How Bad Data and Seasonal Adjustments May Have Prolonged the Jobless Recovery

The little things matter. As liberating as it may be to be told to not sweat the small stuff, you take that advice at your peril.

For instance, we continue to wrestle with the implications of big data. By big we tend to mean that it is so voluminous that we can't worry our pretty little heads about anything other than the big picture the big data is painting for us.

So we design econometric models to normalize and adjust and smooth to rid us of all those annoying anomalies that make for those juicy quick insights and sound bites that the media who pay thought leaders homage and the clients who pay bills so love.

But, as the following article explains, some of those pesky speed bumps turn out to offer useful warnings. One of the reasons why hope continued to quite literally spring eternal for the past five years of the jobless recovery is that the US Bureau of Labor Statistics developed models that extrapolated from a limited data set. That set suggested that there were seasonal adjustments in the destruction and creation of jobs. Building that assumption into future expectations was not unreasonable, but it was incorrect.

So why should anyone care? Because those false assumptions guided public policies. Which, in turn, may have prolonged the recessionary effects of the jobless recovery. And all thanks to a detail that many thought too unimportant in the great sweep of data-drive history. Measures do matter. JL

Matt O'Brien comments in Economist's View:

You know something is really boring when economists say it is.

That's what I thought to myself when the economists at the Brookings Institution's Panel on Economic Activity said only the "serious" ones would stick around for the last paper on seasonal adjustmentzzzzzzz...

... but a funny thing happened on the way to catching up on sleep. It turns out seasonal adjustments are really interesting! They explain why, ever since Lehmangeddon, the economy has looked like it's speeding up in the winter and slowing down in the summer.

In other words, everything you've read about "Recovery Winter" the past few winters has just been a statistical artifact of naïve seasonal adjustments. Oops. ...

The BLS only looks at the past 3 years to figure out what a "typical" September (or October or November, etc.) looks like. So, if there's, say, a once-in-three-generations financial crisis in the fall, it could throw off the seasonal adjustments for quite awhile. Which is, of course, exactly what happened. ...

And that messed things up for years. Because the BLS's model thought the job losses from the financial crisis were just from winter, it thought those kind of job losses would happen every winter. And, like any good seasonal model, it tried to smooth them out. So it added jobs it shouldn't have to future winters to make up for what it expected would be big seasonal job losses. And it subtracted jobs it shouldn't have from the summer to do so. ...

Now, the one bit of good news here is this effect has already faded away for the most part. Remember, the BLS only looks back at the past 3 years of data when it comes up with its seasonal adjustments -- so the Lehman panic has fallen out of the sample.

Here are two words we should retire: Recovery Winter. It was never a thing. The economy wasn't actually accelerating when the days got shorter, nor was it decelerating when the days got longer. ... The BLS can, and should, do better.