A Conversation About Payroll Data That You Never Saw On CNBC
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A Conversation About Payroll Data That You Never Saw On CNBC
by Robert E. Bronson, III
Bronson Capital Markets Research
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Joereal at CNBC: We’re very pleased today to introduce Constant Pangloss for his take on today’s payroll data report.
Pangloss: Thank you, Joereal. I’m delighted to be here. Today’s report was just outstanding! To be factual, July’s loss of jobs was 44% less than June’s jobs loss and a huge 64% less than the biggest jobs loss in February! This “green shoot” is proof positive that the recession has nearly, if not already, ended, which is confirmed by today’s breakout bull market rally to a new intraday high at 1018 for the S&P 500!
Joereal: Did you notice that the 9.4% unemployment rate from the household survey would have been 9.7% rather than 9.4%, if some 422,000 unemployed people had not left the labor force during the four weeks prior to the survey, many of them so discouraged that they did not look for work because they know there are no jobs for them?
Or that the seasonal adjustment of the payroll data artificially created 28,000 jobs in the auto industry, when, in fact, automakers cut 8,600 jobs, so that the reported 247,000 jobs lost in July were really closer to 285,000 jobs or 15% worse than reported?
Pangloss: I’ll take your word for it, Joreal, but I find that hyper-technical, and in any case, it doesn’t change the solid uptrend of “green shoots” in the payroll data because of the current administration’s stimulus and their other constructive interventions.
Joereal: Ok, but we’re putting up on the screen the long term history of the growth in payrolls since the BLS first started tabulating them during The Great Depression.
Pangloss: Well…OK…I guess the more contextual information the better.
Joereal: Ok, here’s the chart that appeared in Rex Nutting’s article illustrating the record-breaking descent in the 10-year growth rate in private sector payroll employment since 1939.
Source NYT
Pangloss: No, no, no…that’s way too much data. I prefer the shorter term chart that the rest of the media is presenting. If you simply ignore the fluke in June, just look at this year’s strong uptrend of solid “green shoots!”
Joereal: I see what you have selected, but do you mind if we also consider both the raw or base employment data, as well as their monthly changes, at least since just before the recession started?
Pangloss: I guess not.
Joereal: OK. Notice that the cyclicality and the downtrends in both of the following datasets, suggest job losses in the coming months may be worse.
Pangloss: What? I’m just going to ignore the so-called perfect polynomial fit in the first chart. In the second chart, I believe that the linear downtrend is irrelevant, and the cyclical extrapolations are obviously too influenced by both the longer term data since the recession began and by the recent June fluke in the data.
Joereal: Well, here’s a two-month average of just this year’s data, and a curvilinear best-fit of the data suggests the same outcome of lower payroll data coming in the months ahead….
Joereal: …and there’s another consideration. As you can easily extrapolate visually from the downtrend in the last three years of data and from the curvilinear best fit of all eight years of data in the following table prepared by the BLS, their annual payroll benchmark revisions to be reported in early October for the 12 months of employment data through March 2009 (which was the worst part of the banking crisis), along with the regular monthly report of September (2009) data, will likely show a downward change of a whopping 2,000,000 jobs, if not more, similar to the 2,000,000 downward revisions reported in October 2002 as a result of the last recession. A huge, downward revision in payroll jobs expected in the report this October is all the more likely because the BLS data are only sample-based estimates and do not include any business cycle considerations, and because the current recession is much more severe than the last one.
Source: BLS
Joereal: ….Surely you’ll agree that this will raise questions about the “green shoot” proof that the recession has ended, or nearly ended, right?
Pangloss: This is all hyper-technical and I don’t respond to hypothetical questions.
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Bob Bronson, August 12, 2009
Bronson Capital Markets Research
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September 8th, 2009 at 12:03 pm
Ok, I don’t think we’re out of the woods in this recession and certainly don’t buy the “employment is improving and will be great by years end” junk, but seriously, extrapolating with 6th order polynomials? Either this post is a joke or no one should ever take advise from the author (“If you have a y=(a*sin(bx)+c^x*cos(d*x^e)+sum(f*x^n,n,1,h))^k function and do a least squares regression, you’ll find employment should be at 180% next month! Huzzah!”)
September 8th, 2009 at 12:06 pm
Thanx for the feedback, Zebov. See six polynomial best fits below with the latest August payroll data.
And here is the track record of the past 12-years’ forecasts of the author that you questioned:
http://www.financialsense.com/editorials/bronson/0512_Forecast.pdf
And here is a 6 polynomial study:
http://www.ritholtz.com/blog/wp-content/uploads/2009/09/6-polynomials.PNG
September 8th, 2009 at 12:11 pm
Hear hear!
The string theory accept the possibility that the Universe has 4 or 5 dimensions.
So, what’s wrong with a 6th order extrapolation? ;-)
September 8th, 2009 at 12:45 pm
A good argument, damned with faint praise — those polynomial regressions always put all the errors into the ends of the fitted range. For example, extrapolating any of these backwards into history would find 120% employment (negative 20% unemployment) for history where it’s obviously not, nor would it be.
Alternatively, a good hoot. Thanks, Dr. P!
September 8th, 2009 at 1:24 pm
All backwards extrapolations of a part of a cyclical time series dataset, even the simple linear trendlines used in technical analysis, have such a desirable limitation – by design. The advantage of polynomial best fits is that they give equal weight to all the data, whereas technical analysis trendlines usually only consider a few extreme data points.
There are many extrapolation techniques – we’ve developed one that discovers any and all time series-like analytic functions given just three consecutive data points – which must be appropriately applied including qualitative analysis of the underlying.
In this case, the sixth degree polynomial best fit was featured because it best comports with the highly probable coming shock of the Oct 5th BLS benchmark revision of the payroll data according to their own history of such revisions as Joereal explains.
September 8th, 2009 at 1:28 pm
Well I think the post is quite profound.
And much closer to the truth than anything else you can find. MSM? Gov? Think not. Green shoots.
They obviously have a good fix on the seasonality issue and how the BLS boogers things. Birth/death? Right.
September 8th, 2009 at 3:30 pm
@Bob Bronson “the sixth degree polynomial best fit was featured because it best comports with the highly probable coming shock of…”
This is my point exactly. There is ALWAYS a function you can pick whose least squares regression will hit all the current datapoints AND “predict” whatever you want it to. Unless there is some other supporting reason on why a 6th order polynomial should be chosen (and should ALWAYS be chosen) to get accurate results, this is purely manipulation of data to make it say what you want.
As a researcher, if I attempted to support an argument in a journal article submission with a 6th order regression supported by nothing else other than “that’s what makes my opinion look right” my article would get rejected immediately.
There are SO MANY logical arguments supporting the conclusions of this article, why mess it up by throwing this in there? It makes the rest of the article look questionable.