The Bureau of Labor Statistics (BLS) and the Gallup Survey people both generate numbers to help us understand the employment/unemployment situation. Unfortunately, they often present a different picture. Typically the BLS data presents a rosier picture than the independently surveyed Gallup numbers. However, like the previous two summers, the gap between the BLS and the Gallup numbers closed for July 2016 with the BLS and the Gallup numbers coming in identical. The numbers for the month of August showed the gap returning with the BLS saying the Unadjusted U-3 Unemployment rate was 5.0% while Gallup said it was 5.4%.
For January 2017 Gallup said unadjusted U-3 unemployment was 5.8% while the BLS said it was 5.1%.
Comparing Bureau of Labor Statistics numbers to an Independent Source
There has been a lot of talk about the validity of the government generated unemployment numbers created by the U.S. Bureau of Labor Statistics (BLS) and what is the true unemployment rate. Generally, we look at the Employment numbers rather than the Unemployment Rate because they are much more accurate. We’ve looked at Employment vs. Unemployment to see how they compare and we’ve looked at U-6 (total labor force including those who’ve given up looking) vs. U-3 (those who are still actively looking). The U-3 unemployment rate is the commonly quoted one. But the one problem is that all that data comes from the government. If they are fudging the numbers how would we know? Unless as we’ve noted before there are inconsistencies between the Unemployment and Employment Charts. But we do have an alternative source of information.
In an effort to determine the True Unemployment Rate the Gallup survey people began doing their own survey on unemployment rates back in 2010. So we can compare their results with the results the BLS publishes.
In this first chart, we have BLS U-3 Unemployment rates (both Seasonally Adjusted and Unadjusted) along with the Gallup Unadjusted Unemployment rate. In this series, Gallup is using similar criteria to the U-3 so we can compare apples to apples. Note that in a few months in Summer 2013 and the Summer of 2014 the red line converged with the green line but mostly beginning in 2013 the BLS has been severely low-balling Unemployment numbers compared to the independent Gallup numbers.
In a perfect world with unbiased information, the two lines should be identical. In the real world, you might expect some minor variations but they should track closely. Lately, however, they have diverged drastically once again. So for most of 2015 and through the first half of 2016 , there was a major divergence between what the BLS was saying and what Gallup was saying.
In the beginning of this jumble, it is hard to determine whether the BLS numbers are providing the True Unemployment Rate or not. We can see that the Gallup numbers start out higher at the beginning and are also higher frequently throughout but there are also a few points where they are actually lower so it is hard to determine whether the BLS is fudging or not. But after 2013 the BLS numbers appear to get further and further from reality.
According to Shadowstats, the government is really underestimating unemployment by even more than our numbers suggest since ” long-term discouraged workers were defined out of official existence in 1994.” The new U-6 numbers only include short-term discouraged workers.
Next, let’s look at only the Unadjusted Unemployment Rate. I always prefer to see the data before the BLS “adjusts” it for “seasonal” reasons. So we will look at the BLS and the Gallup Unadjusted numbers for Unemployment.
True UnAdjusted Unemployment Numbers according to BLS and Gallup
In this chart, we can see that the Gallup numbers are generally higher than the BLS numbers but in the beginning, they were occasionally lower as well, so it was difficult to tell if the data is significantly different. They appear to track pretty well until July of 2013 when they start diverging drastically. The BLS numbers seem to take a break from reality as they continue to fall while Gallup numbers actually move higher. But then in July and August of 2014, the numbers become very close… as they should be. But reality doesn’t last long and in September the BLS got back to their old tricks and went down sharply while Gallup rose and went in the opposite direction. By January 2015 the gap returned to one full percentage point. But in July of 2016 the BLS numbers and the Gallup numbers converged once again.
Let’s look at the actual data.
|BLS Unadj.||Gallup Unadj.||Diff.||Gallup Higher||BLS Higher|
|BLS Unadj.||Gallup Unadj.||Sum 36.2%||Ave.
In the fourth column marked “Difference” I’ve subtracted the BLS number from the Gallup number. This will result in a positive number if the Gallup number is higher and a negative number if the BLS number is higher. Theoretically, if the data collection methods are equal and the difference in the numbers is just based on random gathering differences, the Gallup number should be higher 50% of the time and the BLS number should be higher 50% of the time. Also, the amount of difference between the numbers should be equal.
So What are the Results?
First of all, we find that out of 86 data pairs the BLS number was higher only 17 times and the Gallup number was higher much more often at 66 times and only three times were the results the same. This definitely sounds like the BLS numbers are lower for some reason other than random chance. Next, we look at the average variation and we see that when the BLS is higher the average variation from the Gallup numbers is only 0.318% but when Gallup is higher the average difference is 0.6303%.
So not only do the Gallup numbers come out higher more often, the amount of difference is higher as well. From the table, we can see that when you add up all the negatives with all the positives the difference is 36.2 percentage points. I’ve been accused of including this “nonsense number” even though it doesn’t really mean anything. But if the methods were equivalent you would expect the positives to cancel out the negatives and the total would be zero. So the fact that this number is “non-zero” does indicate that this is an ongoing situation with the BLS continually underestimating the Unemployment rate compared to the Gallup numbers.
There is one major difference in the calculation of the BLS numbers and the Gallup numbers and that is the age Gallup considers employment for those 18 and up while BLS considers those 16 and up. This could account for the difference in the calculations except that the unemployment rate among teenagers is way above that of the general populace so rather than explain it, it actually makes the BLS numbers even further off base.
The True Unemployment Rate– Conclusion
Although up until recently the difference wasn’t massive it does appear that the BLS data is biased to the low side compared to the independently surveyed Gallup numbers. The average amount the BLS numbers come out below the Gallup numbers is roughly 0.6303% and 76.7% of the time the BLS numbers come out lower so in other words if the BLS says the unemployment rate is 5.0% approximately 75% of the time Gallup would say the True Unemployment Rate was really 5.63%. But on several occasions recently the difference was significantly more than 1%.
But this does not take into consideration the other major problem that most people cite when they are concerned about the true unemployment rate and that is all the people who have stopped looking for a job. For more information on the people who have stopped looking, you need to look at the U-6 unemployment rate. See: What is U-6 Unemployment? Also recently the FED and various economists are saying that we are at or near “full-employment” see Is the U.S. Really at “Full Employment”?
There is also some evidence that a factor like Obamacare is causing a shift in the number of part-time employees (reducing the number of hours worked per employee) so the number of part-time workers necessary is increasing. See Unemployment, Part-time Workers and Obamacare, See also Labor Force Participation Rate.
Source: US Bureau of Labor Statistics and Gallup Pollsters.
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