By Ron Robins

How did the developed world economies come to be in such a mess? It seems that revisions to key U.S. statistics over the past three decades may have played a significant role in fashioning an illusion of unbounded prosperity. Inflation and unemployment rates were lowered and a brighter spin on the gross domestic product (GDP) fashioned. Unaware of the subtleties of many statistical changes, the media, investors, and the general public believed in a new golden era of an ever increasing GDP.

This golden economic era even had a name: the ‘Cinderella economy.’ Believing in the illusory Cinderella economy, people made financial and economic decisions that many subsequently regretted as they saw their homes and stocks plunge in value. They were uneducated about, and duped by statistics that helped lead them astray. These statistics are thus, ‘unethical statistics.’

Chief among these revisionist statistics is the Consumer Price Index (CPI). Most people believe it is the measure of inflation. But the way it is now constructed makes it a ‘cost-of-living-index.’ The two are very different from each other. Inflation, technically, usually means inflation of the money supply and might be evidenced by rising prices. And rising prices are observed by monitoring the same products and services from period to period to period!

On the other hand, a cost-of-living index like the CPI refers to price changes in what people buy, and this basket of goods and services changes frequently—as does the way in which the numbers are massaged.

The changes made by the U.S. Bureau of Labor Statistics (BLS) to the U.S. CPI over the past three decades have been staggering. As John Williams, an economist-statistician who has made it his life’s work to study U.S. government statistics, states, “Inflation, as reported by the Consumer Price Index (CPI) is understated by roughly 7% per year [compared to that of the pre Clinton Administration]. This is due to recent redefinitions of the series as well as to flawed methodologies, particularly adjustments to price measures for quality changes.” For June 2010, the CPI (CPI-U) showed an annual gain of 1.1 per cent compared to 8.4 per cent had the 1990 methodology been used, according to John Williams’ website, http://www.shadowstats.com/.

One simple example of these changes refers to substitution. Mr. Williams says the cost of living was being replaced by the ”cost of survival. The old system told you how much you had to increase your income in order to keep buying steak. The new system promised you hamburger, and then dog food, perhaps, after that.” So, as the price of beef goes up, the statisticians producing the CPI believed the consumer would substitute another cheaper meat, say chicken, for the beef. Furthermore, the weighting of beef in the index could be lowered too. Hence the CPI component related to meat costs might not change even though the cost of beef had risen markedly.

Other complex and controversial issues with the CPI series include: (1) adjustments for quality improvements, such as when a washing machine comes with improved technology but sells for a similar price to the old one, the CPI component registering washing machine costs might show a price fall; (2) ‘intervention analysis’ which relates to spreading out price changes over extended periods of time so that sudden price changes are smoothed out and do not shock people; (3) an outdated focus on a ‘core-CPI’ which excludes energy and food as if they do not matter because statistically they are too volatile; and, (4) a housing cost component that for many people is irrelevant.

The problems surrounding the employment/unemployment statistics are similarly disturbing. One huge concern here is the uncounted millions of individuals who have given up looking for work for more than a year. If they were included, June’s U.S. unemployment rate of 9.5 per cent (BLS data) would be 21.6 per cent according to Mr. Williams.

In a recent column I explained why the gross domestic product (GDP) statistic had to be replaced. That was primarily because it does not measure our well-being. My criticism here concerns how the numbers within the GDP are constructed. As Mr. Williams states, the “upward growth biases built into GDP modeling since the early 1980s… have rendered this important series nearly worthless as an indicator of economic activity.”

One of the upward biases in GDP referred to by Mr Williams is the downplaying of inflation in calculating the real GDP. This reduction of inflation results in a higher GDP. The raw or nominal GDP data has to be inflation adjusted by a ‘deflator’ to arrive at the real GDP number. For simplicity’s sake, say the nominal GDP is $1 million. If you have high inflation of 10 per cent, then your GDP is ‘deflated’ by 10 per cent for a real GDP of $900,000. But if you manage to keep down the inflation rate to 5 per cent, then the deflator is only $50,000 and your real GDP is now $950,000. Hence, the potential bias to create a lower inflation (deflator) number.

In its latest estimate of first quarter 2010 GDP the U.S. Bureau of Economic Analysis (BEA) said the economy grew at a year over year rate (not annualized) of 2.4 per cent. Mr. Williams’ believes it was actually closer -1.5 per cent!

Unbeknownst to the investing and general public, the changes in methodologies made to the principal statistics—the CPI, unemployment and GDP numbers—over the past thirty years helped them believe in a Cinderella economy and an ever expanding GDP. Believing that, they made ill-informed financial and economic decisions and suffered the consequences as the golden age evaporated before their eyes.

Had there been greater transparency, questioning and education concerning these statistics, we may have averted some of the economic excesses of recent times. But, unfortunately, that did not happen. Clearly, ‘unethical statistics’ may have played a large part in leading us astray.

August 3, 2010

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