Mixed Signals: Be Careful With Statistically Adjusted Statistics

“January figures can be particularly hard to predict, economists say, because seasonal adjustment factors play a big role.”

Experts, and others, were amazed at the January figures released last week relating to the number of jobs added to payrolls in January.

The Labor Department reported last Friday that employers added 517,000 jobs to their payrolls.

Wow!

How, could the economy add so many jobs when almost everyone is asking the question, “when is the recession going to begin?”

Well, because there is so much seasonal variation in some statistical series, agencies, like the Labor Department make adjustments to these series in order to smooth out bumps in the data that mainly come about due to how the season of the year impacts what happens to the series.

Seasonal adjustment is supposed to make things more understandable.

Seasonal Adjustments

Seasonal adjustments are constructed with historical data. Statistical agencies may go back years in order to construct the “best” adjustments possible.

The effort is to make the series as reliable as possible so that analysts can better interpret movements in a statistical series and better determine what is happening in that particular area of the world.

The underlying assumption of these seasonal adjustments is that things stay the same…or, at least, perform in a relatively consistent way, year-over-year.

And, that is a huge assumption!

Especially in a time period when things are changing…many times quite dramatically.

This is why some analysts will only use year-over-year results. Year-over-year results tend to take away some of the seasonal impacts that take place without introducing a statistical distortion into the equation.

Whenever I can, I will stick with year-over-year numbers for my analysis. I don’t want to have to incorporate what some statistician has done to the numbers when trying to understand a movement in the data.

For example, the last three years have seemingly altered almost everything going on in the economy.

Using seasonal adjustment factors, especially at this time, seem to just cloud up our understanding of what is going on, rather than help us with the analysis.

For example, Austen Hufford, in the Wall Street Journal tells us that

“On an unadjusted basis, U. S employers shed 2.5 million jobs in January. A year earlier they shed 2.8 million.”

Mr. Hufford adds that

“Nela Richardson, chief economist at payroll processor Automatic Data Processing Inc., said seasonally adjust figures might be skewing true results because the current period could be different from the pre-pandemic economy.”

“The seasonal adjustments are based on models developed over many years.”

In making the seasonal adjustments,

“Everyone has to tinker with the engine a little bit, to make sure you are getting a true reflection of what’s going on in the economy.”

Mr. Hufford adds to this,

“Wrong for now.”

Furthermore, the statistics get revised, for many reasons other than just seasonal adjustment.

“The Labor Department considers the latest figures to be preliminary and will revise them in the next two monthly reports.”

“Then, once a year, the department uses an expanded data set relying on tax records to more finely tune its estimates. It releases that update each February, alongside January numbers.”

And,

“The revisions can be large. For example, a year ago, the Labor Department initially estimated 467,000 jobs were added in January 2022, on a seasonally adjusted basis, but after the latest revisions the gain was cut to 364,000.”

At least, using the year-over-year comparison, one does not have to deal with the inconsistencies built into the seasonally adjusted numbers when dealing with the yearly revisions in the basic data series.

Things Have Changed

The important thing is that as the pandemic spread and supply chain problems evolved along with major changes in the technological makeup of the economy, the seasonal behavior of many important statistical series has been distorted.

Yes, these changes have “muddied up the waters” surrounding the statistics and this makes it harder for us to “read” the statistics, but looking at non-seasonally adjusted figures does not introduce “human-made” errors that can even further distort our reading of the situation.

This is the nature of the world, however, and we humans have to deal with it as best we can.

We would like to “deal with it” without adding further problems with the data.

The employment numbers that came out last week had a major impact on what investors did in the stock market.

If the numbers released last week overstated the strength of the economy, then many investors “place a bet” on the basis of incorrect data.

One of the major problems investors are facing right now is all the conflicting data that are coming into their knowledge.

Why does the labor market look so strong when many other areas of the economy look so weak?

Why are employers adding so many workers to the work force when so many other statistical series are showing that the economy is declining?

Industrial production has declined for quite a few months now, and the capacity utilization of manufacturing firms has also undergone quite a number of monthly declined.

The term structure of interest rates is negative, another sign that a recession is on-the-way and the inflationary expectations built into financial markets right now have dropped close to 2.0 percent.

The Economy Is Messed Up

For quite a few months now, I have been writing about how “out-of-sync” the economy seems to be and what this means for the future.

The economy is “out-of-sync” because the world is “out-of-sync” and no smooth “return to the normal” is likely to take place.

If the economy is “out-of-sync” then it will be true that the many disequilibrium situations created by the pandemic, the Covid-19 recession, and the Federal Reserve’s attempt to reverse the asset price bubble it created will, at one time or another, have to be “worked out.”

Unfortunately, having to work with statistical series that are constructed with seasonal adjustment tools that are meaningless in the “new” environment only makes the task more difficult.

In essence, the statistical data we have to work with just add to the confusion.