US Core CPI Eases to Four-Year Low in Shutdown-Impacted Report

Stephanie Roth, Chief Economist at Wolfe Research, reacts to the November US CPI report.

Underlying US inflation rose in November at the slowest annual pace since early 2021, an unexpected improvement in a report muddled by the government shutdown.

The core consumer price index, which excludes the often-volatile food and energy categories, increased 2.6% in November from a year ago, according to Bureau of Labor Statistics data out Thursday. The overall CPI climbed 2.7% in November from a year ago.

The longest-ever government shutdown prevented the BLS from collecting much of the October price data. That not only limited the agency’s ability to determine month-over-month changes for the broader measures of inflation, but some economists noted it likely impacted the annual November figures as well.

Despite numerous caveats, the report offers some hope that inflationary pressures are easing after remaining stuck in a narrow range since early this year. The BLS said the core CPI rose just 0.2% over the two months ended in November, restrained by declines in costs of hotel stays, recreation and apparel. Prices of household furnishings and personal care products rose.

However, several economists noted that the two-month change in key shelter categories, which are some of the biggest components of the CPI, was basically flat — calling the calculations into question.

“It’s possible that this does reflect a genuine drop off in inflationary pressures, but such a sudden stop, particularly in the more-persistent services components like rent of shelter is very unusual, at least outside of a recession,” Paul Ashworth, chief North America economist at Capital Economics, said in a note.

“The upshot is that is looks like we all have to wait until the December data is published next month to verify whether this is a statistical blip or a genuine disinflation,” he said.

After long-awaited government data showed underlying US inflation cooled to a four-year low in November, economists agreed on at least this much: something was off.

In a report fouled by the record-long government shutdown, inflation in several categories that had long been stubborn seemed to nearly evaporate. Chief among those were shelter costs, which make up about a third of the consumer price index, but other categories like airfares and apparel notably declined.

Because of the shutdown, the Bureau of Labor Statistics couldn’t collect prices throughout October and started sampling later than usual in November. The so-called core CPI, which excludes food and energy, increased 2.6% in November from a year ago — the slowest pace since 2021 and below all estimates in a Bloomberg survey of economists.

Several forecasters pointed to the absence of that October data — which resulted in pages of blank spaces in the widely watched report — as effectively the same as assuming no price growth for the month. That culminated in sizable downward pressure on the November inflation figures, they said. Some noted the shortened collection period could have also skewed the data.

The titles of their analyses were telling: “Lost in Translation,” according to TD Securities. “Delayed and Patchy,” per William Blair, and a “Swiss Cheese CPI report” from EY-Parthenon.

“This one-of-a-kind report produced anomaly after anomaly, almost all pointing in the same direction,” Stephen Stanley, chief US economist at Santander US Capital Markets LLC, said in a note. “I think it would be unwise to dismiss the results entirely, but I also believe it would be rash to take them at face value.”

The shutdown limited the BLS’s ability to calculate standard month-over-month price index values, so it mostly observed changes from September to November instead. In FAQs and other supporting documents published the day before the report, the agency forewarned that some the data may not be totally trustworthy.

“If bimonthly CPI data are volatile, then less confidence should be placed in estimates for the missing months,” BLS said Wednesday in a document explaining how to approximate missing data points.

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