The American economy appears to be in a cyclical recovery that is gaining strength. Firms have begun to hire and consumer spending seems to be accelerating. That is what usually happens after particularly sharp recessions, so it is surprising that many commentators, whether economists or politicians, seem to doubt that such a thing could possibly be happening. … Why is good news being received with such doubt? Why is "new normal" the currently popular economic phrase, signifying that growth will be subpar for an extended period, and that the old normal is no longer something to be expected?
– "Why So Glum? Numbers Point to a Recovery," the New York Times, April 8, 2010
There are three kinds of statistics. First, there are objective and verifiable statistics which are extremely difficult to fake due to the ease with which they can be independently measured and confirmed. These are most typically seen in sports. It would be very difficult for the Minnesota Vikings to falsely claim that Adrian Peterson ran for 3,000 yards in 2009 due to the NFL game logs and thousands of recorded videos of the 16 games in which he played. Second, there are objective and unverifiable statistics which are more easily faked due to the difficulty involved in measuring them. A movie's box office take, for example, is not something that a third party can reasonably confirm without sending thousands of people to all of the various movie theaters and counting how many people entered the relevant screen rooms.
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Third, there are subjective and unverifiable statistics, which include all of the most commonly cited economics statistics. These statistics are produced by a series of estimates, surveys and expert adjustments and attempt to measure vast quantities of things that are far beyond the ability of any individual or organization to confirm. They are, therefore, intrinsically subject to error. And while the precise amount of the error is unknowable, we can still manage to determine which of these subjective and unverifiable statistics are more likely to be significant than the others by comparing them to each other and connecting them to observable events in the real economy.
Two of the various reasons that I reject the assertion that the economy is in recovery can be seen in the two charts below. The first was produced by Karl Denninger of the Market Ticker, and shows how U.S. GDP growth since 2001, which has apparently ranged between negative one percent and six percent, has been entirely dependent upon expanding the federal government deficit. While spending that is funded by debt is counted in the GDP formula, it is not actually indicative of real, wealth-producing economic growth. This means that the encouraging numbers cited by the New York Times do not denote economic recovery, but rather an increasing divergence between the statistical measures and actual economic activity.
(Chart: The Market Ticker)
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The second reason is the continued collapse of bank credit. Consumer spending in the United States is heavily dependent upon debt. If banks are not making more loans by providing increasing amounts of credit to consumers, those consumers cannot spend more money. In the last week reported alone, $56.7 billion in bank loans disappeared, the second largest reduction in bank credit since the crisis began in October 2008. This means that bank credit is already down nearly three percent in 2010, a faster rate of decline than was seen in 2009. And as the blue columns show, at the present rate, by the end of the year $1.4 trillion in bank credit will have vanished over the course of 27 months.
This is not an economic recovery. It is a deepening economic contraction temporarily disguised by an increase in government spending, and what the economically unsophisticated journalists at the New York Times are seeing is nothing more than a statistical mirage. Compounding their inability to correctly understand and interpret the data is the optimistic spin being generated on a daily basis by the central bankers and stock-market cheerleaders of the world. Statistics lie as easily as they tell the truth; the trick is to determine which metrics are deceptive and which are reliable.