Bad government statistics can cost the economy billions

Low engagement is a problem because the surveys are much more than a way to satisfy Uncle Sam’s curiosity about the citizenry. Bosses and investors rely on accurate data about GDP growth, joblessness, inflation and much else besides to manage businesses and allocate capital. When the information becomes less reliable, investment decisions get more difficult to make. Some may be delayed, creating a potential drag on the economy.

But how much of a drag? A new working paper by Nicholas Bloom of Stanford University, Erica Groshen, former head of the Bureau of Labour Statistics (BLS) now at Cornell University, and Duncan Hobbs and Michael Strain of the American Enterprise Institute, a think-tank, hazards an estimate. Preserving trust in “the integrity and quality of official statistics”, the authors claim, generates economic benefits of about $25 for every $1 spent on the BLS, the agency with an annual budget of $700m that is responsible for many of these data.

To arrive at their conclusion the quartet analysed an ignoble episode in the BLS’s recent history. On August 1st 2025 Donald Trump sacked Erika McEntarfer, appointed as the agency’s commissioner by his predecessor, Joe Biden. The president alleged, without evidence, that the BLS’s steep downward revision to recent jobs numbers had been “RIGGED in order to make the Republicans, and ME, look bad”.

In fact, it was the wanton dismissal that looked bad in the eyes of many observers. In the following seven days there was a 50% leap, relative to the week before, in the average value of the index of Economic Policy Uncertainty (EPU), which tracks the number of articles mentioning such uncertainty that are published daily in American newspapers. This was a discernible jump even when compared to the chaos caused by Mr Trump’s trade war in April 2025 and his real one in Iran in the past two months. Based on Mr Bloom’s earlier study with other colleagues of the EPU’s impact on business investment, industrial production and employment, the authors estimate that the jump reduced American GDP by over $100bn (0.33%) and non-farm payrolls by 168,000 (0.11%).

Some of this may not have been the direct result of Ms McEntarfer’s firing. A part was probably due to the big jobs revision itself. The unrelated resignation of a Federal Reserve governor on the same day may have played a role, too. But even after controlling for those variables, the researchers reckon that the McEntarfer affair trimmed GDP by $20bn and employment by 31,000 jobs.

A brazen presidential attack on statisticians’ dependability may, admittedly, do more damage than a slow-motion crisis caused by unco-operative respondents. But the study shows that reliable statistics have real value. Forsaking them would be an incalculable loss.

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