Is the Government Fudging Unemployment Numbers?

What is the True Unemployment Rate?

Comparing Bureau of Labor Statistics numbers to an Independent Source

BLS_vs_Gallup_Unemployment_3_Mar2016There 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 the media was going on about how the unemployment rate was nearing 5% while Gallup said it was really hovering above 6%.

BLS_vs_Gallup_Unemployment_3_Mar2016Click Chart for larger image

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

BLS_vs_Gallup_Unemployment_2_Mar2016Click Chart for larger image

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.

Let’s look at the actual data.

BLS Unadj. Gallup Unadj. Diff. Gallup Higher BLS Higher
Jan-2010 10.6% 10.7% 0.1% 0.1%
Feb-2010 10.4% 10.8% 0.4% 0.4%
Mar-2010 10.2% 10.4% 0.2% 0.2%
Apr-2010 9.5% 10.1% 0.6% 0.6%
May-2010 9.3% 9.5% 0.2% 0.2%
Jun-2010 9.6% 9.2% -0.4% -0.4%
Jul-2010 9.7% 9.0% -0.7% -0.7%
Aug-2010 9.5% 9.2% -0.3% -0.3%
Sep-2010 9.2% 9.6% 0.4% 0.4%
Oct-2010 9.0% 9.8% 0.8% 0.8%
Nov-2010 9.3% 9.2% -0.1% -0.1%
Dec-2010 9.1% 9.2% 0.1% 0.1%
Jan-2011 9.8% 9.6% -0.2% -0.2%
Feb-2011 9.5% 10.1% 0.6% 0.6%
Mar-2011 9.2% 10.1% 0.9% 0.9%
Apr-2011 8.7% 9.6% 0.9% 0.9%
May-2011 8.7% 9.3% 0.6% 0.6%
Jun-2011 9.3% 8.8% -0.5% -0.5%
Jul-2011 9.3% 9.0% -0.3% -0.3%
Aug-2011 9.1% 9.0% -0.1% -0.1%
Sep-2011 8.8% 8.8% 0.0%  —  —
Oct-2011 8.5% 8.4% -0.1% -0.1%
Nov-2011 8.2% 8.5% 0.3% 0.3%
Dec-2011 8.3% 8.6% 0.3% 0.3%
Jan-2012 8.8% 8.4% -0.4% -0.4%
Feb-2012 8.7% 9.0% 0.3% 0.3%
Mar-2012 8.4% 8.8% 0.4% 0.4%
Apr-2012 7.7% 8.3% 0.6% 0.6%
May-2012 7.9% 8.0% 0.1% 0.1%
June-2012 8.4% 8.0% -0.4% -0.4%
Aug-2012 8.6% 8.2% -0.4% -0.4%
Sept-2012 7.6% 7.9% 0.3%
Oct-2012 7.5% 7.0% -0.5% -0.5%
Nov-2012 7.4% 7.8% 0.4% 0.4%
Dec-2012  7.6%  7.7% 0.1% 0.1%
Jan-2013 8.5% 7.8% -0.7% -0.7%
Feb-2013 8.1% 8.0% -0.1% -0.1%
Mar-2013 7.6% 8.0% 0.4% 0.4%
Apr-2013 7.1% 7.4% 0.3% 0.3%
May-2013 7.3% 7.9% 0.6% 0.6%
June-2013 7.8% 7.9% 0.1% 0.1%
July-2013 7.7% 7.8% 0.1% 0.1%
Aug-2013 7.3% 8.7% 1.4% 1.4%
Sep-2013 7.0% 7.7% 0.7% 0.7%
Oct-2013 7.0% 7.3% 0.3% 0.3%
Nov-2013 6.6% 8.2% 1.6% 1.6%
Dec-2013 6.5% 7.4% 0.9% 0.9%
Jan-2014 7.0%  8.6% 1.6% 1.6%
Feb-2014 7.0% 8.0% 1.0% 1.0%
Mar-2014 6.8% 7.5% 0.7% 0.7%
Apr-2014 5.9% 7.1% 1.2% 1.2%
May-2014 6.1% 7.0% 0.9% 0.9%
Jun-2014 6.3% 6.8% 0.5% 0.5%
Jul-2014 6.5% 6.4% 0.1% -0.1%
Aug-2014 6.3% 6.3% 0%
Sep-2014 5.7% 6.6% 0.9% 0.9%
Oct-2014 5.5% 6.2% 0.7% 0.7%
Nov-2014 5.5% 6.2% 0.7% 0.7%
Dec- 2014 5.4% 5.8% 0.4% 0.4%
Jan-2015 6.1% 7.1% 1.0% 1.0%
Feb-2015 5.8% 6.7% 0.9% 0.9%
Mar-2015 5.6% 6.5% 0.9% 0.9%
Apr-2015 5.1% 5.9% 0.8% 0.8%
May-2015 5.3% 6.2% 0.9% 0.9%
Jun-2015 5.5% 5.9% 0.4% 0.4%
Jul-2015 5.6% 6.2% 0.6% 0.6%
Aug-2015 5.2% 6.3% 1.1% 1.1%
Sep-2015 4.9% 6.3% 1.4% 1.4%
Oct-2015 4.8% 5.6% 0.8% 0.8%
Nov-2015 4.8% 5.6% 0.8% 0.8%
Dec-2015 4.8% 5.6% 0.8% 0.8%
Jan-2016 5.3% 5.5% 0.2% 0.2%
Feb-2016 5.2% 6.1% 0.9% 0.9%
Mar-2016 5.1% 6.1% 1.0% 1.0%
 BLS Unadj.  Gallup Unadj. Sum 30.7% Ave.
0.6446%
Ave.
-0.318%

 

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 75 data pairs the BLS number was higher only 17 times and the Gallup number was higher much more often at 56 times and only two 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 0.6446%.

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 30.7 percentage points. If the methods were equivalent you would expect the positives to cancel out the negatives and the total would be zero.  So the BLS has underestimated the Unemployment rate by a cumulative total of 30.7 percent compared to the Gallup numbers from 2010 through the present.

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 0.6382% (Difference Average) so in other words if the BLS says the unemployment rate is 6.0% on average Gallup would say the True Unemployment Rate was really 6.64%. But on several occasions recently the difference was significantly more than 1%.

Date Difference
August 2013 1.4%
November 2013 1.6%
January 2014 1.6%
April 2014 1.2%
August 2015 1.1%
September 2015 1.4%

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?

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.

For more information on the true unemployment rate see Employment vs. UnEmployment.

See Also:   Job Growth Stalls, Labor Participation at 38-Year Low

Unemployment and Employment Charts

Source: US Bureau of Labor Statistics and Gallup Pollsters.

Does the Gallup Poll Provide the True Unemployment Rate?

 

 

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About Tim McMahon

Work by editor and author, Tim McMahon, has been featured in Bloomberg, CBS News, Wall Street Journal, Christian Science Monitor, Forbes, Washington Post, Drudge Report, The Atlantic, Business Insider, American Thinker, Lew Rockwell, Huffington Post, Rolling Stone, Oakland Press, Free Republic, Education World, Realty Trac, Reason, Coin News, and Council for Economic Education. Connect with Tim on Google+

Comments

  1. Syon Smith says:

    It is very difficult to ascertain the exact unemployment rate by just relying on U.S. Bureau of Labor Statistics (BLS) report. The uncertainties in right unemployment and employment figure have led the independent Gallup survey people to find out the True Unemployment Rate. The result shows that average Gallup numbers are 1% higher than BLS report, which is to the low side. Therefore, it is always wise to compare both Gallup survey and BLS report to determine true unemployment rate across the country.

    • Johnnny Dull says:

      I think people these days rely too much on “facts and figures” to guide their lives. Fact is that your personal situation and employment chances depend much more on the reality of your town/workplace and much less on what the national statistics say or even what the unemployment is within your state….

      • Johnny,

        You are absolutely right. If you are without a job it is much easier to find a job in a state with 5% unemployment than in a state with 10%. But once you have a job, if you get your work done faster so you can help others it will put you in the top 25% of job performers so they would have to lay-off 3/4 of the workers before your job was in jeopardy. Thus your individual likelihood of getting laid-off is virtually non-existent and the likelihood of getting a promotion is much greater. So doing a good job goes a long way toward job security.

    • I don’t think the U.S. government would intentionally “fudge” the unemployment numbers, although certain local municipalities might provide the U.S. gov’t with funny numbers.

      • I wish that were true. I also wish NSA wasn’t spying on Journalists, the IRS wasn’t targeting certain groups, and the data presented by Snowden didn’t exist. But unfortunately if we take off the rose colored glasses and look at the actual data presented in this and other articles and compare them with other datasets from both the BLS and other sources we find that the BLS data looks awful fishy.

  2. You seem to assume that it’s the BLS numbers that are “biased,” but you never justify this assumption. How do you know Gallup isn’t biased? How do we know YOU aren’t biased?

    • Good Question Ken. If we have two sets of numbers that supposedly measure the same thing and they provide two different sets of answers there are a few obvious conclusions. The first is that due to sampling or other random statistical reasons they aren’t really significantly different. But we found that the Gallup numbers are consistently significantly higher so we can eliminate this as a possibility. So there is obviously some sort of bias involved. The key question is whose numbers are biased?

      Am I biased? Well maybe. Have I cherry picked data? Have I misrepresented any data-set? In this case, whether I’m biased or not really doesn’t matter since I am reporting their entire numbers and you can easily check the original sources against this article.

      So that leaves it up to you to decide whose numbers are biased. The logical way to do that is to ask yourself who has the most to gain by fudging the numbers… a company whose reputation is based on the accuracy of their numbers and polls or the government whose political fortunes are dependent on the numbers themselves.

      Next if you look at our other articles you can see that we have also compared the BLS’ own numbers against themselves in our Employment vs Unemployment Chart and shown that they don’t add up. Thus providing additional evidence that it is the BLS numbers that are suspect. Plus we have provided Shadowstats corroborating evidence that it is the BLS that is doing the fudging.

      Most people have no problem accepting that the inflation numbers provided by the BLS are biased since they see the results every week at the store but for some reason these same people trust the unemployment numbers. Why is that?

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