What is the value of high-quality, trustworthy official statistics? Given the number of things that statistical agencies measure, we might expect that they have attempted to put a number on this too. In fact, they have often been rather coy. A UN report, “Promoting, Measuring and Communicating the Value of Official Statistics”, published in 2018, was packed with qualitative ideas about how statistics were useful: they were said to build trust in government, improve decision-making, promote equality and “help us understand who we are, have been and are becoming”. All reasonable enough, but cost-benefit analysis was thin on the ground.
A cynic might suggest this near silence speaks volumes. Maybe official statistics have little value? That was the radical view of Sir John Cowperthwaite, who was the financial secretary of Hong Kong throughout the 1960s, when it was a rapidly growing, laissez-faire British colony. Cowperthwaite thought the value of official statistics wasn’t merely minimal, but negative: he told the economist Milton Friedman that he didn’t collect economic data, because it would only encourage the Whitehall variety of mandarin to interfere.
In context, Cowperthwaite’s position was understandable: few economies were more at risk of clumsy meddling than Hong Kong, a colonial possession pursuing a libertarian path on the opposite side of the world from soft-left imperial rulers. Still, there are at least two weaknesses in his argument. The first is the hope that ignorance might restrain the interventionist impulses of governments. It might simply make those interventionist impulses clumsier. The second is the unexamined premise that only a government might find official statistics useful.
A report from the US National Academies last year argued otherwise. While governments do rely on official statistics for everything from political representation (often tied to population) to the inflation adjustment of pensions and other welfare payments, many organisations and individuals also rely on trustworthy statistics for anything from deciding where to locate a new storefront or warehouse to directly selling analysis based on government data. The National Academies reckons that the revenue of the “government data-intensive sector” in the US almost doubled between 2012 and 2022, to just shy of $800bn, a direct sign that somebody finds these numbers useful. For context, the total budget of all US statistical agencies and programmes in 2022 was $7.1bn.
But if you want to understand whether a thing is useful, you can always look at what happens when somebody breaks it. Call this the Joni Mitchell principle: you don’t know what you’ve got till it’s gone.
The National Bureau of Economic Research’s new working paper “The Value of Reliable Statistics” comes from Stanford’s Nicholas Bloom, Erica Groshen, a former boss of the Bureau of Labor Statistics (BLS), and two scholars from the American Enterprise Institute, a pro-market think-tank. It studies the impact of one particular fracture: President Trump’s firing last August of Erika McEntarfer, head of the BLS, along with his simultaneous claim that, “In my opinion, today’s Jobs Numbers were RIGGED in order to make the Republicans, and ME, look bad.”
As I wrote at the time, this was a two-pronged attack on the credibility of the BLS. By attacking the institution’s record, Trump was damaging it in the eyes of his supporters, and, by replacing its leader in such a way, he was damaging it in the eyes of his opponents. Bloom and his colleagues do not try to measure the impact of Trump’s actions on the BLS’s capacity; it is the question of its credibility that interests them.
To measure this, the researchers look at the Economic Policy Uncertainty Index, a dataset developed about 15 years ago by Bloom and others. The EPU measures uncertainty about the direction of economic policy by analysing the text in major US newspapers. It spikes when newspapers can talk of little else but how policymakers are causing confusion.
Unsurprisingly, the index sharply rose immediately after McEntarfer was fired. It fell back not long after, but as the researchers note, “even when the underlying rise in uncertainty is more persistent . . . the news cycle moves on”. Based on earlier research into the effect of uncertainty on investment and growth, the researchers suggest that the increase in economic uncertainty that week could have caused $104bn of economic damage and that up to 168,000 jobs could have been lost.
Those are large numbers, but as Bloom and colleagues freely admit, they are not good estimates of the impact of Trump’s words and actions because there were other reasons for the EPU to increase. The first and most obvious reason is that the trigger for McEntarfer’s firing was a large downward revision in the jobs numbers, which would itself have raised uncertainty even if Trump had done nothing. A Federal Reserve governor, Adriana Kugler, announced her resignation on the same day. All three events happened within hours of each other and all three could reasonably have been interpreted at the time as adding to a sense of chaos and pushing the EPU up.
After trying to isolate coverage only of McEntarfer’s firing, the researchers produced a preferred estimate of nearly $20bn of economic damage, resulting from the fear, uncertainty and doubt generated by the ejection and criticism of McEntarfer. It’s still fair to describe this estimate as itself highly uncertain. It is, after all, measuring what the newspapers found newsworthy. Generally, serious newspapers put stuff that matters on the front page, and when the news is about unpredictable economic policy that has generally been a bad sign. But sometimes newspapers get excited about things that don’t much matter; perhaps McEntarfer’s firing was one of those things. It is impossible to be sure.
The estimated damage from the affair, while a tiny sliver of US GDP, is about 25 times the entire budget of the BLS. This, perhaps, is the argument for investing in reliable statistics, and for not undermining them in the hope of fleeting partisan advantage: they do not cost very much relative to what they are trying to measure.
In the US, just over one dollar in a thousand of federal government spending goes to statistical agencies and other statistical programmes. The case for government-funded statistics is that it is worth spending one dollar in a thousand in the hope that the other $999 might be fractionally better used as a result.
Written for and first published in the Financial Times on 13 May 2026.
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