Much has been said about the January Non-farm payrolls number, which rose by 113K on expectations of a 180K increase, most of which has been focused on the US atmospheric conditions during the winter. There is a problem with those numbers: they don’t really exist (as for the non-impact of “the weather” on jobs we showed previously that the number of people “not at work due to weather” as calculated by the BLS itself. this winter was lower than 2008, 2009, 2010, 2011 and 2012 – so much for historic winter weather).
So what really happened in January?
For the real answer we have to go to the BLS’ non-seasonally adjusted data series. It is here that we find that in January, some 2.870 million real, actual jobs were lost, not gained. Putting this further in perspective, the number of NSA jobs losses in January 2014 was greater than in January of 2013, 2012, 2011 and tied that of 2010. In fact only during the peak of the depression in January 2009 was there a greater NSA drop in the first month of the year when 3.691 million jobs were lost.
So how does a loss of 2.9 million jobs become a “gain” of 113K jobs in the same month? Simple: through the magic of seasonal adjustments.
Incidentally, for all those confused, it is these same seasonal adjustments that at least on paper, are supposed to account for such things as seasons, and, well, weather. Only sometimes they apparently don’t, like right now. Which is also the reason why one can completely ignore the entire seasonal adjustment process because one after another economist is lining up to inform anyone caring to listen that the seasonal adjustment number is actually not adjusted enough.
Below we break down which jobs comprised the 2.9 million jobs lost when ignoring the ARIMA fudge factors.
On one hand the transition from 137.386 million seasonally adjusted December jobs to 137.499 million seasonally adjusted January jobs is simple: just add 113K jobs.
On the other, the one has to consider that the actual number of January 2014 jobs was a far lower 135.396 million jobs. Furthermore, one has to recall that his unadjusted number is the one impacted by the monthly Birth-Death adjustment. For those who don’t recall this nuance, here is the BLS’ explanation of this incremental adjustment factor to the final number:
There is an unavoidable lag between an establishment opening for business and its appearance on the sample frame making it available for sampling. Because new firm births generate a portion of employment growth each month, non-sampling methods must be used to estimate this growth.
Earlier research indicated that while both the business birth and death portions of total employment are generally significant, the net contribution is relatively small and stable. To account for this net birth/death portion of total employment, BLS uses an estimation procedure with two components: the first component excludes employment losses due to business deaths from sample-based estimation in order to offset the missing employment gains from business births. This is incorporated into the sample-based estimate procedure by simply not reflecting sample units going out of business, but imputing to them the same trend as the other firms in the sample. This step accounts for most of the birth and death employment.
The second component is an ARIMA time series model designed to estimate the residual birth/death employment not accounted for by the imputation. The historical time series used to create and test the ARIMA model was derived from the UI universe micro level database, and reflects the actual residual of births and deaths over the past five years. This ARIMA model was originally applied once a year using new data to calculate the net birth/death forecasts. Effective with the release of preliminary January 2011 employment estimates in February 2011, BLS began updating the Current Employment Statistics (CES) net birth/death model component of the estimation process more frequently, generating birth/death factors on a quarterly basis instead of annually.
The net birth/death model component figures are unique to each month and exhibit a seasonal pattern that can result in negative adjustments in some months. These models do not attempt to correct for any other potential error sources in the CES estimates such as sampling error or design limitations. Note that the net birth/death figures are not seasonally adjusted, and are applied to the not seasonally adjusted monthly employment estimates to derive the final CES employment estimates.
Simple enough, right. Anyway, in January the Birth-Death adjustment resulted in a further 307K jobs being subtracted from the final NSA number as an intermediate adjustment step.
So what happened next? Instead of explaining, we’d rather show it:
While there are numerous other intermediary “transformational” steps designed precisely to obfuscate and baffle with BS, the bottom line is that through the magic of the BLS’ statistical gimmickry, a NSA change of -2,870K, impacted by a further -307K “Birth/Deaths” (or -3,177K total), became a positive 113K change in the seasonally adjusted jobs series, or a total “seasonal adjustment” factor of 3.290 million!
And here, keep in mind, that the Wall Street estimate for a payroll “beat” was the addition of 180K. Of course, in keeping with the above “seasonal” transformation, what this means is that instead of having added a total of 3.290 million “statistical” jobs, the Bureau simply had to bump up the “fudge factor” to 3.357K or over, to match or beat the expectations. The difference between the 3.290MM and 3.357MM numbers? 2%.
Which begs the question: why did whatever prompted the BLS to add just under 3.3 million “excel” jobs, not also prompt them to add another 57K jobs to the final adjustment number which was a minute fraction of the total fudge factor, and beat Wall Street estimates, reincarnating the narrative that the US economy is now back in “escape velocity” mode, it is “self-sustaining” and all those other spins and stories serious economists tell themselves to justify that QE has worked? Unless, it was specifically the intention of the BLS to not give such an impression.
Whether the reason was precisely to give the Fed the loophole it needs to taper the taper in a few weeks time when the latest “economic recovery” thesis crashes and burns, will be uncovered over the next several months.
Finally, for all those same very serious economists who lament the impact of the weather on the Establishment survey, and yet point to the surge in the Household Survey which “added” 638K seasonally-adjusted jobs, the unadjusted change there was a drop of 403K jobs. So there’s that.