Seems like with spring in the air, everyone is happy and festive about the jobs report we saw yesterday. But don’t take my word for it, look here. Any signs of weakness were just glossed over or brushed aside. Kind of like the schoolgirl who has it firmly rooted in her head that her middle/high school crush likes her back, but her friends aren’t so sure and they don’t know how to tell their friend this.
So in that vein, I found the following data points which are uniformly bad (from the Employment Situation release) in a report that everyone wants to cheer:
- The number of long-term unemployed (those jobless for 27 weeks and over) increased by 414,000 over the month to 6.5 million. In March, 44.1 percent of unemployed persons were jobless for 27 weeks or
- The number of persons working part time for economic reasons (sometimes referred to as involuntary part-time workers) increased to 9.1 million in March. These individuals were working part time because their hours had been cut back or because they were unable to find a full-time job.
- About 2.3 million persons were marginally attached to the labor force in March, compared with 2.1 million a year earlier. (The data are not seasonally adjusted.) These individuals were not in the labor force, wanted and were available for work, and had looked for a job sometime in the prior 12 months. They were not counted as unemployed because they had not searched for work in the 4 weeks preceding the survey.
These three data points taken together point to an underlying structural dynamic that is not positive, but is a ways off in showing itself too. Duration of unemployment and a downshifting of employment expectations isn’t something you’ll see in a week, a month, or maybe even in a year. These are things we won’t feel the full effect of for several years. GDP will be lower. Wages will be lower. Standards of living will not improve at the same rates we’ve been accustomed to. It’s baked in now, it’s just going to take time for the effects/symptoms to show themselves. It’s ingrained in our collective psyche to not manage our affairs until symptoms are full-blown and nearly out of control.
But still, the print was +162,000. We should be happy about that, right? But the unemployment rate stayed at 9.7% – unchanged from February. Some folks were wondering how we could have positive jobs growth but the unemployment rate stayed the same. As I pointed out in a tweet to Heidi Moore:
The estimate was a 162,000 increase in jobs while the population’s growth was estimated to be 161,000. So it was a wash. This is why we can add 200,000 jobs a month and it won’t get us anywhere: it’s equal to the population’s estimated growth each month.
You’ll notice I used the word estimate a few times. It’s because we don’t actually know from month-to-month whether we added jobs or not, but we can take surveys and collect samples and run statistics to give us an indication – a clue (Vanna, I’d like to purchase a clue for $500, please). This table at the Bureau of Labor Statistics is handy because it tells us whether or not the estimates they produce are significant (i.e. can be used as an indicator). I took a look at the headline number here:
The column I look at is Minimum significant change which directly feeds the next column, Passes test of significance. You’ll see in this case it does. But what does that mean? It simply means we know that jobs were created – the number was positive this month. Certainty around the estimate is a whole other issue. There would have to be a separate test done to see if, in fact, 162,000 jobs were created.
Another thing: in addition to the survey of businesses, there is in fact a model running behind the scenes. It’s called the Birth/Death model and it’s used to estimate the number of jobs created each month from new businesses either being created or destroyed. They use it because short of having armies of researchers traipsing across the country and plugging all business data (establishment, sale & bankruptcy) into something akin to The Matrix, there’s really no other way to get this stuff in the time frames we want to get it. I decided to spare everyone the wrist-slitting details, but there’s plenty to to your sharpen knives with over here should have the desire to do so. Here’s what the model did for jobs estimation in the first three months of the year:
I’m going to leave you with one chart: it’s the net adjustment due to the model, which either adds or subtracts jobs from the Not Seasonally Adjusted (NSA) numbers. It peaks in the spring/summer, and then we get massive negative revisions in January. Like clockwork. You can say it’s like a metronome. Only creepier…
So this whole process far from perfect. I wouldn’t go far as to call it a ‘Random Number Generator‘ but the uncertainties around data collection and estimation make it a noisy data series to say the least.
But back to our schoolgirl and her crush. Does he, in fact, like her back?
Who knows. She seems to think he does, and it looks like that’s all she needs…