Some good economic news!

The employment rate is plunging faster than a hundred kilogram bolide entering the atmosphere at a normal angle to the plane surface of the Earth [thank 49er!].

Manufacturing is down over a million, with Construction close behind. Plain Business and and Retail are close behind, each losing hundreds of thousands. Even the Hospitality sector lost over a quarter million slots.

But, lo!, do not despair! For there is at least one area that has seen an increase in the number of jobs.

That area? Government.

According to the Labor Department, the Drain To Our Wallets Sector increased the number of luke-warm bodies it employs by about 170,000 since December.

Problem with that number, and all the numbers the Labor Department issues, is that they are “seasonally adjusted.” Which means that the numbers aren’t real numbers, they are outputs from some statistical model. We don’t know what the actual numbers are.

What happens is that unemployment usually decreases in the first part of the year, due to things like retail letting go of the temporary workers it hired for the Federally-Recognized Holiday of December 25th rush, etc. The opposite is also true: employment grows in the weeks leading up to December.

The Labor Department, and economists elsewhere, don’t like to see these dips and doodles and so they apply a statistical filter that brings the unemployment rate up when it usually goes down, and down in when it usually goes up.

These are weird statistical models. They take as input the real numbers and spit out not-real numbers. Economists worry people won’t understand the natural up- and down-swings in unemployment and so massage the real numbers to make them less variable, and, presumably, more calming.

Anyway, the real number of bodies tossed onto the street is different than the numbers you read in the press. I don’t know by how much. Just something to keep in mind.

Point is, it’s not all doom and gloom out there. The government is growing, and that must be a good thing, right?

Categories: Culture

11 replies »

  1. It is the best of all possible worlds. The government stimulus package aims 39% at state and local governments so they don’t need to reduce their size. The State of California’s budget calls for 9,000 fewer new students this year, rather than reduce entitilements or its own size. But I am happy and content. All three of my children either work for the gov or get their funding from the gov.

  2. Hey, every job I’m currently looking at is either government or a public agency of some sort.

    I ain’t complaining! 🙂

  3. The purpose of the seasonal adjustment factors is to remove the seasonality from the data so that the trend can be observed. The not seasonally adjusted numbers are also available so you can observe the actual numbers as well. If you compare the over the year change of the seasonally and not seasonally adjusted numbers they come out to be the same.

    I don’t think the seasonal adjustment factors are controversial. If you want to open a big can of worms, look at the birth death factors! There is where you will find real controversy.

  4. Tom,

    It’s true that seasonal “adjustment” is not controversial, but this is what I question. It should be.

    You can certainly see a trend in non-adjusted data. The adjustment they use also assumes that any trend is linear, which obviously needn’t be the case.

    Really, the adjustment is an attempt to simplify the data to avoid explaining that, for example, “At this time of year, the rate often goes up” or down or whatever. This simplification comes at the price of trusting that the model used to adjust is perfectly accurate.

    And, lately, I’m less trusting of other people’s models.

  5. I think you are tilting at windmills a little with this objection to seasonally adjusting data.

    The data invariable appear right alongside the raw data and I can’t think of one instance where the data are represented as something they are not.

  6. Geckko,

    You might be right; I might be too touchy.

    But I don’t seem to have your luck. I rarely, if ever, see the actual raw data printed with the adjusted. At the least, you have to work very hard to find it. I have never, for example, seen it show up in the Wall Street Journal.

  7. At the very least, seasonally adjusted numbers for job loss in January would show a lower number of jobs lost then the actual raw number. The unadjusted number would look worse since thousands of seasonal jobs end in January.

  8. The argument for using seasonally adjusted data in some instances goes something like this.

    A: Our data indicate that the labour market deteriorated alarmingly in August with 100,000 fewer jobs than reported in September.

    B: Don’t go making alarmist statements like that, employment always fall in August because temporary summer jobs end and the workers go back to school.

    A: Yes, but last year for example employment only fell by 70,000 in August, the fall was relatively large this time so we should be alarmed by this apparently extra 30,000 jobs lost.

    B: Aahh, but in July there was a larger increase in employment than there was last year. All we are seeing is a larger number of temporary jobs being created and then ended.

    A: But the change in July was affected by the change that occurred in June, which appeared unusual.

    etc etc. etc.

    Nothing sinister or wrong in wanting to sift any useful month-to-month or quarter-to-quarter information out from data.

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