused her of “faking” jobs numbers. The firing came immediately after the release of the July 2025 jobs report, which came in lower than expected. But the bigger issue was a huge downward revision in jobs numbers for May and June, seemingly in the wake of April’s tariff-setting “Liberation Day.”
I’ve talked to former officials at the BLS who say they are absolutely confident that these longtime, nonpartisan statisticians would not agree to “cook the books” for any administration. But they also point to severe staff and funding cuts at the BLS, which could result in potential inaccuracies.

One person I trust on economic issues is Claudia Sahm, a former Federal Reserve official who created the “Sahm Rule,” a reliable indicator of recession based on employment data. Sahm, now chief economist at New Century Advisors, is known for being a “bit” outspoken, a rare trait for a macroeconomist.
Sahm writes on economic data on her Substack site, Stay-At-Home Macro, which conveniently abbreviates to her name, SAHM. The site is free to read and I highly recommend it. You can also follow her on X at @Claudia_Sahm, where she freely expresses her opinions.
Sahm wrote a Feb. 20 Substack post with the title “Trust in Numbers,” where she analyzed the U.S. government’s credibility issues. I am going to highlight some of her thoughts, but I encourage you to read her entire post.
There is currently no evidence of manipulation. Even so, it is appropriate to monitor for signs of it.
Sahm noted that fired BLS director McEntarfer rejected the idea of political interference in a recent interview, saying, “You should still trust BLS data. The agency is being run by the same dedicated career staff who were running it while I was awaiting confirmation from the Senate. And the staff have made it clear that they are blowing a loud whistle if there is interference.”
However, the U.S. government shutdown in October 2025 cut off data collection for an entire month and created nearly impossible barriers to accuracy. Sahm noted that the BLS was forced to rely on long-documented methods to estimate inflation:
The BLS chose procedural consistency over discretionary adjustment. That choice matters because the CPI is more than a statistic. It is written into law and contracts, such as those that adjust Social Security benefits, Treasury Inflation-Protected Securities (TIPS), and countless private agreements. …
Safeguarding against manipulation is necessary, but not sufficient for data to be trustworthy. Even when the process is sound, trust can erode if people feel the statistics do not align with their lived experience.
Sahm elaborated on that thought in a recent interview with Yahoo Finance:
Sahm writes that she believes neglect, not manipulation, is harming U.S. economic data:
I am worried about the state of U.S. economic statistics. The current problem is not political interference; it is chronic and intensifying underinvestment. Budgets for statistical agencies have failed to keep pace with inflation for years, and conditions deteriorated further during the first year of the Trump administration.
The Bureau of Labor Statistics lost more than 20% of its staff since the fiscal year 2024, and 13 of its 35 leadership roles remain unfilled. Budget constraints led to a 15% reduction in the CPI sample and to the elimination of three cities from coverage. The federal government shutdown caused the first-ever break in the monthly unemployment and inflation series. …
Lower response rates and smaller sample sizes make the estimates less precise and more volatile. The threat to data quality is not manipulation—it is neglect.
Sahm concludes:
Trust in economic statistics depends on more than guarding against political manipulation. It requires sustained investment and public understanding of what the numbers can—and cannot—tell us. To preserve a reliable lens on the U.S. economy, we must protect both the integrity of the data and the institutions that produce it.
My thoughts
The potential for manipulation remains a threat. Trump’s first nominee to fill the BLS director’s post was an inexperienced economist with strong MAGA connections and a past history of “incendiary rhetoric.” Republican senators quietly joined together to kill that nomination. Trump’s current nominee, Brett Matsumoto, is a highly respected statistician that Sahm has strongly endorsed.
Beyond the threat of manipulation is the difficult issue of trust. I now sense widespread, bipartisan distrust of U.S. inflation numbers. That’s a difficult problem for the BLS to solve in the aftermath of funding and staffing cuts. The October shutdown left a crater of missing inflation data, which very well could be causing U.S. inflation to be under-reported. This will take time to fix. Meanwhile, the distrust grows.
Bonus Sahm coverage
Claudia Sahm was a guest last week on the “On Investing” podcast hosted by Schwab’s Kathy Jones and Liz Ann Sonders. She discussed the Fed’s balance sheet, the impact of AI on labor markets and touched on trust in economic statistics. Here is the podcast:
In closing
Yes, I have just returned from Australia, suffering from terminal jet-lag. Please excuse any typos and nonsensical sentences in this article. Reader are invited to be my editors, as always!
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David Enna is a financial journalist, not a financial adviser. He is not selling or profiting from any investment discussed. I Bonds and TIPS are not “get rich” investments; they are best used for capital preservation and inflation protection. They can be purchased through the Treasury or other providers without fees, commissions or carrying charges. Please do your own research before investing.

