As fresh labor market numbers for November are set to hit the headlines, top economist Justin Wolfers is urging the public to read the data with extreme caution.
Shutdown’s ‘Echo Effect’
Wolfers warns that the recent government shutdown has created a statistical “deep fog” that is likely artificially inflating November's unemployment rate, making the economy look worse than it actually is.
According to Wolfers, the root of the problem is the cancellation of the October survey during the government shutdown. This gap in data collection has disrupted the delicate “4-8-4” rotation cycle used by the Bureau of Labor Statistics (BLS), where households are surveyed for four months, left alone for eight, and then surveyed again.
Because there was no survey in October, the composition of the November sample has been drastically altered. Typically, only one-eighth of the respondents are new to the survey.
However, Wolfers points out that in the November sample, there are “twice as many inexperienced respondents” as usual—roughly 25% of the total pool.
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Understanding ‘Rotation Group Bias’
This shift in the sample composition matters because of a phenomenon Wolfers identifies as “rotation group bias.”
Historical data show a clear psychological or reporting discrepancy: individuals answering the labor survey for the first time consistently report higher unemployment rates than those who have been surveyed previously.
Pointing to data from 2022 through 2025, Wolfers notes that the measured unemployment rate among first-time respondents is “typically 0.7 percentage points higher” than among experienced respondents.
With a higher volume of these “pessimistic” first-timers flooding the November sample, the overall weighted average is mathematically destined to skew upward. Wolfers predicts this “will likely push the measured unemployment up a tad,” regardless of the actual health of the job market.
Foggy Economic Picture
Beyond the bias, the data is also suffering from increased sampling error. The BLS usually relies on panel data methods—effectively tracking the same people over time—to smooth out volatility.
With a quarter of the sample being new, these methods are less effective, leading to potentially erratic estimates.
Wolfers notes that while the government is technically “out of the statistical blackout,” the shutdown's “echo effects” have made it uniquely difficult to parse the official numbers, leaving analysts and policymakers navigating a “deep fog.”
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