**Summary:**

Most economists consider the use of sophisticated mathematical and statistical methods key to understanding the complexities of economics. By means of mathematical and statistical methods, an economist establishes relationships between variables. For example, personal consumer outlays are related to personal disposable income and interest rates. Most economists would present this relation as a mathematical function: ...

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Most economists consider the use of sophisticated mathematical and statistical methods key to understanding the complexities of economics.

By means of mathematical and statistical methods, an economist establishes relationships between variables. For example, personal consumer outlays are related to personal disposable income and interest rates. Most economists would present this relation as a mathematical function:

C=a*Yd – b*i

*C* represents personal consumer outlays, *Yd* is personal disposable income, *i* stands for interest rate, *a* and *b* are parameters.

If *a* is 0.5, *b* is –0.1, *Yd* is 1000 and *i* the interest rate is 2%, then *C* will be 0.5*1000 – 0.1*2 = 499.8.

Parameters *a* and *b* are obtained using a sophisticated statistical method called the *regression analysis*.

By presenting the supposed relation between personal outlays, disposable income, and interest rates as a mathematical function the economist creates the impression of being scientific. Most people who are not familiar with mathematical and statistical methods are likely to be reluctant to question the analysis of the so-called scientific economist.

But is this quantitative approach a valid way to understand economic events?

#### Is the Quantitative Method Valid in Economics?

In the natural sciences, the employment of mathematics enables scientists to formulate the essential nature of objects. Using a mathematical formula, the response of objects to a particular stimulus in a given condition can be captured, and the same response will be obtained time and again.

This approach, however, is not valid in economics, which deals with human beings and not objects.

To pursue quantitative analysis implies the possibility of assigning numbers, which can be subjected to all the operations of arithmetic. To assign numbers, it is necessary to define an objective fixed unit. Such an objective unit, however, does not exist in the realm of human valuations.

There are no constant standards for measuring the minds, values, and ideas of men. People can change their minds and pursue actions that are contrary to what was observed in the past. It is individual goals or ends that set the standard for weighing the facts of reality.

The employment of mathematical functions implies that human actions are set in motion reflexively by various factors rather than by deliberate choice.

For instance, given levels of outlay on goods are not "caused" by income. Every individual decides how much of his income will be used for consumption and how much for savings. Although it is true that people respond to changes in their incomes, the response is not automatic, and it cannot be captured by a mathematical formula. An increase in an individual’s income does not automatically lead to one in his consumption expenditure. Every individual assesses the increase in income against the goals he wants to achieve. Thus, he might decide that it is more beneficial for him to raise his savings rather than raise his consumption.

#### Coin Tosses and Fires: Probability Theory in Economics

Modern economics also employs probability distributions. What is probability? The probability of an event is the proportion of times the event happens in a large number of trials.

For instance, the probability of obtaining heads when a coin is tossed is 0.5. This does not mean that when a coin is tossed ten times, five heads are always obtained. However, if the experiment is repeated a large number of times then it is likely that 50 percent will be heads. The greater the number of throws, the nearer the approximation is likely to be.

Alternatively, say it has been established that in a particular area, the probability of wooden houses catching fire is 0.01. This means that on the basis of experience 1 percent of wooden houses on average will catch fire. This does not mean that this year or the following year the percentage of houses catching fire will be exactly 1 percent; however, over time the average of these percentages will be 1 percent.

This information, in turn, can be converted into the cost of fire damage, establishing a case for insuring against fire. Owners of wooden houses might decide to spread the risk among themselves by setting up a fund. Every owner of a wooden house will contribute a certain proportion to the total amount of money that is required to cover the damages of those whose houses are damaged by fire.

Note that insurance against fire risk can only take place because we know its probability distribution and because there are enough owners of wooden houses to spread the cost of fire damage among them so that the premium is not excessive.

In *Human Action*, Ludwig von Mises labeled this type of probability *class probability*. According to him,

Class probability means: we know or assume to know, with regard to the problem concerned, everything about the behavior of a whole class of events or phenomena; but about the actual singular events or phenomena we know nothing but that they are elements of this class. (p. 107)

Thus, the owners of wooden houses are all members of a particular group or class that is going to be affected in a similar way by a fire. We know that on average 1 percent of the members of this group will be affected by fire. However, we do not know exactly who it will be. The important thing for insurance is that the members of a group be identical as far as a particular event is concerned.

#### Human Acts Are Not Like Coin Tosses

In economics, we do not deal with identical cases. Each observation is unique and not a member of any class—it is a class on its own. Consequently, no probability distribution can be established.

Let us take, for instance, entrepreneurial activities. Since entrepreneurial activities are not identical, probability distribution for entrepreneurial returns cannot be formed. For example, in year one, an entrepreneurial activity might yield a 10 percent return on investment. In year two another entrepreneurial activity might produce a return of 15 percent. In year three a third entrepreneurial activity might secure a return of 1 percent, and in year four a fourth one might generate a return of 2 percent. The average is 7 percent.

By no means does this imply that we can establish a probability distribution of returns on this basis as one can for the risk of fire or for obtaining heads in a coin toss. The returns in various years are the result of specific entrepreneurial activities. These activities are not alike and repeatable, and they cannot be regarded as members of the same class.

Profit emerges when an entrepreneur discovers that certain factors are undervalued relative to the potential value of the products that they could produce. By recognizing the discrepancy and doing something about it, the entrepreneur removes it, i.e., eliminates the potential for further profit.

The entrepreneur’s recognition of potential profits means that he had particular knowledge that other people did not have (*Man, Economy, and State*, p. 466). Mises called this *case probability*, which he defined thus:

Case probability means: We know, with regard to a particular event, some of the factors which determine its outcome; but there are other determining factors about which we know nothing. (

Human Action, p. 110)

Mises held that case probability is not open to any kind of numerical valuation. Human action cannot be analyzed in the same way that one would analyze objects, whose class probability is relevant.

To make sense of data in economics one must try to understand how it emerged, not scrutinize it on its face through statistical methods.

The acceptance of probability distribution as a valid concept in economics leads to absurd results. It describes not a world of human beings who exercise their minds in making choices, but machines.

The employment of probability in economic analyses implies that a random process without method or conscious decisions generated the various pieces of economic data. But in order to survive, human beings must act consciously and purposefully. They must plan their actions and employ suitable means.

Contrary to popular thinking, advanced mathematical and statistical methods are not applicable in economics.

The use of numerical probability is only relevant in the sphere of noneconomics, where we deal with identical cases. This is however not so in economics, because human action cannot be analyzed as objects are.

Quantitative methods *describe* events, but they do not *explain* them. These methods do nothing to improve our knowledge of the driving factors in economic events, but rather distract economists from thinking about their essential causes.