U.S. employers added 146,000 workers in January, less than expected, BLS reported. The unemployment rate fell to a three-year low of 5.2
percent as more people dropped out of the labor force.
As to the ever-decreasing unemployment rate, that’s thanks to NILFs – People who are Not In Labor Force. Once a claimant exhaust benefits and stops looking for work, they actually make the unemployment data look better. I’ve been working with a friend at Bear Stearns on this, and I may have an interesting analysis of the BLS data sometime in the coming months. Suffice it to say, the low data point much less healthy than it appears. (See the most recent Augmented Unemployment Numbers, or our previous discussion on the subject).
I will continue to take "the Under" against my Economist friends until that becomes a losing trade regarding the employment situation report. And, I continue to be astounded by the Dismal Scientists’ capacity for self-deception. It reminds me of that wonderful exchange between Jeff Goldblum and Tom Berenger in the Big Chill:
JG: I don’t know anyone who could get through the day without two or three juicy rationalizations. They’re more important than sex.
TB: Ah, come on. Nothing’s more important than sex.
JG: Oh yeah? Ever gone a week without a rationalization?
The bottom line is that we are continuing to muddle through the what looks like the worst post-recession job creating recovery in history, and much of the dismal set continues their ongoing rationalization.
Incidentally, we have Merrill Lynch to thank (via Alan Abelson) for this fascinating chart:
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Calling Nostradamus
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The sooner economists go back to their models and figure out a) why this is so, and 2) why they have been so wrong for so long, the better off we all will be . . .
>
Table Source:
Security and Securities
Up And Down Wall Street
Alan Abelson
Barron’s Monday, February 7, 2005
http://online.barrons.com/article/SB110756195448446598.html
Anecdotal but true: Many who are collecting benefits already have new jobs. Just in last year one of my neighbors did just that. He was laid and decided to go into business for himself in graphics design. within six months he was making more than he did before. Another neighbor did same thing, but wasn’t collecting benefits. He got laed off and turned his part time Vitamin selling business into full time. Never plans to work for some one else again. A few years back I hired a guy contract who was still collecting benefits. I paid his “consulting” company, not him. For every person who gives up, there is some one who never reenters traditional work force. Do we have any way to count these people other than househild survey? That’s why it shows better results.
A few anecdotal stories of people cheating the system hardlty explains a drop of several million people from th labor force.
This is really scary stuff. And can someone explain to this grandmother, trying to learn something about economics, why the stock market responded to this news by going up?
Logic tells me that if people don’t have jobs, are afraid they will lose their jobs, or are underemployed, sooner or later they will stop spending. So how are they going to be able to buy the products wall street is selling?
Ahhh, but the expectation is that the Fed will be under less pressure to raise interest rates — at least that’s the rationale
In the market, sometimes Bad is Good, until it becomes really Bad. Then its Bad, until it becomes Terrible. Then its Great!
I’m so tired of economists throwing up their hands and scratching their heads over the employment data. Take another look at the models, is right.
It seems reasonable to study the correlation between two things heretofore unpredented, globalization and outsourcing, and the post recession weak job market. Month after month, this isn’t mentioned. There are tons of IT jobs in low cost India, and manufacturing galore in China. Duh.
Do you discount the birth death adjustment? If the government statisticians cannot get the raw numbers remotely right from month to month, what business do they have adjusting them by hundreds of thousands of jobs based on what some computer model predicts was omitted from their sampling?
Whether they are “right” — meaning accurate versus precise — is statistically irrelevant.
As long as the methodology remains fairly consistent, we have a base line of which to compare and contrast.
Think of it this way: It really doesn’t matter if your bathroom scale is accurate; But as long as its precise you can tell whether or not you are gaining or losing weight, relevant to a prior known measurement.
It’s good clean fun to rip on forecasters. But the “average miss” in Abelson’s chart was -16, not 98. 98 was the average of the errors in absolute values, and you’d have to be perfect to have that number be zero. The little t-test function in Microsoft Excel says that the probability of the actual numbers and forecast numbers being drawn from the same distribution was 66%. That’s not bad. Is anyone out there doing a better job?