Why Economists’ Predictions are Always Wrong

The general conclusion at the moment seems to be that there is a need for a new set of theories of the market and economics – “crisis economics.” The reason for this need is the fact that “no one” predicted the current downturn and crisis, and that the predictions made turned out as wrong as they could possibly be. In fact, many economists predicted increased growth and continued prosperity while the true future held an economy in freefall with a number of imploding industries and sectors. 

In an opinion piece in the National Post the obvious question is asked: Why Do We Have Economists? The question had to be asked, especially since there has been no real “blame game,” no real and public debate on why all predictions turned out wrong, and no consequences for the economics profession. After all, economists often stress the fact that action is taken under rational assumptions of consequences and that all actions have consequences of some form. The army predicting economists is obviously an exception to that rule.

As some sociology professors frequently joking: say what you will about economists, but you will always get a straight and precise answer – and you always know that it is wrong. So the question asked by the editor of the National Post should be well taken; it is an important one. Why do we have economists?

But there is a question that is more important, especially for the professional economists who make all these “always wrong” predictions, and that is what makes the predictions always turn out wrong? The answer to this question lies in the error of Milton Friedman in his now famous (should-be infamous) article “The Methodology of Positive Economics” and the people who followed him (and still do).

Economics prides itself of being a deductive science, i.e. that new knowledge is deduced directly and logically from true premises or assumptions. Friedman argued that it doesn’t matter if the assumptions are wrong as long as one can extract general rules from which one can make predictions that are somewhat reliable and come close to the truth. What he spelled out was a theory of economics aiming to be a natural science, where exactness is both important and possible. In economics, however, we should learn that exactness is neither important nor possible.

In order to provide a positive, rigorous science that can produce exact predictions, one has to through out all understanding (in the Weberian verstehen-sense) and rely solely on cold data. One cannot make predictions unless that which is studied is perfectly observable and with clear boundaries. But what if we apply this line of thinking on human action, which is the core of what is studied in economics. Are the causes, nature, and consequences of human action perfectly observable and have clear boundaries? How do we measure the causes of an individual’s actions? His choice of action? The action itself? Its consequences?

The latter comes closer than the former, but it is still not even close to having the properties of the objects studied in the natural sciences. Mixing x grams of A with y grams of B may always create the substance C, and exposing D to E or F may always show exactly z – but doing m to one individual does not necessarily create the same effect as doing m to another. People are not simply responding perfectly and blindly to exogenous influences, there is a whole lot of other things going on that are at least as important as certain influences. Some call it “free will,” but you don’t have to go as far into metaphysical or religious pondering to realize that people are neither rocks nor [simpler] animals.

The problem of economic prediction is just that underlying assumption that we can “easily” predict the outcome of numerous people through meddling with some of the variables that affect people’s choices. It is simply not the case that different individuals choose to act the same way when exposed to (or influenced by) the same stimuli. Our bodies may – may – react in the same way, but our minds do not. 

To this some might retort: thanks to the law of large numbers we can generalize our conclusions despite individuals not being alike. When the law of large numbers is applicable, we can simply assume that if we just have a sample large enough all potentially skewed or unrepresentative data will even out and we will find The Truth about human beings. But this does not change the problem at hand – we are still generalizing in the same way, but only with more data and more individuals. 

Even if we accept the law of large numbers as a sufficient reason to use statistics to understand people, we will have to face the problem with their not being the same. That people, being boundedly rational, would always choose more over less (which necessarily follows from the definition of choice) does not mean they will choose a particular outcome over another in every situation. Each individual will make a subjective assessment of his preferences and rank them, then make a choice based on what he knows of his ranked preferences (this is the decision process, whether it is carried out consciously and reflectingly or not). But the ranking may change depending on circumstances as well as what the individual has learned.

Making perfect predictions the way Friedman proposed means we must take the quality of being human out of every individual, or at least “even it out” in order to calculate precise predictions. What do we learn by knowing that people without personalities and without “inner depth” (some call it soul) would necessarily act according to our the predictions? Probably not much.

Furthermore, the predictions are based on extrapolating well beyond what is reasonable. Establishing one person’ s assessment of everyday risk and the costs he accepts to take care to avoid this risk, and translating it into dollar amounts, does not necessarily give us monotonous knowledge of this individuals preferred choices. It does not follow that he would accept a high risk to lose his life if he was paid some muliple of the cost he was willing to take on for smaller risks.

Predictions simply do not cut it. So why do we have economists?

The answer to this question is that we do not need most economists, but we do, at the same time, need economists more than ever. The reason for this is that the economists working on predicting the exact outcome of hundreds or thousands (or millions or billions) of individuals’ simultaneous choices are worthless, their methodology is fundamentally flawed and they are nothing but frauds. And they should be treated accordingly.

While we think of what to do with the predicting economists we need to find the real economists, the people who understand the market and can tell us how it functions and what is required for it to function well. Very few economists understand what the market is about and how the emergent order arises, subsists, and what it effectuates. These economists were able to say a long time ago that we were heading towards a meltdown, and they did. They even published these warnings, but nobody listened or wanted to hear about it. “Nobody” here denotes the prediconomists and the political elite that [usually] hire them. 

Economists need to do what businesses did a long time ago: go back to basics. There is no need for armies of economists trying to predict the exact results of public policy, of interest rate changes, or monetary policy, etc. The use of prediconomists is not to learn about the future or politics, but as “useful idiots” disguising blind, naive, and ignorant attempts to regulate people’s choices through granting the commandeering of society an air of scientificity. And they serve well as scapegoats when their predictions turn out to be wrong and the people in charge can hide behind their “good intentions.”

What there is a need for is real economists who do not engage in futile attempts to “scientifically” make exact predictions of people’s future choices. We need people to tell us how the market works so that we can reap the full fruits of our hard work and profit from the risks we take.