Economombo 3

The last Economombo article reflected on the inadequacies of economists. Certainly, they have gotten quite enough wrong. However, the last statements about how business seemed to be taking a turn for the worse have, in fact, turned out otherwise. Our business has gotten better, and the general outlook has turned positive.

It takes a big man to admit when he’s wrong, and I’m not that big. Taking disparate data and forging a prediction from it is fairly ambiguous at best, and flat out random at worst. I won’t be happy until all our economist friends publish all their predictions in historical format, and line them up with what actually happened. That is, if they can agree on what actually happened.

Which brings us to the heart of today’s article. Counting. Simply counting. Sounds easy enough, because it’s something you’ve been doing before you can remember. How many fingers? How many toes? There’s a good chance you’ve been counting since kindergarten, and for you math whizzes out there, you may have been counting since you were three. Maybe four.

Here’s two examples of things that look like numbers, but really don’t count anything. The first is the stock market indicators, like the Dow Industrial Average (DIA), or the S&P index. Both are mentioned by news sources throughout the day. Both are something that you might pay attention to occassionally, especially if you have money in the market. But these aren’t numbers – they don’t count anything.

Back in the mists of time, the market indicators started as a simple count of what the stocks were trading. You took their price, added all the stocks that you were interested, and there was your number. Today there are thirty stocks in the DIA. But they don’t simply get added together. As time went by, some of the stocks split. Some companies dropped out of the average, others were put in their place. Not all the values were the same. So what the DIA managers do is multiply every stock by some special number, a ‘weight.’ This weight adjusts the overall numbers so that they supposedly reflect the economy, and don’t change so much every time the DIA basket of stocks is adjusted.

The fundamental fact is that the DIA is a made up number! It doesn’t count anything! You can’t compare the DIA to anything else in the universe and have it make sense! Go ahead and try.

The second example is that of unemployment. An economist may have countered our first example by saying that the DIA is not an accepted economic measure. A much harder number for them to argue against is unemployment. Many many economists use unemployment in their predictions, in fact trying to predict it as well on a regular basis.

The problem with unemployment is that it’s an interpreted number. Interpreted, you say? Yes, interpreted. There is no direct number called unemployment. The way the experts get to a final number, say this month’s 7.6%, is by contacting so many households every month. They ask these households a number of questions. Central to these questions is this; are there household members looking for work, that don’t have a job? If the answer is yes, then they are unemployed!

The bad news is that, just perhaps, that person no longer looks for work because they haven’t worked in a year. Or, perhaps, that person is looking for work, but still also works two other jobs. In any case, there are a number of other factors that have to be taken into account to count someone as unemployed. But wait, there’s more!

We’ve counted people who are unemployed, but what about those that are employed. Turns out that you can use the same households to figure that out, in combination with all the numbers reported by employers throughout the country. Every month, employers get survey questions that they have to fill out. All these numbers, then, are mashed together, or interpreted, so that we come out with a certain number of people who are employed.

Again, like before, there’s some bad news. The number reported by employers can be a bit old. There may be people who are employed by the company that are also working another job. It could be that the employer is slightly under-reporting their numbers so that the government doesn’t come looking too closely. And it may be that things have changed since the employer reported the numbers to when someone at a government desk gets around to reading their survey.

The final interpretation is this. Take the number of people in the household survey and figure out a percentage, and a range of accuracy. That’s statistics. Then take all the people who are working, compare the survey number to what employers report, and make an adjustment. Then take the first percentage and adjust it so that its denomenator matches the numbers of total employed. THEN adjust the whole thing based on any seasonal or other cycles there may be in the time series. That is what I call an interpreted number.

Again, there is no simple item that we can point to and say that this number, unemployment, is counting accurately. It’s impossible because unemployment is an interpreted number. What can we do to improve the situation?

We can count. We can count something that really exists. Something that exists, and doesn’t vary whether or not someone decides to keep looking for work, and doesn’t vary if someone works a ½ job, or 3 jobs. We call this number participation. It counts the actual number of people who work. And if you combine this number with the number of people who can work, everyone between the ages of 18 and 65, for example, you get a very stable number that we call the participation rate.

So, remember, when you hear the stock market report, or hear an unemployment number, don’t fret. It’s meaningless. Take a look at the participation rate instead. [1] And draw your own conclusions.

[1] This link should take you to the US Department of Labor’s Bureau of Labor Statistics. They do a great job of collecting and reporting all the numbers. Now, if they could only tame those economents.