The Retired Investor: How the Gold Standard of U.S. Economic Government Data Is Being Tarnished

The data system supporting U.S. economic decisions is losing staff and funding. What's worse is the diminished trust the financial markets have placed in that data.

 

There are roughly 13 primary statistical agencies embedded within various cabinet departments and federal agencies. There are another 100 or so small decentralized statistical offices scattered across different agencies. A major problem all these agencies face is in data collection. The process is antiquated, and the system overall is worse.

 

It is a system badly in need of modernization, innovation, and a huge technological overhaul. The introduction of artificial intelligence might help. To do so, we need to bring in outside partners in the private sector. The government needs to identify state-of-the-art players in AI but also in other areas. Those that have the ability and experience in data gathering necessary to come up with new methods and strategies to obtain data in the most efficient and accurate ways possible.

 

Currently, in this age of cost-cutting through bureaucratic reductions, there is little enthusiasm in Congress or the White House to address the problem. Few data advocates exist in today's mostly populist Congress. Since bureaucrats rarely, if ever, get to talk to legislators, it's an out-of-sight, out-of-mind kind of situation.

 

As things continue to fester, another issue has also seeped into the equation. A growing belief among many is that this data is trustworthy. Is this data truly unbiased, or has it become politicized?

 

Readers may recall that a year ago, President Trump fired the head of the Bureau of Labor Statistics, Erika McEntarfer. Trump, fresh off his second presidential win, was incensed when the BLS released a revised employment statistic. The BLS reduced the number of jobs thought to have been created from March 2024 to March 2025 by 91,000.

 

Those 91,000 ghost jobs rank among the largest revisions in recent decades. "Biden's economy was a disaster, and the BLS is broken," Trump claimed, "This is exactly why we need new leadership to restore trust and confidence in the BLS's data on behalf of the financial markets, businesses, policy makers and families that rely on this data to make major decisions."

 

Trump had a point. Ever the political animal fresh off a tight race, the president recognizes the iron law of American politics — economic conditions drive election results. Historically, good economic indicators (such as low unemployment, rising wages, and low inflation) often help incumbents, while poor performance hurts them.

 

Conspiracy theorists immediately accused the former administration of padding the numbers in hopes of tilting the voters toward the Democrats' candidates. Whether that was true or not, experts insist that the process that produced that number, while imperfect, was transparent and has been used by the agency for decades. This conclusion was largely echoed by the Chairman of the Federal Reserve.

 

Back in December 2025, former Fed Chairman Jerome Powell explained that the BLS has consistently overstated jobs by at least 60,000 per month since April of 2025. The culprit was a monthly BLS estimate of how the labor market is affected by business openings and closings. The estimate, known as the birth-death model, guesses at the jobs gained and lost due to closings. This, Powell said, was "something of a systematic overcount" that would likely see big revisions to job growth numbers.

 

Nonetheless, Trump, without any evidence, fired the BLS chief, claiming the data was being rigged. That move had ramifications that reverberate today. New research by four economists at the Center for Economic and Policy Research indicates that because of Trump's claims, there was a sharp increase in policy uncertainty among American businesses. That reduction in trust and increased uncertainty depressed economic activity as corporations pulled back on investment. 

 

The hit to the economy was in the vicinity of $20 billion. Next week, I will discuss additional ramifications of doing nothing to change the status quo of how government data is gathered and distributed.