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The Federal Reserve has a tough job. It must use monetary policy to achieve two goals for the U.S. economy: stable prices and maximum employment.

Doing its job requires Federal Reserve officials to pay close attention to a lot of data, which ideally, would be of high quality. After all, both jobs and the control of inflation are on the line. Having accurate, high quality data is needed to make achieving the Fed's dual goals possible.

Unfortunately, a lot of the data on which they rely hasn't been passing muster in recent years, particularly employment data reported by U.S. government agencies. The situation has gotten bad enough that Fed officials have been openly complaining about it for months. In their view, the contemporary employment data is becoming less than useful.

Here's an example discussing how bad data in 2021 caused the Fed to badly misread the true state of the U.S. economy.

Federal Reserve officials have said countless times they take a “data-dependent approach” to their policy decisions, including their current conundrum of when to slash interest rates. But what if the data isn’t as dependable as it once was?

That’s what appears to be happening — and it’s making central bankers’ jobs a lot harder.

“We have to make decisions in real time,” Fed Governor Christopher Waller said late last year. “Whatever data is released, that’s the data I have to use. The problem with data is it gets revised.”

That wouldn’t necessarily be so much of an issue if the revisions, which can come months after initial reports are released, were relatively small. However, many revisions over the past few years have been game-changers.

For instance, Waller pointed out that initial monthly headline employment numbers for 2021 led him to believe that the job market was “okay, but it’s not really great.” Even though inflation was at a 40-year high, he and other Fed officials were under the impression that they’d need to proceed very carefully with raising interest rates, fearing it could lead to job losses, Waller said.

In reality, the job market was much stronger than the numbers indicated after state and local governments lifted their pandemic lockdowns, which added to the inflation unleashed in 2021, making it much more difficult to contain. Getting the data wrong, as in this example, literally imposed a higher cost of living on all Americans.

That situation involved the U.S. government's employment data failing to capture the true number of Americans who went back to work after being forcibly idled by the lockdowns. Let's flash forward to today, where the opposite situation has just taken place. The U.S. government has failed once again to accurately account for the number of working Americans, only this time, it has overcounted them. Job growth in the U.S. has been "far weaker than initially reported":

US job growth during much of the past year was significantly weaker than initially estimated, according to new data released Wednesday.

The Bureau of Labor Statistics’ preliminary annual benchmark review of employment data suggests that there were 818,000 fewer jobs in March of this year than were initially reported.

Every year, the BLS conducts a revision to the data from its monthly survey of businesses’ payrolls, then benchmarks the March employment level to those measured by the Quarterly Census of Employment and Wages program.

The preliminary data marks the largest downward revision since 2009 and shows that the labor market wasn’t quite as red hot as initially thought.

Here's how that matters to Federal Reserve officials:

Both Chicago Fed president Austan Goolsbee and San Francisco Fed President Mary C. Daly said the central bank was closely watching unemployment to gauge whether to cut rates and if so, how much to cut by.

And last week, Atlanta Fed president Raphael Bostic told the Financial Times that he was open to the idea of the central bank cutting interest rates at its next meeting in September, and that the Fed has to be “extra vigilant.”

“Because our policies act with a lag in both directions, we can’t really afford to be late. We have to act as soon as possible,” he said.

That's what's at stake. If jobs were really as strong as the data had previously indicated, Fed officials would every reason to continue acting as if job growth in the U.S. economy were not slowing, which is what they have been doing for much of the past year.

One of the outcomes of that are news stories that point to the bad jobs data as if it's an accurate description of reality and treat the people affected by the true lackluster performance of the U.S. job market like they're idiots. Here's an example of that phenomenon, which is coincidentally from the same article quoting the Fed officials paying close attention to unemployment data:

The economy may look strong on paper, but Americans are more worried than ever about losing their jobs as fears of a “vibecession” take hold.

The portmanteau “vibecession,” coined by Gen-Z economist and TikTok star Kyla Scanlon, refers to the disconnect that occurs when public financial sentiment is negative even though the economy isn’t in a recession. Given the growing chasm between the stock market and job anxiety, the phenomenon appears to be in full swing.

In truth, that Gen-Z TikTok influencer hot take had a one-day shelf life. It was quickly debunked by the second largest-ever downward revision to the Bureau of Labor Statistics employment data in the bureau's history. Only 2009 has seen a larger downward revision, during the Great Recession.

It would seem that public sentiment is not as misplaced as that initidata, now recognized to have been far off target, had suggested. The revised data, such as it is, still suggests the U.S. job market improved over the past year, but with far less growth than had previously been claimed. The one thing we know for sure is the economy is not anywhere near as strong as the previous data had claimed.

The bottom line is the BLS has some serious work ahead of it to improve the quality of its employment data collection. Getting it so far off target and letting it stay wrong for so long is making it harder for decision makers to put forward the policies that are truly needed when they are needed. In the case of Federal Reserve officials who rely on that data, getting the data wrong has costs that ripple all the way through the economy.

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