The Biden Administration systematically overstated job creation in 2023 for a total of 1.3 million jobs.
As suspected, under Biden the number of jobs created as reported by the Bureau of Labor Statistics was wildly overstated. We suspected this in the Obama years and suspected the opposite in the Trump years, now there is evidence it is happening with Biden’s economic numbers.
The Biden administration regularly overestimated the number of jobs created each month over the past year by at least 1 million, data from the Bureau of Labor Statistics (BLS) indicate.
The Daily Caller reported that the “federal government in 2023 overestimated the number of jobs in the U.S. economy by an average of 105,000 per month in initial reports, equating to a cumulative monthly difference of 1.3 million.”
The Daily Caller News Foundation’s analysis of BLS data shows that there were 1,255,000 fewer jobs reported each month than previously thought.
This significant downward revision is attributed to new seasonal and census data impacting total employment estimates. Despite this, there was a noteworthy upward revision of 115,000 jobs in December, marking the only month in 2023 to see such a revision to the employment level, the outlet reported.
The agency’s data indicates that the most significant downward revision occurred in March, totaling a reduction of 266,000 jobs, followed by January with a revision of 234,000 jobs, and April with a revision of 205,000 jobs. Conversely, the smallest downward revisions were observed in November, amounting to only 2,000 jobs, followed by 11,000 jobs in October, the report said.
“Revisions are a normal part of the reporting process, but large changes, or adjustments that consistently move in the same direction, are not normal,” E.J. Antoni, a research fellow at the Heritage Foundation’s Grover M. Hermann Center for the Federal Budget, told the news outlet. “Instead, they’re indicative of something problematic with the BLS’ methodology. That can happen when market conditions change drastically enough to be outside of the assumptions used in their models.”