Who's Your Data? The Myths and Misnomers of Unemployment Figures

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By now you probably know that data is a group of informational bits, facts, figures, ideas, quantities or patterns in behavior, collections of samples or a group of scientific measurements collected to show informational bits, facts, figures...etc. etc.

Yeah but did you know data is the plural of the Latin word datum?

Oh you didn’t care?

Okay I didn’t either until I found out. And it turns out that datum is seldom used because 1) The word data is so pervasive and 2) Datum by itself is boring and inconclusive. There’s nothing to compare a single datum to except other data.

You can’t have data without datum but without data, datum doesn’t make sense.

The same is true for analyzing that precious unemployment and labor data that comes out every week. Sure it may be enough for some to look at a fluctuation and see how many made a claim on lost jobs and look at the exact number the U.S. Department of Labor came up with to determine if it’s time to jump off a bridge or not but without context or comparative data more than "400,000 new jobs added" means jack sprat.

Case in point, with the latest unemployment figures that came out the week of May 30, we found that more than 400,000 new jobs were on the books. Good right? Well, turns out that some 360,000 of them were a result government jobs — temporary U.S. Census-related jobs to be exact.

More numbers will be out for the first week of June by the time you read this. And they will be crunched into contexted, rounded, shaped, seared, marinated for your taste buds.

Thus the truth, that you will hear nowhere but here, is that ambiguity is more useful in measuring true unemployment than any actual pinpoint numbers. But that’s too complicated isn’t it? It’s bad enough that some of us can’t find a friggin’ job so don’t go mucking with our data!

The reality is that because of the different classes of employment (skilled labor, non-farm labor, farm labor, seasonal, professional) and different types of unemployment (cylical, structural, classical, frictional) we can derive no stone or iron-clad figure but only make a guestimate that is based on unemployment claims-divided-by- workforce-equals-rounded-up-or-down unemployment rate.

Whew!

But these figures that are released every week to the delight or consternation of prognosticators, traders and financial market dilletantes, are really bogus in making any real predictions or decisions.

Why?

Well, because as this Wall Street Journal opinion joint points out, there are about six different data sets based on six different definitions of unemployment — that’s tons of data made of each individual datum if you will — which, given different variables, can put unemployment at as high as 16% but as low as 6% rather than the 9.7-9.9% range that it is these days.

This list of six read like German submarines and Irish rock bands:

• U1: Percentage of labor force unemployed 15 weeks or longer.
• U2: Percentage of labor force who lost jobs or completed temporary work. (Bono is employed though.)
• U3: Official unemployment rate per ILO (International Labour Organization) definition.
• U4: Workers who are fed up and not filing claims anymore or have had claims expire.
• U5: People who picked up odd jobs and want to work and are available to work but realize they make more by filing unemployment or working in cash heavy businesses than they do by working at Taco Bell or Home Depot. These are defined as those who ”would like” to and are able to work, but have not looked for work recently per their state department of labor claim filing criteria.
• U6: Part-time workers who want to step their game up but can’t because no one in their field is hiring full-time, the pay is not good, or the market is too crowded and therefore they don't try. Part-time is better than no time.

Based on these different definitions, one could go by many measures that incorporate census and claim data that is often old, incomplete or inconclusive, or all three by the time Katie Couric relates it to us.

As Alan Reynolds, a senior fellow at the Cato Institute and a much smarter man than I , points out, statistical voodoo is often used to make a weak job market look stronger or a stronger job market look worse than it actually is.

Talk about mucking with the data.

Then the spin starts and the market reacts based on incomplete math and unfinished bits of datum and the news people report with long faces and gloom and the sad jazz music plays on NPR based on another down day on the market. This, ladies and gentleman is the crucial weekly data that the movements of our stock portfolios are based on and our overall psychological profile is based on monetarily. So often our micro decisions are made based on incomplete findings, skewed survey sample, focus-group findings, polling, varying sample sizes, and findings that are either contextually dubious or outright wrong. This is the case with U.S. Department of Labor Jobless claims numbers and it's this misinformation, half-truth and piece-of-Apple-to-describe-an orange data that shapes poltical funding, economic forecasts and all of the jaw-dropping stuff that's reported in commoditized accounts on a daily basis by various outlets.

Good thing you make your decisions based on actual facts huh? Well do you?