President Donald Trump’s dismissal of Erika McEntarfer, the Commissioner of the Bureau of Labor Statistics (BLS), has sparked widespread alarm among economists, statisticians, and policymakers who warn that the move undermines the credibility of the nation’s economic data and risks long-term damage to public trust and economic decision-making.
The firing, announced following Trump’s claims that McEntarfer led a year-long effort to manipulate jobs data against him, is seen by many experts as politically motivated and based on a misinterpretation of standard statistical practices.
The Allegations and the Reality
Trump’s accusations center on two claims:
- Job Estimate Revisions:
In August 2024, the BLS announced a downward revision of total U.S. employment figures by nearly 800,000 jobs, later finalized at just under 600,000. Trump cited this as evidence of intentional inflation of job numbers to damage his reelection campaign.
However, these annual revisions — known as “benchmarking” — are a routine part of the BLS’s methodology. Each year, monthly payroll survey estimates are compared against comprehensive administrative records from the prior March. In an economy with over 163 million jobs, revisions of this scale are common. - Recent Job Growth Figures:
Trump also pointed to weaker job gains in July 2025 (73,000 jobs) and downward revisions for May and June (combined net loss of 258,000 jobs) as supposed fabrication.
In reality, these figures resulted from late-arriving employer surveys, which revealed a slowdown in public education hiring after the expiration of COVID-era federal subsidies. Most economic sectors showed broad weakness in July, with the exception of retail and healthcare.
Why This Matters: The Dangers of Politicizing Data
While the president’s claims are unsubstantiated, the consequences are serious. Labelling official economic statistics as “fake news” or accusing federal agencies of manipulation without evidence damages the credibility of the BLS and casts doubt on all federal statistical agencies.
This is problematic for several reasons:
- Federal statistics have no private-sector equivalent. Unlike commercial data, federal statistics are:
- Privacy-protected
- Transparent in methodology
- Free and publicly accessible
- Designed for national consistency and policy relevance
- Private data providers rely heavily on public data to build their own models and forecasts.
- Undermining trust in BLS data could lead policymakers, businesses, and the public to base decisions on lower-quality information, increasing uncertainty and economic risk.
Consequences of Political Interference
Replacing an independent commissioner with political appointees, as Trump reportedly plans, could compromise the BLS in several key ways:
- Loss of perceived independence — future commissioners may not be trusted, regardless of qualifications.
- Declining response rates — individuals and businesses may opt out of surveys they view as politicized.
- Staff under pressure — fear of political retaliation could influence how data is handled or reported.
- Sudden changes in methodology — without proper testing or transparency, new approaches could distort key indicators.
- Brain drain — expertise may be lost as merit-based hiring gives way to loyalty-driven appointments.
These risks mirror crises faced in other countries where economic data was politicized, such as Greece and Argentina — both of which suffered deep economic and social consequences after credibility in national statistics collapsed.
The Bottom Line
President Trump’s decision to fire Erika McEntarfer and his broader attack on the BLS represents a serious threat to the integrity of U.S. economic data. Experts warn that the move not only endangers the reliability of crucial statistics, but could also undermine market confidence, reduce investment, and damage the U.S.’s global economic reputation.
As the country faces ongoing economic uncertainty, trusted and independent data is more vital than ever. Undermining that foundation could have lasting repercussions far beyond political cycles.