To Be Useful, Data Needs TheoryTags Philosophy and Methodology
For most so-called practical economists, information regarding the state of an economy is derived from data. Thus, if an economic statistic such as real gross domestic product or industrial production shows a visible increase, it is considered indicative of a strengthening of the economy. Conversely, a decline in the growth rate is regarded as weakening. It seems that by looking at the data one can ascertain economic conditions. Is this the case, though? The so-called data that analysts are looking at is a display of historical information.
But according to Ludwig von Mises in Human Action,
History cannot teach us any general rule, principle, or law. There is no means to abstract from a historical experience a posteriori any theories or theorems concerning human conduct and policies.
In the The Ultimate Foundation of Economic Science, Mises elaborates on this, asserting that
What we can "observe" is always only complex phenomena. What economic history, observation, or experience can tell us is facts like these: over a definite period of the past the miner John in the coal mines of the X company in the village of Y earned p dollars for a working day of n hours. There is no way that would lead from the assemblage of such and similar data to any theory concerning the factors determining the height of wage rates.
The historian does not simply let the events speak for themselves. He arranges them from the aspect of the ideas underlying the formation of the general notions he uses in their presentation. He does not report facts as they happened, but only relevant facts.
The Importance of Defining the Subject of Investigation
The key in data analysis is to establish the subject of investigation. Once the subject is established, the next step is to define the subject. The purpose of the definition is to ascertain the key factors that determine the subject of the investigation.
To form a definition it is helpful to go back as far as one can to the point when a particular thing emerged. For instance, when analyzing money supply we would go back to the point when a particular commodity started to assume the role of money. In the case of money, one would discover that people introduced money in order to promote the trade of goods. A commodity that was selected as money enabled the most efficient exchange.
By establishing that money is the medium of the exchange we can infer that people are paying for one good in terms of other goods with the help of money. But without defining money, it is not possible to say anything meaningful about it and its role in human affairs.
Now, when an analyst raises an alarm on account of a strong increase in the money supply, what triggers this alarm is not just an increase in money supply as such but the knowledge that the increase will set in motion an exchange of nothing for something. This in turn will result in the diversion of real wealth from wealth generators to the holders of the newly created money. Following the definition that the price of a good is the amount of money per good, the analyst is likely to infer that an increase in money supply, all other things being equal, will result in more money spent per good (i.e., prices of goods are going to increase).
The definition of money as the medium of exchange enables us to establish that once it is injected there are always early and late recipients of money. We can also establish that once injected, money is likely to be employed by some individual to exchange for the goods and services of another individual.
This enables us to notice that there is a time lag before the unit of money reaches the third individual and so on. This, in turn, can help us to infer that, as a result of the time lag and the definition of price as the amount of money per good, a change in the money supply is likely to have a delayed effect on the prices of goods in various markets.
According to Mises in The Ultimate Foundation of Economic Science,
The data of history would be nothing but a clumsy accumulation of disconnected occurrences, a heap of confusion, if they could not be clarified, arranged, and interpreted by systematic praxeological knowledge.
We can conclude that without a theoretical framework the data cannot tell us the conditions of the economy. It cannot tell us whether the strong GDP data is on account of wealth expansion or on account of the erosion of the wealth generation process.
But once we determine that the loose monetary policies of the central banks are behind the so-called strong economic conditions, we can deduce that those policies are going to weaken the wealth generation process using our definition of money. We could then conclude that loose monetary policy will be bad news for the well-being of individuals in the months ahead.
Are Metaphors Useful in Understanding an Economy?
Some commentators employ various metaphors to make sense of data. For instance, the value of various transactions is lumped together under the label “the economy,” which in turn is seen as following a trajectory similar to that of a spaceship.
If the economy (i.e., the space ship) deviates from the trajectory that was established by central bank economists as the ideal, then it is the role of the central bank decision makers to introduce the necessary policies to get it back on the desired trajectory.
Information regarding the current trajectory and its deviation from the ideal is obtained by assessing data such as GDP, industrial production, the consumer price index, the unemployment rate, etc.
Observe that the theory that policymakers are employing is derived from the view that the economy is akin to a spaceship and should follow a trajectory set by central bank policymakers.
A strict definition of money supply, on the other hand, can help clarify that central authorities' increases of the money supply can undermine the process of wealth formation. In striving to achieve their target, central bank policies are going to undermine the life and well-being of various individuals in a given country.
Hence, metaphors that are detached from the valid definition of the subject of investigation can in fact be detrimental to individuals’ well-being, creating misconceptions that affect policymakers trying to make sense of data.
We suggest that irrespective of the sophistication of the tools employed in the analysis of the data if the definitions employed are flawed then the results of the data analysis are going to be misleading.
The popular approach of creating a hypothesis and then testing it by means of various sophisticated tools is no different from data torturing to prove the case — torturing the data until it “confesses.”
For instance, an analyst speculates that a dog barking could be useful in verifying the phenomena of boom-bust cycles. If a dog barks four times, it is indicative of an economic boom ahead. If it barks two times, it is indicative of an economic bust.
By means of sophisticated statistical and mathematical methods, the analyst manages to prove the hypothesis. Should we take such results seriously?
As ridiculous as it may sound, the famous economist Milton Friedman used a guitar string analogy to explain boom-bust cycles. If the string is pushed strongly down, it is likely to come up strongly once the downward pressure is removed. Based on this, Friedman concluded that a strong economic bust is going to be followed by a strong economic boom.1 Various studies that employed sophisticated mathematical tools supported this hypothesis, which runs contrary to Mises’s business cycle theory, in which a bust follows a previous boom.
This is what one can come up with once the need to establish a rigorous theory is replaced with the framework (suggested by Milton Friedman) that anything goes as long as one can match the hypothesis with the data. On this Friedman wrote,
The ultimate goal of a positive science is the development of a theory or hypothesis that yields valid and meaningful (i.e., not truistic) predictions about phenomena not yet observed.2
Furthermore, according to Friedman,
The relevant question to ask about the assumptions of a theory is not whether they are descriptively realistic, for they never are, but whether they are sufficiently good approximation for the purpose in hand. And this question can be answered only by seeing whether the theory works, which means whether it yields sufficiently accurate predictions.3
Lacking a properly thought-out framework, Friedman’s theory of a boom-bust cycle is as ridiculous as the dog-barking theory, and no sophisticated mathematical framework is going to make it valid. What Friedman’s theory lacks is a valid definition of a boom-bust cycle and what brings it about.