3 Kinds Of Statistics: Lies, Guesses And Grand Illusions

by: Louis Navellier

Benjamin Disraeli is famous for saying, "There are three kinds of lies: Lies, damned lies and statistics." That's not fair, but it reflects the common perception about how numbers are abused among consenting adults. In truth, most statistics are pretty close to the truth, but an army of biased advocates can use clever spinning to throw sand in our face every day. Some are lies, but most are wild guesses or grand illusions.

Take, for instance, the two most important economic statistics - GDP growth and jobs. One stat [GDP] comes out today, the other [JOBS] next Friday. How close to the truth will either of these numbers be?

The Latest GDP Figure: A Wild Guess or a Grand Illusion?

Let's look at this morning's GDP figures first. Each quarter's GDP evolves through a series of monthly revisions. The "flash" estimate comes out about 30 days after the quarter ends. Then come two major revisions (at monthly intervals). Later on, we often see massive changes, so let's place this morning's second-quarter GDP figure in the "wild guess" category. How do I know? During the "flash" estimate of July 31, the Bureau of Economic Analysis (BEA) revised GDP data going back five years. It turns out that the 2008-09 recession was shallower than previously thought, and the recovery was more robust.

Specifically, we now learn - five years later - that the contraction was 4.3%, not 4.7%, and the strongest year of recovery (first quarter 2011 to first quarter 2012) was +3.3%, not the 2.5% previously reported. Also, 2012 GDP growth was revised up to 2.8% vs. its previous "final" calculation of 2.2%. This added $560 billion to the U.S. economy in 2012. But wait - first-quarter 2013 GDP growth was revised down to 1.1% from the latest report of 1.8%... so we had a better 2011 and 2012 than thought, but a worse 2013.

Still, none of these numbers approach the grand illusion promised by the Obama team in 2009, when the "hope and change" crew estimated their $787 billion stimulus package would generate over 4% GDP growth in 2011, 2012, and 2013. Even with the latest rosy revisions, we're still nowhere near those 4% levels. If a CEO of a major corporation promised 4% real (6% nominal) growth for three years and delivered half that amount, the SEC would investigate. If Sarbanes-Oxley covered politicians, some of them would go to jail, but we all know that political promises are grand illusions.

More Lies, Grand Illusions, and Wild Guesses about "Jobs"

Next Friday (September 6), the market will await the all-important wild guess about how many jobs were created in August. It's impossible to count all the new jobs that fast, but there's also a growing problem with what constitutes a job. We think we know what a job looks like - 40 hours of well-paid work in a big company with good benefits. Not exactly. Out of 953,000 jobs created this year, 731,000 (77%) were part-time.

The Wall Street Journal reported recently that over half of the new jobs created this year have been in the low-wage sectors, like fast-food and retail industries, hiring legions of over-indebted college graduates who hoped for better pay. Sadly, only 43.6% of young people now have full-time work, and 21.6 million aged 18 to 31 (36% of the new "Millennials") live with their parents - the highest percentage in 40 years.

Last week, Bespoke Investment Group [BIG] added this new twist on the jobless numbers: The Gallup unemployment rate has suddenly diverged from the Bureau of Labor Statistics [BLS] data, even though they historically track each other well. Last month, Gallup's unemployment rate surged from 7.7% to 8.9%, or 1.5% above the BLS rate. Part of the problem is seasonal adjustments, but economist Ed Yardeni explained last Thursday that the BLS' payroll survey counts one worker doing two part-time jobs as two payroll jobs, so the lower (7.4%) BLS unemployment rate comes from double-counting people with two jobs.

The "Economists' Consensus" - More Wild Guessing by Credentialed Professionals

Have you noticed how each new statistic is graded according to how far it diverges from the economists' consensus - even though those credentialed experts seem unable to hit the broad side of a barn?

New home sales: Last Friday, we learned that July new home sales were significantly (90,000) weaker than expected. That's a big percent. The experts expected 487,000 new home sales vs. 397,000 reported, an 18.5% miss. Bespoke showed how these experts have missed the new home sales number by 75,000 or more 14 times since 2001. Don't they have access to actual home sales records, or are they just guessing?

Durable goods orders rose 4.2% in June, or triple the 1.4% consensus, following an upward-revised 5.2% in May (revised up from 3.7%). Once again, the revisions are huge while the consensus is out to lunch. In the same report, core capital expenditures fell 0.9% vs. a consensus for a positive +1.1%.

The U.S. trade deficit plunged to $34.2 billion in June. That's over 20% below economists' consensus estimate of $43 billion. This, in turn, forces economists to revise their second quarter GDP figure higher.

Numbers can also be conflicting. Inventories increased $56.7 billion in the second quarter, according to the BEA's "flash" GDP estimate on July 31, but the monthly inventory figures for May and June were both down. June wholesale inventories were down 0.2%, following -0.6% in May. So which is right?

The "Fatal Conceit" of the "Pretense of Knowledge"

We need statistics to get a general (but not too specific) idea about the direction of our economy, but we shouldn't put too much faith in consensus estimates (guesses) or grand illusions from politicians.

Are most government statistics a pack of lies, perpetrated to mislead the public? That's a widespread theory from some respected statisticians, like John Williams Shadowstats website. Supposedly, the Consumer Price Index [CPI] is manipulated by arcane new definitions of price vs. value, but I reject most conspiracy theories of evil number-crunchers with an intent to mislead. Instead, I believe most of the line workers in government tend to have their blinders on. They plug in numbers the same way they've been taught, seldom bothering to question their premises. They keep their job by not making too many waves.

The basic myth behind macro-economic statistics is that some centralized bank of experts can count what billions of people do each day. Then, government central planners use those seemingly accurate numbers to plan their policies to "fine-tune" the economy for constant growth, usually through monetary stimulus.

Long before he wrote The Wealth of Nations (1776), Adam Smith wrote an equally interesting treatise, "The Theory of Moral Sentiments" (1759), in which he says that statesmen "assume an authority which could safely be trusted not only to no single person but to no council or senate whatever, and which would nowhere be so dangerous as in the hands of a man who had folly and presumption enough to fancy himself fit to exercise it." This "man of system," Smith said, is "apt to be very wise in his own conceit."

Over 200 years later, Friedrich Hayek titled his Nobel Prize lecture on December 11, 1974, "The Pretense of Knowledge." Later, in 1988, he titled one of his last books, The Fatal Conceit. His basic point is that the macro (total) economy is too large for any individual to fathom, but we pretend we can add all of the billions of individual transactions each day and come up with some way to predict future output.

Despite these warnings, please don't give up on statistics. Rightly understood, they are helpful. But swallowing undigested half-baked guesses and political pipe dreams in numerical form can be dangerous to your wealth. Take the necessary time to get behind the numbers or keep reading navellier.com.

Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

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