Back before the stimulus package was passed in 2009, the models coming from the Obama administration claimed that if no stimulus package was passed, the unemployment rate would go as high as 9 percent. With the stimulus package, however, the highest it would get was around 7.5 percent or so.
As we all know, even after the $831 billion stimulus package, unemployment topped out at 10 percent in October 2009 and was well above 9 percent for more than two years.
This undated photo provided by the Colorado Department of Transportation shows a Denver-area sign. Colorado highway authorities say they'll continue requiring highway bids using federal stimulus money to include these signs. That's despite complaints from Republicans that the "Putting America To Work" signs are simply promotions for the spending plan. (AP Photo/Courtesy of the Colorado Department of Transportation)
That faulty prediction adds just one more faulty prediction to a long history of such proclamations. Indeed, government predictions may be the worst.
In 1967, the House Ways and Means Committee predicted the entire cost of Medicare would be $12 billion by 1990. The actual cost was $98 billion. In other words, they were off by a mere 817 percent!
This faulty belief in our ability to foresee the future is what Frederich Hayek referred to as "the pretense of knowledge." His interest was primarily in economics, although this pretense applies itself to all sorts of predictions about the future. As Hayek put it:
It seems to me that this failure of the economists to guide policy more successfully is closely connected with their propensity to imitate as closely as possible the procedures of the brilliantly successful physical sciences - an attempt which in our field may lead to outright error. It is an approach which has come to be described as the "scientistic" attitude - an attitude which, as I defined it some thirty years ago, "is decidedly unscientific in the true sense of the word, since it involves a mechanical and uncritical application of habits of thought to fields different from those in which they have been formed."
The world is a very complicated place. Science can correctly predict the movement of a single atom in a controlled environment. But the world at large has far too many variables to apply the same logic too. There are some 6.5 billion people making a variety of different decisions all over the planet that interact in ways that are extraordinarly complex. No matter how detalied some economic model is, it will leave out plenty.
Jim Manzi offers a great explanation of regression analysis, which is so commonly used by economists and other social scientists to make predictions.
"I’ve built thousands of regression models in my life, and they are not useless; they are useful for certain purposes. What I argue is that they are not capable of determining reliable, useful, and nonobvious effects of interventions…
"…if I have a regression analysis that tries to predict a set of variables–say, I want to predict what unemployment will be... hypothetically, the size of the population, economic growth rate in a prior period, the education level of the population, and so on, and I say: I am trying to use this to measure the effect of changing education levels on unemployment. And all the variables other than education level are meant to be controls, or to hold constant these other effects we described. That if I neglected to include a variable in my model for, let’s say, the amount of immigration into the society, that turns out to be causally important... what happens is by failing to include that, I modify or create instability in all the parameter estimates, including the estimate of the variable I care about. And therefore, if I’ve left out any significant variables from my equation, the estimate of the impact of the variable I care about is called into question."
"And my argument is that all regression models... are subject to omitted variable bias because we can’t get data on all the potential causes. The complexity of this phenomena overweighs our ability to build terms, to build interaction terms, and so on; and they are always subject to this problem of significant omitted variable bias. Such that we cannot rely on their results."
And as you've probably noticed, most predictions aren't based on anything as detailed or thorough as what Manzi describes.
Oftentimes, such predictions are based on nothing more than the supposed expertise of some expert. These predictions have an absolutely horrendous track record. As Daniel Kahneman notes in his book "Thinking Fast and Slow" about the work of Philip Tetlock,
"Tetlock interviewed 284 people who made their living 'commenting or offering advice on poliitcal and economic trends...' In all, Tetlock gathered more than 80,000 predictions...Respondents were asked to rate the probabilities of three alternative outcomes ... The results were devastating. The experts performed worse than they would have if they had simply assigned equal probabilities to each of the three potential outcomes."
Kahneman also looked at the performance of stock brokers at a major firm to see if the top brokers from one year consistently outperformed the others year-over-year. The correlation ended up being "...0.1. In other words, zero." Yes, those hot stock tips are usually pretty useless.
And if you want some more humorous examples of failed predictions, you can just read "The Experts Speak" about how "The abolishment of pain in surgery is a chimera. It is absurd to go on seeking it" or "This crash [of 1929] is not going to have much effect on business" and so on.
Of course, not all predictions are wrong. Economic, financial and political forecasting are not all together useless, but their reliability is wildly overstated. And the confidence many have put in these tools offers buearcrats and planners all the excuse they need to intervene more and more in the economy and every other aspect of our lives. The dismal history of these predictions and forecasts, however, should grant these planners no such authority.
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