Archive for October 12, 2012

BLS Numbers: The Case For and Against Paranoia

At first, I thought the BLS numbers for September were weird. I thought about commenting on them, but I’m very slow in writing blog posts (what with trying to understand things and run the numbers and all) so I thought the moment had passed.

But then I saw Mickey Kaus’s post on “The Case for Paranoia” and I thought maybe I should add my 2 cents.

But first, I want to put forth my position. I hear a lot about how conservatives lack basic empathy, but I’ve been pretty frustrated at how liberals lack basic empathy over the results to this recent jobs report. Empathy is the art of seeing through the eyes of another human being, and it is a beautiful art… possibly the only true art. What I want to do here is to aid empathy. Why would someone be skeptical of the BLS numbers? Would they have any good reasons? And why might that skepticism be unwarranted?

The Case for Skepticism

If you look at this data one way, it actually looks very weird, very out of place. In September 2012 according to the BLS, the unemployment rate dropped from 8.1% to 7.8%, due largely to an increase of 873,000 jobs (as measured in the “A” Tables, which are based on a survey of individuals). However the “jobs increased” number (as measured in the “B” tables, which are based on a survey of business payrolls and is the number commonly reported) was only 114K, which is a pretty weak number. After all, just to keep up with population growth the job increase needs to be 125,000, right? So how can unemployment decrease so dramatically when the jobs number didn’t even keep pace with population growth?

Let’s look at every time in the history of modern job growth (since 1948) when we’ve seen +800K jobs increase in a month.


Something looks kind of weird here. In the last 18 years (not counting September 2012), the only times we’ve seen +800K job growth has been during January. Why is that?

It turns out every January, the BLS (Bureau of Labor Statistics) re-aligns the data to conform with population increases. So we may see employment increases that are augmented by population adjustments (and may not actually be “real” increases). So let’s take those out.


We’ve had such a huge non-adjustment employment increase only 6 times in the last 70 years. And, with those other increases, did we have similarly large corresponding “payroll jobs increases”?

It turns out this last September was the ONLY TIME IN THE HISTORY OF BLS DATA that we had an “employment” increase this large where the “payroll” increase didn’t even meet population growth.

In fact, since 1950, every single +800K employment gain has been joined by a +300K payroll gain. Outside of the census hiring in May 2010, we haven’t seen such healthy monthly payroll growth for any month since 2006.

You could even go a step further. This is also the first time we’ve seen an employment growth number this large that wasn’t preceded by 3 months of solid +200K payroll growth. So we’re looking at a fairly weird number here.

And this number, this number that is unique in the history of BLS numbers and is beneficial to the incumbent administration, just happened to come out just in time to influence an election that depends heavily on jobs numbers.

So, even if you don’t agree, I hope you can see why some people are skeptical of this jobs report.


The Case Against Skepticism

We have to keep in mind that there are 2 surveys that look at job growth. Think of the “B” tables as some guys calling employers and asking “how many people do you have on payroll?” Based on that number they come up with the “job growth” data. In September, that was an increase of 114,000 jobs. Very weak.

But you can’t call a company and ask “how many people do you NOT employ?” so to determine unemployment, they call individuals and ask “are you employed or unemployed”? This survey becomes the “A” tables and they take number of people looking for work, divide it by the number of people unemployed and get the unemployment rate.

Because of this, the two numbers (payroll increases in the B Tables and employment increases in the A Tables) can differ greatly. You could call all the companies in the Fortune 500 and ask how many they employ and cover millions of jobs. But if you call 500 individuals, that’s such a small sample size, it is basically meaningless. So the “A” tables (individuals) has a higher margin of error.

And we see that margin of error if we look at the data a little more holistically.


For those of you who (heart) some numbers, the standard deviation for the A Tables is significantly higher than the standard deviation for the B Tables (293,000 for A Tables vs. 209,000 for B Tables). This means that we’ll see a higher level of variability in the A Tables (the 873K job number) than we see in the B Tables (the 114K job number).

But looking at this data in this way, we see a couple things:

1) The September jobs report is a CLEAR outlier. It is totally reasonable to raise some eyebrows at this.

2) When we look at all the data, and not just pare it down to a few data points like we did above, we can see the September jobs report isn’t enough of an outlier to be considered unique. It could very easily be an artifact of randomness. The randomness just happens to fall  in a way some people don’t like.

And that second position is where I am. There is a lot of variability in the jobs data, especially the A Table employment data. Add into this the fact that we saw a lot of part time jobs added (600,000 of the 873,000 increase was in part time jobs) at the same time that we saw some major employers announce a shift to part-time workers in response to Obamacare and we see that maybe this report isn’t some conspiracy, maybe it is actually telling us something about the changing status of employment in the country.

UPDATE: Conn Carroll points out that part time jobs as a whole did not increase by 600,000, but instead fell by 26,000. What increased by 600,000 was the number of people working part time “out of economic necessity”, but that shouldn’t have influenced the overall job number. Only the overall number of part time workers should do that.


Is it a weird jobs report? Yes. But it isn’t unique in its weirdness and there are some very important extenuating circumstances that help explain it.

I’ve been following jobs reports very carefully for about 3 years. I’ve run through the historical numbers dozens of times, looking for averages, estimates, trends and patterns. For what it is worth, I don’t see anything that would suggest any kind of conspiracy or number tampering.

I know the numbers well enough to say that this was an odd report and I wish others would give the BLS skeptics a little bit of slack. This was a weird report, no doubt about it. But an understanding of how the report is compiled and a little bit of exploration shows that this report wasn’t so weird as to warrant particular skepticism.