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TrimTabs employment analysis, which uses real-time daily income tax deposits from all U.S. taxpayers to compute employment growth, estimated that the U.S. economy shed 255,000 jobs in November. This past month’s results were an improvement of only 10.2% from the 284,000 jobs lost in October.
Meanwhile, the Bureau of Labor Statistics (BLS) reported that the U.S. economy lost an astonishingly better than expected 11,000 jobs in November. In addition, the BLS revised their September and October results down a whopping 203,000 jobs, resulting in a 45% improvement over their preliminary results.
Something is not right in Kansas! Either the BLS results are wrong, our results are in error, or the truth lies somewhere in the middle.
We believe the BLS is grossly underestimating current job losses due to their flawed survey methodology. Those flaws include rigid seasonal adjustments, a mysterious birth/death adjustment, and the fact that only 40% to 60% of the BLS survey is complete by the time of the first release and subject to revision.
Seasonal adjustments are particularly problematic around the holiday season due to the large number of temporary holiday-related jobs added to payrolls in October and November which then disappear in January. In the past two months, the BLS seasonal adjustments subtracted 2.4 million jobs from the results. In January, when the seasonal adjustments are the largest of the year, the BLS will add anywhere from 2.0 to 2.3 million jobs. In our opinion, trying to glean monthly job losses numbering in the tens of thousands or even in the hundreds of thousands are lost in the enormous size of the seasonal adjustments.
In November, the BLS revised their September and October job losses down a surprising 44.5%, or 203,000 jobs. In the twelve months ending in October, the BLS revised their job loss estimates up or down by a staggering 679,000 jobs, or 13.0%. Until this past month, these revisions brought the BLS’ revised estimates to within a couple percent of TrimTabs’ original estimates.
The large divergence between the two results begs the question of what is causing the difference. While we don’t have an answer today, we will be poring over the data in an attempt to answer that question.