Table of Contents
Reflections on Epistemology
The Epistemology of Mathematics
From Physics to Economics
Economic Thought Before Econometrics
The Origins of Econometrics
Methodenstreite: The Austrians vs. the Prussian Historical School
Caveats in Economic Statistics
Causation or Correlation?
Are Economic Equations Really Necessary?
What is the Purpose of Economics?
The Praxeological Alternative
Mathematics has become an integral part of modern economic theory. At this point, one can hardly read a paper by mainstream scholars in the discipline without encountering complex formulae, functions, and equations which appear incomprehensible to anyone without a deep comprehension of mathematics. With decades of research and university classes based upon the validity of econometrics – the application of mathematics to economic theory – a lot of scholarly effort would’ve been spent for nothing if it was found to have shaky epistemological foundations. The economists of the Austrian tradition have been especially critical of econometrics, and consider a causal-realist approach to be the preferred methodology. It is of primary importance for legitimate contributions in academic work that empirical claims be well-documented and the premises and methodology to have sound epistemological foundations, so the significance of a proper investigation on the issue could hardly be overstated. In this article, I’ll first delineate some principles of epistemology and the general epistemological value of mathematics, then document the history of econometrics, and afterward explain and elaborate on the Austrian critique. As many a reader may find it difficult to see the relevance of the preliminary discussions to the central question at hand, the model below is provided to clarify why I feel the need to go through them all in order.
Econometrics is based upon certain uses of mathematics, which is again based upon certain epistemological assumptions. To put it in another way, the validity of econometrics is dependent on epistemological assumptions which allow for such applications of mathematics to economics. We’ll later see that much of the disagreement between the Austrian school and the econometricians is firmly grounded in epistemological disputes, so some basic understanding of epistemology is certainly warranted. In the words of Ludwig von Mises (2008: 5),
Such doctrines [condemning economic theory] go far beyond the limits of economics. They question not only economics and praxeology but all other human knowledge and human reasoning in general. They refer to mathematics and physics as well as to economics. It seems therefore that the task of refuting them does not fall to any single branch of knowledge but to epistemology and philosophy.
Reflections on Epistemology
The central question in epistemology is this: Can we truly know anything at all? The significance of this question can be illustrated by Immanuel Kant’s distinction between the noumenal and phenomenal sphere, the former describing some external, objective reality, and the latter our individual, subjective perceptions thereof. To rephrase, we may ask whether it’s at all possible for our phenomenal sphere to grasp the noumenal, and if so to what extent. Certain skeptics answer in the negative and end up adopting relativism as a view of reality: We cannot know anything for certain, so anything goes.
Two central arguments one may levy against this position are consequentialist and deontological, respectively. First, the consequences of acting out this viewpoint are highly impractical and even dangerous, as progress in the theoretical sciences has allowed for an ever-improving understanding of the subjects at hand, and the application thereof has allowed for an escape from the Malthusian condition of perpetual poverty; extended longevity; and improvement in living standards. Secondly, it ignores the degree to which one may be more or less certain about certain empirical claims.
A useful epistemological principle for the latter point has been provided by the philosopher of science Karl Popper, who posited that the purpose of science should not be about trying to confirm certain empirical statements (and he believed that was ultimately impossible), but rather seeking to refute them. A hundred cases supporting an empirical statement can make it seem highly likely to be true, but it only takes one counterexample to call into question whether it is really so. From this one may deduce a probabilistic view of epistemology, i.e. that certain information can increase or decrease the likelihood of something being true, but possibly never indefinitely prove it. As Ludwig von Mises explains,
There is no such thing as perfection in human knowledge, nor for that matter in any other human achievement. Omniscience is denied to man. The most elaborate theory that seems to satisfy completely our thirst for knowledge may one day be amended or supplanted by a new theory. Science does not give us absolute and final certainty. It only gives us assurance within the limits of our mental abilities and the prevailing state of scientific thought. A scientific system is but one station in an endlessly progressing search for knowledge. It is necessarily affected by the insufficiency inherent in every human effort (p. 7).
To use a classic example, in the effort of René Descartes to discover a truth claim that could hardly be questioned, he contended that cogito ergo sum (I think, therefore I am). This has often been mischaracterized, in my view, to be a case of circular reasoning, i.e. that the definitions of thinking and existing are conflated. I consider his argumentation rather be as follows:
- (Unstated) Premise 1: A necessary precondition for thinking is to exist;
- P2: I think;
- Conclusion: Therefore, I exist (am).
One may then apply Popper’s theory and try to refute Descartes’ argumentation, but how may one go about doing so? The Roman statesman Marcus Cicero suggested four possibilities, contending that “Every argument is refuted in one of these ways:
- [O]ne or more of its assumptions is not granted;
- [I]f the assumptions are granted, it is denied that a conclusion can be drawn from them;
- [T]he actual form of argument is shown to be fallacious; or
- [A] strong argument is countered by one equally strong or stronger.
(Cicero and May, 2016: 61)
The strength of Descartes’ argumentation can be recognized clearly by trying to apply any one of these, as the definitions of the verbs “thinking” and “existing” may have to be altered significantly from their common usage for the refutation to make any sense (or perhaps some subtle thinker may come up with a good use of the fourth option here). For most argumentation, however, Cicero’s methodology can be incredibly useful.
A final topic to discuss before finally applying these principles to the field of mathematics and economics (and likely the most relevant for our purposes) is the difference between induction and deduction as epistemological methodologies. Deductive reasoning is based upon applying general statements to a specific case, while inductive reasoning extrapolates some pattern in a specific case to apply more generally (Bradford, 2019). An example of deductive reasoning has been provided above on Descartes’ famous statement, which can be reformulated to show that a general statement (if something/someone thinks, it has to exist) is applied to a specific one (I think, therefore I exist). If the premise is valid, the conclusion is so as well, given that the deduction has been done appropriately. If Cicero had encountered someone who tried to use induction for his argumentation, on the other hand, he might have ventured to refute it by contending that the conclusion cannot necessarily be legitimately derived from the premises. David Hume showed the problem of induction in 1739 when he explained that
When the mind […] passes from the idea or impression of one object to the idea or belief of another, it is not determin’d by reason, but by certain principles, which associate together the ideas of these objects, and unite them in the imagination (As cited in Henderson, 2018).
In other words, induction is based on principles of regularity and association. Regularity-based induction extrapolates the same category over time, while the association-based extrapolates to similar categories. An example of the former is that one infers from previous observations that the sun has always risen that it will (most likely) also do so the next day and far out in the future. If one had used deductive reasoning, on the other hand, one might have instead deduced from astronomical knowledge that the sun would be visible from the earth at one point or another during the day (given clear weather) unless either the sun or the earth suddenly disappeared, the moon or some other large entity blocked the view from the earth to the sun, or that the rotations of the earth radically and suddenly changed (all of which are either extremely unlikely or rare).
Hume explains association-based induction as follows:
I have found that such an object has always been attended with such an effect, and I foresee, that other objects, which are, in appearance, similar, will be attended with similar effects.
When someone has previously made numerous observations that a certain object or process has one or more specific causes, it is not uncommon that he’ll also consider (or at least suspect) that the same will be the case in similar scenarios. In many cases, this kind of reasoning is justified, but it is also the source of many cognitive biases. For instance, a single bad experience (anecdote) with a barber, a teacher, a doctor, or others, can make one infer that it signals something about the contemporary state of the entire profession in general as if they be a clear representative thereof. I’d say that such observations certainly warrant investigation to the broader category, findings from which one can deduce whether it’s an outlier or an indicator of a larger problem, but as we can see here, it’s the deduction, not the induction, that provides us with the most adequate knowledge on the matter.
The Epistemology of Mathematics
I consider it indubitable that mathematics has some epistemological value. In an ocean of infinite information, the cognitive limitations of humans force us to make perceptual categorizations based on common characteristics in the objects and processes we decide to pay attention to. No two objects or processes in the physical world are perfectly identical, but as it’d be impossible for us to make sense of anything if we didn’t, we have to categorize based on their common traits.
Mathematics appears to me to be the application of this observation: the abstraction of similar concretes (objects/processes) into numbers, and finding patterns of the relationships between them. Arithmetics enables us to make calculations from known numbers, algebra to find unknowns from the knowns, functions to illustrate and calculate the relationship between two variables, calculus to uncover deeper information from those functions, and so on. Mathematics has a great deal of variety, and I could hardly mention all of its different subcategories, but the ones above are at least the main and most well-known ones. Where the components of the category abstracted are adequately similar in their common characteristics, mathematics can thus provide much epistemological value to derive knowledge therefrom.
The discipline of physics has especially benefited greatly from making use of mathematics. The processes which the physicists observe are rather homogenous – i.e. they appear indistinguishable – which very much allows for the kind of adequate application of mathematics delineated above, and a vast amount of knowledge can thereby be derived therefrom. Newton’s second law of motion, for instance, is illustrated by the formula F=ma, which means that the force applied to an object is equal to the latter’s mass multiplied by its acceleration. Even in physics, however, there are certain limitations and caveats to keep in mind. Jim Lucas (2017) explains that
In formulating his three laws, Newton simplified his treatment of massive bodies by considering them to be mathematical points with no size or rotation. This allowed him to ignore factors such as friction, air resistance, temperature, material properties, etc., and concentrate on phenomena that can be described solely in terms of mass, length and time. Consequently, the three laws cannot be used to describe precisely the behavior of large rigid or deformable objects; however, in many cases they provide suitably accurate approximations.
Simplified models and mathematical applications can hardly take all or even most factors into account, especially regarding complex phenomena, but they may reasonably be used to illustrate particular causal relationships ceteris paribus (all other things equal). Newton had to disregard other factors to properly analyze and illustrate the nature of forces, which can be useful for understanding thereof given that we understand its limitations, i.e. that in the real world other factors may be necessary to account for as Lucas listed above. As we can now recognize that even the application of mathematics to physics must be conducted mindfully, one may begin to imagine that it’s even more so in cases where the objects and processes analyzed are more heterogeneous – i.e. that the components of the category at hand are less indistinguishable – which is what we’ll turn to next.
From Physics to Economics
Econometrics isn’t a fundamental component to economic theory per se, as one can recognize from the long intellectual history of economic thought, but in the last one to two centuries it has come to be prioritized as a major feature within mainstream circles of the discipline.
Economic Thought Before Econometrics
In Oeconomicus, dated around 362 B.C. making it one of the earliest documented discussions on economics, the ancient Greek philosopher Xenophon presents a dialogue between Socrates and Critobulus where the use-value rather than the comprehension value of economics is emphasized. Socrates is there reported to be asking rhetorically that “It would seem, it is the part of a good economist to know how to deal with his own or his employer’s foes so as to get profit out of them?” Though he later takes Critobulus to task for the ethical implications of agreeing with this, it is for our purposes more relevant to mention that Socrates generally derived knowledge on the matter through the use of deduction (though he sometimes also uses induction in his analogies) by applying general principles to specific cases. Although the practicality of mathematics was well-known at the time (i.e. Euclid and Pythagoras), it seems that it did not cross the line to also be applied to the field of economics.
Also 1700 years later was this the case, as Nicole Oresme, often said to have been the “greatest medieval economist (Kirschner, 2017),” reflected in the 14th century on the origins of money and used deduction rather than statistical analysis to derive his theory:
One country abounded in one thing and lacked another. Men therefore began to trade by barter: one man gave another a sheep for some corn, another gave his labour for bread or wool, and so with other things. […] But as this exchange and transport of commodities gave rise to many inconveniences, men were subtle enough to devise the use of money to be the instrument for exchanging the natural riches which of themselves minister to human need (Oresme and Johnson, 1956: 4).
Followers of Austrian economics will recognize a remarkable similarity with Carl Menger’s – the founder of the school of thought – discussion on the issue written about five centuries later. Oresme also came up with what is now known as “Gresham’s law”, often credited to the 19th-century economist Thomas Gresham.
A final figure who will be discussed prior to uncovering the origins and popularization of econometrics is the North African medieval economist Ibn Khaldun, a contemporary of Oresme. Among the many contributions to economic thought by Khaldun, Mujahidin (2018) notes they include
doctrine of values, the division of labor, the price system, the law of supply and demand, consumption and production, money, capital formation, population growth, macroeconomics of taxes and public spending, trade cycles, agriculture, industry and trade, property rights and prosperity.
Ibn Khaldun presents the theory of multidisciplinary and dynamic development. This theory argues that the development or decline of the economy or society does not depend on a single factor, but rather on the interaction of moral, social, economic, political and historical factors over a period of time. One of these factors acts as a trigger mechanism and if another response in the same direction, development or decrease gain momentum through a chain reaction until it becomes difficult to distinguish the cause of its effect.
In his fascinating book The Birthplace of Capitalism: The Middle East, Nima Sanandaji (2018: 152-4) adds that “many of the ideas we today associate with Western free-market thinkers originated in the Islamic world during the Islamic Golden Age,” and notes that Khaldun among other things came up with the “Laffer curve” long before Arthur Laffer. In Sanandaji’s words, the Laffer curve “shows that the revenues from each additional tax-raise will be lower than the revenues generated by each previous raise, the reason being that people and enterprises will adapt by avoiding taxable activities.” To my knowledge, Khaldun himself uncovered it through a priori deduction and did not present it graphically or otherwise mathematically, and that it was only with Laffer that the concept was “mathematized”, so to speak. The Laffer Curve itself, however, is merely a model, an illustration, which helps better understand the concept rather than further obfuscate it. Still, if one attempts to make a function of it intended to represent the relationship in a given area, the calculation may run into a variety of errors, as will be explored later.
The Origins of Econometrics
When did econometrics as a methodology and subfield of economics begin? Perhaps the first known application of mathematics to economics was done by Sir William Petty in the 17th century. Calling his methodology “political arithmetick”, Petty argued that this line of thinking would provide further epistemological value for predicting the likelihood of certain future events occurring and understanding complex systems more easily:
The Method I take to do […] is not yet very usual; for instead of using only comparative and superlative Words, and intellectual Arguments, I have taken the course […] to express my self in Terms of Number, Weight, or Measure; to use only Arguments of Sense, and to consider only such Causes, as have visible Foundations in Nature; leaving those that depend upon the mutable Minds, Opinions, Appetites, and Passions of particular Men, to the Consideration of others: Really professing my self as unable to speak satisfactorily upon those Grounds (if they may be call’d Grounds), as to foretel the cast of a Dye; to play well at Tennis, Billiards, or Bowles, (without long practice,) by virtue of the most elaborate Conceptions that ever have been written […] (Petty, 1690).
Blaise Pascal and Pierre de Format were already developing probability theory in the middle of the century (Batanero, Henry and Parzysz, 2005), and to play such sports and games well it’s clear that one can get quite far by being adequately knowledgable about “such Causes, as have visible Foundations in Nature,” though practice and dedication also plays an important part, so his method wasn’t exactly groundbreaking, but what did Petty then find by utilizing his political arithmetics? Some of the conclusions listed in his treatise include
- That some kind of Taxes, and Publick Levies, may rather increase than diminish the Common-Wealth;
- That France cannot, by reason of Natural and Perpetual Impediments, be more powerful at Sea, than the English, or Hollanders;
- That the Impediments of Englands Greatness, are but contingent and removeable;
- That there are spare Hands enough among the King of England’s Subjects, to earn two Millions per annum, more than they now do, and there are Employments, ready.
Petty’s reasoning thus had direct implications for policy, and as he served as secretary for the contemporary dictator Oliver Cromwell – whom he very much extolled – he also had the possibility to apply it to practice. After Cromwell re-conquered Ireland, for instance, Petty helped design and overlook the so-called “Down Survey” from 1654 to 1656, which enabled “the massive expropriation of Catholic-owned land in the Cromwellian settlement, which […] laid in turn the foundations for the eighteenthcentury [sic] ‘Protestant Ascendancy (McCormick, 2016).'” Although this might seem irrelevant for the epistemological value of Petty’s political arithmetics, it illustrates the important point that the conclusions he derived therefrom could be used to justify and contribute to totalitarian policies, a tendency we see recurring in the writings of Karl Marx and John M. Keynes.
On Petty’s epistemological foundation, Peter Buck (1977) argues that it, in essence, consisted of a sort of subjectivism. “The political arithmetic of John Graunt and William Petty,” he contends,
was predicated on nominalist assumptions about the nature and sources of order in both society and the physical world. Where later statisticians routinely presumed that natural and social phenomena were susceptible to quantitative study only because and insofar as they were ordered by inherent causal laws, Graunt and Petty viewed the world as made up of discrete entities with no intrinsic relations obtaining among them. They regarded causal laws as constructs of the human mind, and they conceived the uses of mathematics in terms of creating order rather than discovering its immanent principles.
If this characterization is accurate, it would seem they could just as well use mathematics and logic more generally more as tools of powers than a source from which one seeks to derive truth.
Despite his political influence, Petty’s methodology didn’t have much influence on contemporary economic theory. Adam Smith even disparaged it in The Wealth of Nations, proclaiming that “I have no great faith in political arithmetic (Smith, 2007).” The Italian engineer Giovanni Cena “urged for the adoption of the mathematical method in economic theory” in 1711, but such a change did not occur before a century later (Encyclopedia.com, 2008), with Leon Walras in the forefront.
Leon Walras is most well-known for his General Equilibrium Theory, which “tried to show how and why all free markets tend toward equilibrium in the long run,” not only in individual markets but also in the aggregate (Green, 2019). He has been praised by the British economist J.R. Hicks (1934) with the assertion that “There are very few economists who have contributed so much to the permanent body of established truth as Walras did,” and Milton Friedman (1955) similarly contended that “Walras has done more than perhaps any other economist to give us a framework for organizing our ideas, a way of looking at the economic system and describing it that facilitates the avoidance of mistakes in logic.” What, then, did these contributions actually consist of, and how were they derived?
Hicks contends that “the discovery of the conditions of static equilibrium under perfect competition was his central achievement,” but what do these two central terms mean? A concrete definition of “perfect competition” is not easy to find, but we do know that although a market can be competitive, it can hardly be perfect: the government can create barriers to entry through regulations and taxes and establish cartels, there’s asymmetry of information, firms may try to acquire a larger market share, etc. Furthermore, supply and demand tend to approach equilibrium in a free market, but it never perfectly achieves it, even less maintaining such a state of “static equilibrium”.
However, Alan Kirman (2010) claims that “Walras also saw pure or perfect competition as an ideal rather than a reality,” and cites Alfred Marshall to have argued strongly against his models:
Perfect competition requires a perfect knowledge of the state of the market; and though no great departure from the actual facts of life is involved in assuming this knowledge on the part of dealers when we are considering the course of business in Lombard Street, the Stock Exchange, or in a wholesale Product Market: it would be an altogether unreasonable assumption to make when we are examining the causes that govern the supply of labour in any of the lower grades of industry.
Despite these theoretical issues, however, Walras seems to have played a significant role in transforming the discipline of economics. In addition to Hicks and Friedman as cited above, also Joseph Schumpeter admired his works, asserting that
Walras is in my opinion the greatest off all economists. His system of economic equilibrium, uniting, as it does, the quality of ‘revolutionary’ creativeness with the quality of classic synthesis, is the only work by an economist that will stand comparison with the achievements of theoretical physics. Compared with it, most of the theoretical writings of that period – and beyond – however valuable in themselves and however original subjectively, look like boats beside a liner, like inadequate attempts to catch some particular aspect of Walrasian truth (As cited in Knell, 2012).
This transformation has been very much disparaged by the Austrian economists. For instance, Murray Rothbard (1987) charged that
Since the equilibrium economy is by definition a changeless and unending round of robotic behavior, everyone on the market has perfect knowledge of the present and the future, and the pervasive uncertainty of the real world drops totally out of the picture. Since there is no more uncertainty, profits and losses disappear, and every business firm finds that its selling price exactly equals its cost of production.
It is surely no accident that the rise to dominance of Walrasian economics has coincided with the virtual mathematization of the social sciences. Mathematics enjoys the prestige of being truly “scientific,” but it is difficult to mathematize the messy and fuzzy uncertainties and inevitable errors of real world entrepreneurship and human actions. Once one expunges such actions and uncertainties, however, it is easy to employ algebra and the tangencies of geometry in analyzing this unrealistic but readily mathematical equilibrium state.
This takes us back to the central epistemological debate: Mathematics is simply not a useful epistemological tool for categories with too heterogeneous components, which is why it is much better for analyzing atoms and particles (physics) than people (economics).
Though Leon Walras and Carl Menger had some similar conclusions, such as independently formulating the subjective theory of value, their methodological differences were significant, and Menger would come to dedicate much of his intellectual effort to exactly this: The Methodenstreit (method conflict).
Methodenstreit: The Austrians vs. the Prussian Historical School
In the 1880s, a rigorous intellectual dispute emerged among academicians whether economic theory should most appropriately be based on deductive or inductive epistemology. Emphasizing the significance of this historical event, Ludwig von Mises documents,
In the Methodenstreit between the Austrian economists and the Prussian Historical School […] and in the discussions between the school of John Bates Clark and American Institutionalism much more was at stake than the question of what kinds of procedure was the most fruitful one. The real issue was the epistemological foundations of the science of human action and its logical legitimacy (p. 4).
In the eyes of the historicists, economic theory should be derived from economic history, and the positivists recommended, in Mises’ words, “the substitution of an illusory social science which should adopt the logical structure and pattern of Newtonian mechanics.”
According to Clark Nardinelli and Roger Meiners (1988), the dispute started in Germany with the “older” Historical School, who merely objected to David Ricardo’s use of “pure theory” without paying attention to social and historical conditions. Later the “younger” Historical School arose with Gustav Schmoller in the lead, who at the time was “the dominant figure in German academic economics and used his influence to keep theoretical economics out of German universities.” Schmoller went even further than his predecessors, arguing that
the abstract, deductive theories of the English classical economists had no place in economics. To Schmoller, scientific economics consisted of generalizations from historical monographs. General economic propositions would emerge from the detailed historical studies that were to be the main activity of the economists.
Carl Menger, the founder of the Austrian school, strongly objected to this view, contending that “history without theory could not lead to progress in economics.” Nardinelli and Meiners claims, similarly to Mises, that the methodological dispute was merely a symptom of a more fundamental divide, and
stemmed from fundamental differences in world views, with the cosmopolitan, individualist outlook of the Austrians standing in sharp contrast to the nationalistic, collectivistic outlook of the historical school.
In many ways, they proclaim, the deductive, theorist view ultimately won out, contending that “Contemporary econometrics has far more in common with contemporary theory than with the detailed empirical studies of the historical school.” I find this assertion dubious, however, as econometrics as well is grounded in an inductive epistemological framework, and has sneaked in to become a major part of the mainstream economic discipline. The Austrian school has especially viewed this trend as a step backward for economic theory, some central reasons for which will be presented below.
Caveats in Economic Statistics
Despite popular belief aren’t statistics per se epistemological tools for economic theory, but for the study of history. “History,” Mises asserts,
is the collection and systematic arrangement of all data of experience concerning human action. It deals with the concrete content of human action. It studies all human endeavors in their infinite multiplicity and variety and all individual actions with all their accidental, special, and particular implications (p. 30).
Statistics are still commonly utilized as an epistemological tool for economic theory, with Economists deriving theories from the factors seemingly associated with some positive results. Complications with the interpretations of such figures will be explained later, but first I’ll show that many popular economic metrics don’t adequately represent the underlying reality they’re supposed to due to misleading assumptions.
Most analyses of contemporary economic conditions today are quite dependent on statistical measures like the Gross Domestic Product (GDP), the Consumer Price Index (CPI), and various metrics of employment and the money supply. Besides the fact that the interpretations of these may vary widely, we may also scrutinize to which degree these measures actually portray appropriately what they’re purported to.
GDP is currently the most commonly used metric for economic growth, but there are several problems with this. For instance, Christopher Casey (2015) points out three of its most catchy assumptions:
(1) intermediate goods (e.g., steel) must be eliminated to avoid “double counting”;
(2) government expenditures consist of viable economic activities; and
(3) imports should be netted against exports.
Only “final” – i.e. consumption – goods are included in the calculation to avoid counting the exchange price both when the intermediary good is sold and after it’s used to construct some final consumption good. Casey argues that there are two main issues with this: (1) It is ultimately an arbitrary decision what is or isn’t a “final” good and one could just as well use the logic to justify “including the sale of an automobile to a consumer and disregarding its previous production;” and (2) dropping intermediate goods provides an underestimate of the economy as a whole and an overestimate of consumption relative to business investment.
The government expenditure part of the GDP metric is at least as important. The definition of the GDP is often explained with the formula GDP=C+G+I+NX, meaning consumption plus government expenditures plus investment plus net exports (exports minus imports) (Chappelow, 2019a), and the inclusion of G here has played a significant role to making GDP a rather misguiding metric. To quote Murray Rothbard (1963: 339),
Spending only measures value of output in the private economy because that spending is voluntary for services rendered. In government, the situation is entirely different: government acquires its money by coercion, and its spending has no necessary relation to the services that it might be providing to the private sector. There is no way, in fact, to gauge these services. Furthermore, every government-conscripted dollar deprives the citizen of expenditures he would rather have made. It is therefore far more realistic to make the opposite assumption […] that all government spending is a clear depredation upon, rather than an addition to, private product and private output.
Perhaps one of the most central misconceptions borne out from including this component has been a perceived association between war and economic growth. Whereas one generally thinks of misery and destruction being the rule during war, this metric makes it seems as if people were getting better off in this period! Robert Higgs (1992) has uncovered some of the problems with this, contending that
In fact, price was “never a factor” in the allocation of resources for war purposes. The authorities did not permit “the price-cost relationship […] to determine either the level of output or the distribution of the final product to individual uses.” Clearly, all presumption of equalities between prevailing prices, consumers’ marginal rates of substitution, and producers’ marginal rates of technical substitution vanished. Absent those equalities, at least as approximations, national income accounting loses its moorings; it necessarily becomes more or less arbitrary.
One can recognize this problem clearly by keeping in mind two central concepts: that which is seen and that which is not seen (Bastiat), and the economic calculation problem (Mises). What can be seen during a war is that the State uses a ton of money on weaponry and equipment, whereas what is not seen is how that money would’ve been used by private individuals had they not been plundered by the State. Secondly, as Higgs makes the point that the US was briefly turned into a command economy at the time, Mises’ concept of the economic calculation problem applies: Without prices, no rational and non-arbitrary economic calculation can be done.
The component of net exports in the GDP metric is also rather significant. In fact, it may be a central reason for why Donald Trump and his supporters are so worried about the trade deficit (imports exceeding exports) with China, leading him to levy heavy tariffs. Whereas it really just means that people in the United States purchase more from people in China than vice versa, this metric is made out to be proof that China is “ripping off” America and making United States residents worse off in general. The history of this perceptual error is long, and one may rightly see Trump’s rhetoric merely as a revival of the Mercantilism of a couple hundred years ago. In 1845, the French economist Claude Frédéric Bastiat (2011: 223-5) described the contemporary consensus: “To attack the balance of trade, it will be said, is to fight with a windmill.” However, Bastiat still did argue against it through an argumentum ad absurdum:
[…] according to the theory of the balance of trade, France has a simple means of doubling her capital at any moment. It is enough to pass them through the Customhouse, and then pitch them into the sea. In this case the exports will represent the amount of her capital, the imports will be nil, and impossible as well, we shall gain all that the sea swallows up.
This is a joke, the protectionists will say. It is impossible we could give utterance to such absurdities. You do give utterance to them, however, and, what is more, you act upon them and impose them on your fellow-citizens to the utmost of your power.
Such absurdities have now become an integral component of the main metric we use for economic growth.
Casey lastly argues that such statistics, regardless of their merit, tend to be exploited by politicians and special interest groups to their advantage. Again quoting Rothbard,
Statistics are the eyes and ears of the bureaucrat, the politician, the socialistic reformer. Only by statistics can they know, or at least have any idea about, what is going on in the economy. Only by statistics can they find out […] who “needs” what throughout the economy, and how much federal money should be channeled in what directions.
Ludwig von Mises (1962) similarly criticized the more general conception of a “national income”:
The concept of national income entirely obliterates the real conditions of production within a market economy. It implies the idea that it is not activities of individuals that bring about the improvement (or impairment) in the quantity of goods available, but something that is above and outside these activities. This mysterious something produces a quantity called “national income,” and then a second process “distributes” this quantity among the various individuals. The political meaning of this method is obvious. One criticizes the “inequality” prevailing in the “distribution” of national income. One taboos the question what makes the national income rise or drop and implies that there is no inequality in the contributions and achievements of the individuals that are generating the total quantity of national income.
Although the idea behind the GDP metric is to get the total value of the goods and services produced in a given period, this abstraction frequently births a conviction in the minds of the general public that if a country is “rich”, then it can “afford” to take care off those worse off in society. It isn’t, however, the “country” that is “rich”, but certain individuals, whether they’ve come to that position through innovation, entrepreneurship, and wealth-creation or rent-seeking. Schemes of “redistribution” can thus be unmasked as planned robberies en masse via the political apparatus. As Henry Hazlitt (1959) warns,
It is impossible […] to arrive at a precise, scientific, objective, or absolute measurement of the national income in terms of dollars. But the assumption that we can do so has led to dangerous policies, and threatens to lead to even more dangerous policies.
The CPI is a measure of inflation meant to illustrate the average price changes of a basket of consumer goods and services associated with the cost of living (Chen, 2019). Analyzing the adequacy of the CPI for measuring inflation is important for a variety of reasons, but a central point is that if wage increases are rising slower or on par with the CPI, real wages appear to be stagnating or declining. I’ve already written in-depth on this before (Kløvning, 2019), so I won’t repeat myself too much here, but it may be mentioned that the economist Donald Boudreaux pointed out that while the claim of stagnating or decreasing wages are supported if one uses the CPI, utilizing other measures like Personal Consumption Expenditures Deflator (PCE) and the Gross Domestic Product Deflator (no less generally accepted by economists than the CPI) indicates rather that the wage increases have been relatively high. Pointing at wage rate changes relative to the CPI thus isn’t by itself adequate proof of claims regarding stagnating or declining wages. The CPI Commission itself further noted that “the change in the Consumer Price Index overstates the change in the cost of living by about 1.1 percentage points per year,” which cumulating over a dozen year could add about $1 trillion to the national debt by over-indexing the budget.
A myriad of other statistical measures could’ve been scrutinized, but I consider these to illustrate clearly the points I’m trying to bring forth. The curious reader may look more into depth on statistics on unemployment (Williams, 2019), the money supply, (Rothbard, 2010; Rothbard, 2018; Shostak, 2014), etc. With the aforementioned points in mind, one begins to understand the significance of the saying “Lies, damned lies, and statistics.”
Causation or Correlation?
In addition to inadequacies in the calculations underlying the statistics, they may be interpreted in a variety of different ways. A common fallacy in interpreting statistics is called post hoc ergo propter hoc, meaning that one believes X to have caused Y only because they appear to be chronologically related. When Bastiat (2011: 367-9) wrote on the matter in 1846, he called it “the greatest and most common fallacy in reasoning,” and argued that
When an accident, like a fire, happens, insurance spreads over a great number of men and […] years, losses that, in the absence of insurance, would have fallen all at once upon one individual. But will anyone undertake that fire has become a greater evil since the introduction of insurance? […] Post hoc, ergo propter hoc. Beware of that fallacy.
Despite many warnings in numerous best-seller books on biases and fallacies, post hoc ergo propter hoc still remains a common logical error. A comprehensive list of examples of its popular usage could stack mountains of books:
- “The GDP increased after Trump levied more tariffs, meaning tariffs promote rather than stifle economic growth.”
- “The GDP increased after the net immigration rate increased, meaning immigration is a boon to economic growth.”
- “Real wages declined after the net immigration rate increased, meaning immigration harms American workers.”
- “The increase in the minimum wage didn’t lead to an increase in unemployment, meaning they’re completely unrelated.”
- “The GDP increased during wartime, meaning war promotes rather than stifles economic growth.”
A clear example of the fallacy within the mainstream economic discipline is the so-called “Phillips Curve”, which contends that “inflation and unemployment have a stable and inverse relationship (Chappelow, 2019b).” This “empirically derived” relationship fell out of vogue for a while after the stagflation period in the 1970s, when there were simultaneously high inflation and high unemployment, but with time it has come to sneak back into economic theory. According to Investopedia, the hypothesis derived from the prior correlation was that “with economic growth comes inflation, which in turn should lead to more jobs and less unemployment.”
This seems to me to be an astounding case of mistaking cause and effect due to a fundamental misunderstanding of the underlying concepts. As Joseph Salerno (2019) has shown, economic growth (i.e. increasing production) tends to lead to deflation, i.e. lower prices, rather than inflation, as one could recognize from a simple supply- and demand analysis. Inflation understood clearly, is the expansion of the money supply, which – depending on the country – is either done by the central bank or the treasury department. No wealth is created in this process, only subtly transferred from the savers of the future to tycoons and politicians presently well-connected politically. The time-delay in this process, called the Cantillon effect, may more probably be what causes the illusion of the causal relationship between inflation and economic growth, as the newly inflated money may be used for “stimulating” either the public or private sector or both. As I’ve delineated briefly before (Kløvning, 2018), this, along with artificially low interest rates, play a significant role in business cycles. A more important point to mention for our purposes, however, is that it’s required to have a proper understanding of the theory before one is able to meaningfully analyze and make sense of statistical historical trends; if one rather tries to derive theory from the statistics, it is very easy to be misguided by flaws in the statistical measures, mistaking correlation for causation, and failing to take other factors into account.
These are all instances of correlation (or lack thereof) being conflated with causation (or lack thereof). While they may appear similar, one may found them to be clearly distinct when analyzing them more closely. Mises wrote that “There are for man only two principles available for a mental grasp of reality: teleology [“explanation by reference to some purpose, end, goal, or function (Encyclopedia Britannica, 2016)”] and causality. What cannot be brought under either of these categories is absolutely hidden to the human mind (p. 25).” This makes it all the more important to properly understand how to derive epistemologically sound knowledge of causality.
Correlation doesn’t imply causation; it warrants an investigation into why the variables at hand are correlated. In some cases, the correlation is completely random, as Tyler Vigen (n.d.) has provided many humorous examples of. In instances where two variables are considered sufficiently unassociated, one considers it absurd when it’s suggested that they’re related, not to mention the reaction to causal claims. To find out whether two things are causally related, it’s thus not sufficient to show that they’re related; it must also be explained and shown how they may be causally linked. The explanation first takes root in a hypothesis, whose probability one may try to defend a priori. To quote Mises further, “The first task of every scientific inquiry is the exhaustive description and definition of all conditions and assumptions under which its various statements claim validity.”
The process to adequately derive causality from correlation is rather arduous, and, as argued above, the use of mathematics here creates a lot more logical pitfalls than it has in physics. In The Book of Why, Judea Pearl (2018: 28) suggests three stages to uncover causal relationships: Association (To which degree are the variables correlated?), Intervention (What would happen to A if B?; Which variables are required for A to occur?), and Counterfactuals (What would’ve happened to A if B hadn’t occurred?). To further use the Phillips Curve as an example, let’s go through the process of correlation to causation between inflation and economic growth. At the first stage, one may see statistics indicating an association between the two (say, for the sake of argument, before the stagflation period). Before going onto the next stage, one must first ascertain that the underlying statistics actually show what they’re purported to and don’t have many underlying issues as those delineated above, or at least above the degree to which an analysis thereof becomes meaningless. If it passes that test, so to speak, one may begin looking at cases of increases and decreases in inflation, and whether it’s consistently followed by economic growth proportionally across time and in different locations. As we know from the stagflation period, we know this isn’t the case, but if it was we could instead wonder what would happen if there had been no, or at least lower, inflation. This is purely hypothetical, and cannot be found statistically, but it still serves a crucial purpose. If economic growth had been higher if there were lower inflation, it’d indicate that there’d be a ceteris paribus negative relationship between the two. That means: If there were “no other forces acting on the system”, so to speak, inflation would lead to lower economic growth. Since there are many other factors involved here, however, economic growth might have increased regardless, but if this counterfactual assumption was correct, we could say that the economic growth could have been even higher had there been lower inflation. Though the econometricians fall into a lot of logical traps through the process laid out above, it is at this final stage where they cannot get any further with their methodology and must acquire a proper understanding of the underlying concepts and deduce the ceteris paribus relationship. As Pearl notes,
[…] generations of researchers came to believe that adjusted (or partial) regression coefficients are somehow endowed with causal information that unadjusted regression coefficient lack. Nothing could be further from the truth. Regression coefficients, whether adjusted or not, are only statistical trends, conveying no causal information in themselves (p. 222).
In other words, it’s all a large, overcomplicated, and unnecessary detour to acquire knowledge about economic theory, and is in many cases epistemologically unsound due to logical traps the econometricians frequently fall into. As Henry Hazlitt wrote in his excellent rebuttal of J.M. Keynes’ magnum opus,
“Mathematical economics,” as Keynes and others use it, can at best give precision to purely hypothetical assumptions. To mistake these hypotheses for known or determinable realities leads to a merely spurious precision and compounds error.
Even David Pollock (2014), Professor of Econometrics at the University of Leicester, concedes hesitantly that there’s merit to the objections of the subdiscipline, and in his attempt to divert the attention subtly indicates that it’s merely just useful “to the practice of statistical inference” (i.e. induction from statistics):
Doubtless, many would contend that the randomness in the variation of household
expenditures is more apparent than real. For they would argue that the appearance of randomness is due to our failure to take into account a host of other factors contributing to this behaviour. They might suggest that, if every factor were taken into account, a perfect description of the behaviour could be derived.
Fundamental though this objection might be, we can afford to ignore it; for it makes little difference to the practice of statistical inference whether the indeterminacy of the behaviour is the result of pure randomness or the result of our inability to comprehend more than a few of an infinite number of peculiar factors and circumstances affecting each household [my italics].
Typical enough for a mainstream academician, the justification of what is to be learned isn’t that it’s necessarily epistemologically valuable or useful, but that it’s what currently is in vogue within the mainstream academy. “Why are we learning this?” “Because it’s in the curriculum.” So pay attention, students, or you’ll flunk your exams!
Are Economic Equations Really Necessary?
Another key component of the mathematical application of economics is the use of equations. An equation, as the name indicates, means that the numbers on both sides must always and necessarily be equal. As we’ve seen earlier, physicists may reasonably establish certain equations of the objects they’re trying to understand within a certain margin due to the fact that they’re relatively homogenous, but that the same cannot be said for economics. Let’s go back to the Phillips curve as an example. According to the theory presented here, we may devise an equation that EG=I*k+O, where EG is economic growth, I is inflation, k is a proportional constant, and O is other factors to EG. For the sake of argument, we may imagine a case where ceteris paribus, i.e. O=0, and we’re left with EG=I*k. If this is to be either an accurate or meaningful equation, inflation must be instantly followed with proportional positive economic growth, which we know isn’t the case based on the Cantillon effect and a common-sense analysis of inflation in general.
The equations at hand may be tweaked in all sorts of ways to address other factors, but the point remains that the requirements for a meaningful equation still stands. Furthermore, the equations are derived from statistics through induction and thus aren’t “epistemologically infallible”, so to speak, as it’s based on historical knowledge (possibly flawed based on problems with the metrics) rather than economic knowledge. The empirical claim could just as well have been formed as a proposition rather than an equation, and the latter must be explained through the former anyway, so where does the need come in to go that extra step? As CJay Engel (2019) writes, “without theory, we merely have a formula, the interpretation of which must be imputed to it. […] formula without theory is so flexible so as to be completely useless if we seek to understand economic relationships.
Judea Pearl disagrees somewhat, contending that
From the practical point of view, students or colleagues can read [a formula] as they would a recipe. The recipe may be simply or complex, but at the end of the day it promises that if you follow the steps, you will know the natural direct and indirect effects – provided, of course, your causal model accurately reflects the real world.
The second purpose is subtler. […] It is a social contract. It puts a frame around an idea and says, “This is something I believe is important. This is something that deserves sharing (p. 334-5).”
The first point is understandable, especially with the emphasis that the causal model is empirically accurate, but applied to economics, they can easily become misleading and inaccurate. Furthermore, if it doesn’t provide more epistemological value to the topic, one should reflect upon whether it’s really needed at all.
As Engel argues,
Economic laws cannot be arrived at by the collection of data; this is because empirical data accumulation itself does not take into account human volition and action. And the attempt at economic science emptied of the essence of mankind itself is dead on arrival.
That a formula is a “social contract” I consider even more dubious. Indeed, creating a formula may require intellectual effort and reflect something that the one behind it considers important, but he doesn’t need to make a formula for something to emphasize its importance. Writing this article, I mention several points on both sides that I consider important and fit into an overarching narrative, putting a “frame around an idea”, illustrating what I think deserves sharing, and I certainly don’t feel obliged to formulate any mathematical formula to do so. The mathematicians may present complicated formulas and sophisticated philosophers may just as well go back to writing in Latin, but most probably, neither of them would be likely to get their message out to the general public unless they do so for political opportunism.
Again, unless both sides of the equation are always and necessarily equal, it’s bound to be misleading at best, and especially so on economic matters. This is well explained by Randall Holcombe, whom Engel cites to illustrate the fundamental problem behind Thomas Piketty’s main thesis/equation:
Piketty uses the relationship α [share of income going to capital] = r [return on capital] X β [capital to income ratio], but a more accurate way to depict the economic relationship is β = α / r. The expressions are mathematically equivalent, but Piketty’s way of showing it assumes that the value of capital determines its return, rather than the more economically accurate depiction in which the return produced by the capital determines its value.
Mathematically correct and empirically wrong – that’s the result of doing mathematical operations with economic formulas in an effort to derive new knowledge. “Thus, by ignoring human action,” Engel contends, “the modern ‘economist’ rejects the true foundation of economic science.
What is the Purpose of Economics?
For thousands of years questions on economics has been meditated on, but only in recent centuries has it emerged as a discipline of its own. The first sentence Mises wrote in his treatise Human Action pointed out exactly this: “Economics is the youngest of all sciences.” Still, these past centuries, many lives have been dedicated to uncovering the knowledge it may provide us, and as we’ve seen, there has been fierce discussions on which methodologies provide us with valid knowledge or not. Though it’s good to have some dedication and a mission to fulfill, once in a while it can be healthy to slow down and think through the bigger picture behind one’s activities: Am I really doing what’s right, meaningful, and helpful? With econometrics having taken a central part in the economic discipline the past century, myriad classes of which are taught in universities and filling 900-page-long textbooks such as Hansen (2019), I think one ought to be taking a step back and asking whether all of this has really been for the better or worse in the economic discipline, especially in light of the points I’ve been presenting here. To the open mind, no question or proposition is beyond reproach, and those willing to ask the questions nobody else would have repeatedly been the ones who’ve made breakthroughs in their fields. Attention may be the most important resource we have, and when it’s diverted to impractical or destructive activities, breaking through the illusion of productivity is essential if we want to progress and provide real value to humanity.
What, then, is the purpose of economics? To understand human action in isolation (i.e. Robinson Crusoe) and in relation to others. It is ultimately a teleological study, analyzing human activity in light of the fact that they’re pursuing certain ends. However, uncovering the contents of the action itself is ultimately up to other fields. Rothbard explains,
Technology deals with the contentual problem of how to achieve ends by adoption of means. Psychology deals with the question of why people adopt various ends and how they go about adopting them. Ethics deals with the question of what ends, or values, people should adopt. And history deals with ends adopted in the past, what means were used to try to achieve them — and what the consequences of these actions were.
What is then left for economics? “[P]raxeology“, he continues, “consists of the logical implications of the universal formal fact that people act, that they employ means to try to attain chosen ends.”
Praxeology, or economic theory in particular, is thus a unique discipline within the social sciences; for, in contrast to the others, it deals not with the content of men’s values, goals, and actions — not with what they have done or how they have acted or how they should act — but purely with the fact that they do have goals and act to attain them.
The Praxeological Alternative
Praxeology is the Austrian methodology for deriving economic knowledge and is grounded in the simple axiom that people act, i.e. utilize certain means to reach particular ends. The significance of this is more easily understood with Immanuel Kant’s distinction of truth statements, separated into analytical and synthetic, and a priori and a posteriori. Some examples of this can be seen below.
The truth of analytical statements can be uncovered in the definitions of the statement’s own particular words, and are thus tautological. Of course, this means that one cannot find its truth “in the real world”, but only within the statement, and is thus by necessity also an a priori proposition. On the other hand, the truth value of synthetic statements are found outside the statement itself, which are also often a posteriori meaning one cannot think oneself to its truth, such as “horses in Australia are quick”, which one may travel to Australia or at least watch videos of Australian horses to figure out.
Whether a synthetic statement can also be a priori is a discussion with many similarities to the Methodenstreit, and this is what Mises built his epistemological framework upon. He starts with the proposition “Humans act.” How may one refute this? One cannot, for the effort to do so is itself an act: utilizing certain means (speaking/writing) to achieve particular ends (refuting the proposition). This is what’s called a performative contradiction, meaning that the very act of trying to refute a statement actually confirms it, and is the firm foundation upon which praxeology is developed. Austrian economists, explains Jörg Hülsmann (2019),
point out that each human action contains relationships between realized and non-realized aspects of this very action. Movements of the human body (human behavior) and mental activities (thinking, listening, etc.) can then be explained by reference to these relationships inherent in human action.
What great findings, one may then wonder, has praxeology helped us uncover? The list is long, but among its most significant ones are
- The economic calculation problem, showing that rational calculation is impossible under central planning and is doomed for failure, giving the Socialists a challenge they could hardly supersede but by introducing artificial markets into their utopian models (von Mises, 2008: 694-712; von Mises, 1944);
- The Austrian Business Cycle Theory, clearly explaining the causes of the business cycle, which have provided them a solid track record of predictions for the timing of particular recessions and major economic events (Thornton, 2018: 111-229);
- That a price is a signal, reflecting certain underlying events such as the scarcity of particular resources relatively to the demand thereof, etc.;
- That preferences are ordinal rather than cardinal, meaning that utility can only be measured relatively (higher or lower order) and not in absolute terms (i.e. “I value this sandwich 35 utils”), further implying that cross-person utility analysis is meaningless (Taylor, 1980);
- Emphasizing the heterogeneity of capital goods and their relation to the plans of the entrepreneur in formulating their theory of capital (Foss, 2012).
A more in-depth perspective of the scope and depth of the epistemological potential in praxeology can be found in treatises like Human Action (von Mises, 2008) and Man, Economy, and State (Rothbard, 1962). I think I can fairly say that Austrian economics and its methodology of praxeology are rather underrated in the mainstream discipline of Economics today, and urge the latter’s constituents (professors, scholars, etc.) to at the very least take a deeper look and become familiar with this particular school of thought, and reflect for themselves whether it may provide more epistemologically sound knowledge than the Walrasian/Keynesian econometric and neoclassical approaches.
Perfect knowledge may be unachievable to humans, but within certain limits, we can establish epistemological frameworks from which we may derive and further build particular truths. Some of these are useful for particular purposes, like mathematics being used in physics to understand homogenous objects and processes, but can be problematic when applied to other fields where they are more heterogenous, causing confusion regarding the underlying causality.
In the late 1880s, it was fiercely debated what methodology was the most epistemologically sound for economic theory, with the historicists and positivists of the Historical School contending that one should adopt the methods of the “hard sciences”, deriving knowledge from mathematics and history, whereas Carl Menger and other Austrians denounced this approach and preferred a deductive, causal-realist perspective, with Ludwig von Mises later devising the methodology of praxeology.
Although the Historical School has not particularly been proclaimed the victor of the Methodenstreit by economic historians, econometrics, as popularized by Leon Walras, introduced key aspects of its central epistemological framework to the mainstream academy, and the Austrian school, in contrast, is contemporarily most commonly either ridiculed or ignored there, among those who’ve even heard about it in the first place.
Econometrics has several methodological errors, however, and doesn’t seem to be grounded on a sound epistemological base. To understand the data, one needs theory first to interpret it; the other way simply doesn’t work. Still, this is what econometricians painstakingly try to do, creating fancy formulas and functions supposedly representing some subtle phenomenons within the relationships resulting from human action. This is in vain, however, as humans are by their nature rather heterogeneous, and can thus not simply be placed into a formula. Statistics, for instance, are historical metrics in essence but within Economics are aggregations of the results of human action. By themselves, they in many cases are misleading and don’t appropriately represent what they’re purported to, and the interpretations thereof are often based on the fallacy post hoc ergo propter hoc, confusing causality and correlation. Formulas, moreover, are also based on aggregation and induction and creates confusion regarding causality and correlation.
The solution to these problems, the Austrian economists contend, is praxeology: analyzing the logical implications of the fact that humans act. We cannot set ourselves into the position of an atom, thinking out why it acts like it does, making it necessary to investigate it a posteriori. This can, however, be done with other humans, which is what makes praxeology a particularly useful methodology for economics. Understanding incentive systems, how certain factors incline humans to act in particular ways, and the consequences thereof, can comprehensively be investigated praxeologically and has allowed for many significant findings.
Thinking praxeologically comprehensively, however, is by no means simple. Mises (1962: 4) wrote that
He who wants to achieve anything in praxeology must be conversant with mathematics, physics, biology, history, and jurisprudence, lest he confuse the tasks and the methods of the theory of human action with the tasks and the methods of any of these other branches of knowledge. When I once expressed this opinion in a lecture, a young man in the audience objected. ‘You are asking too much of an economist,’ he observed; ‘nobody can force me to employ my time in studying all these sciences.’ My answer was: ‘Nobody asks or forces you to become an economist.’
This is the strict standard set to become a proper, good economist, and, like it or not, that’s how the game works: all of these fields are connected to human activity, but differs severely in their methodologies. Regardless of whether one wants to follow this standard, however, I couldn’t emphasize enough the significance of investigating the Austrian school to everyone studying and teaching economics, familiarizing them with a whole other approach to the theory, which I’ve argued to have much more of a sound epistemological framework than econometrics and the historicist methodology. To further emphasize the nature and merit of praxeology, we may most appropriately end with Mises (2008: 21) elaborating,
The teachings of praxeology and economics are valid for every human action without regard to its underlying motives, causes, and goals. The ultimate judgements of value and the ultimate ends of human action are given for any kind of scientific inquiry; they are not open to any further analysis. Praxeology deals with the ways and means chosen for the attainment of such ultimate ends. Its object is means, not ends.
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