Update: looks like Newport Beach is reading:
— PIMCO (@PIMCO) August 6, 2013
Many were surprised when last month we exposed the divergent lies at the Bureau of Labor Statistics when comparing two otherwise convergent data sets: the monthly all-important Non-Farm Payroll report and the (one month-delayed) JOLTS survey. Specifically, what we showed is that the Net Turnover from JOLTS (Hires less Separations) is now 40% below the trendline of cumulative job additions implied by the Non-Farm Payroll report’s Establishment survey which has become the holy grail for both the stock market and the Federal Reserve’s tapering ambitions. Following the release of the June JOLTS update, we can report that the divergence within BLS data series continues, and that the average monthly US job gain for the first 6 months of 2013 is either 198K if one uses the non-farm payroll data, or 30% lower, 140K to be specific, if one uses the JOLTS net turnover number.
The divergence in the two data series, historically convergent, can be seen highlighted on the chart below:
While from a distance the highlighted area may not amount to much, here it is zoomed in just for 2013. The difference becomes quite pronounced, and amounts to just shy of 60K jobs per month on average for 2013 alone.
Putting the above into words:
-> A 42% difference!
Finally, the chart below shows that while until 2013 the divergence between two data series has been mostly cluster-free except for the Lehman collapse and the period just after it promptly normalizing thereafter, the past 7 months have seen a dramatic imbalance in data benefitting the algo-headline scanner moving NFP data, which on a 3 month trailing basis is almost as wide as it has been at any point in the past 5 years and just shy of the wides seen just after the Lehman collapse.
This means that either the JOLTS survey is substantially underrepresenting the net turnover of workers, or that once the part-time frenzy in the NFP data normalizes, the monthly job gains will plunge to just over 100K per month to “normalize” for what has been a very peculiar upward “drift” in the NFP “data.”
When manipulating data series across dimensions, make sure the manipulations foot across, and not just in 1 dimension.
As always, we urge readers to recreate the above results on their own: the Hires timeseries can be found here, the Separations timeseries is here, while the matching reported Nonfarm Payroll series is, as always, here.Courtesy: Zerohedge
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