LARS P. SYLL

Leontief and the sorry state of economics

Page after page of professional economic journals are filled with mathematical formulas leading the reader from sets of more or less plausible but entirely arbitrary assumptions to precisely stated but irrelevant theoretical conclusions …

Year after year economic theorists continue to produce scores of mathematical models and to explore in great detail their formal properties; and the econometricians fit algebraic functions of all possible shapes to essentially the same sets of data without being able to advance, in any perceptible way, a systematic understanding of the structure and the operations of a real economic system.

Wassily Leontief

Mainstream economics has indeed, as noted by Leontief, become increasingly irrelevant to the understanding of the real world, and the main reason for this irrelevance is the failure of economists to match their deductive-axiomatic methods with their subject of study. The fixation on constructing models showing the certainty of logical entailment has been detrimental to the development of a relevant and realist economics. Insisting on formalistic-mathematical modelling forces the economist to give up on realism and substitute axiomatics for real-world relevance.

It is — sad to say — a fact that within mainstream economics internal validity is everything and external validity next to nothing. Why anyone should be interested in that kind of theories and models — as long as one does not come up with export licenses for the theories and models to the real world in which we live — is beyond comprehension. Stupid models are of no help at all in understanding the real world.

Why ontology?

With any phenomenon of interest, understanding its nature or essential properties allows us to relate to, or interact with, it in more knowledgeable and competent ways than would otherwise be the case …

A surprising number of social theorists, when embarking on substantive analyses, pay almost no attention at all to insights bearing on the nature of these (or any other) factors. Instead, the preferred option is to select the types of methods, procedures or tools to be employed before, and quite independently of, knowing the task to be undertaken, the nature of the phenomena involved, the context or any other specifics of the situation …

This is the case of modern academic economics. Economists do indeed widely suppose, prior to undertaking any analysis that there is one specific way of proceeding that is appropriate for all occasions … This is to employ methods of mathematical modelling …

The discipline has failed to provide significant insight for the last 60 years or  more … This persistent failure is indeed to a very large extent the result of sustained ontological neglect.

The kinds of laws and relations that economics has established, are laws and relations about entities in models that presuppose causal mechanisms being atomistic and additive. When causal mechanisms operate in the real world they only do it in ever-changing and unstable combinations where the whole is more than a mechanical sum of parts. If economic regularities obtain they do it (as a rule) only because we engineered them for that purpose. Outside man-made “nomological machines” they are rare, or even non-existent. Unfortunately that also makes most of the achievements of contemporary economic theoretical modelling — rather useless.

When mainstream economists think that they can rigorously deduce the aggregate effects of (representative) actors with their reductionist microfoundational methodology, they have to put a blind eye on the emergent properties that characterise all open social systems – including the economic system. The interaction between animal spirits, trust, confidence, institutions etc., cannot be deduced or reduced to a question answerable on the individual level. Macroeconomic structures and phenomena have to be analysed also on their own terms.

Mainstream macromodels describe imaginary worlds using a combination of formal sign systems such as mathematics and ordinary language. The descriptions made are extremely thin and to a large degree disconnected to the specific contexts of the targeted system than one (usually) wants to (partially) represent. This is not by chance. These closed formalistic-mathematical theories and models are constructed for the purpose of being able to deliver purportedly rigorous deductions that may somehow by be exportable to the target system. By analysing a few causal factors in their “macroeconomic laboratories” they hope they can perform “thought experiments” and observe how these factors operate on their own and without impediments or confounders.

Unfortunately, this is not so. The reason for this is — as underlined by Lawson — that economic causes never act in a socio-economic vacuum. Causes have to be set in a contextual structure to be able to operate. This structure has to take some form or other, but instead of incorporating structures that are true to the target system, the settings made in these macroeconomic models are rather based on formalistic mathematical tractability. In the models they appear as unrealistic assumptions, usually playing a decisive role in getting the deductive machinery deliver “precise” and “rigorous” results. This, of course, makes exporting to real-world target systems problematic, since these models — as part of a deductivist covering-law tradition in economics — are thought to deliver general and far-reaching conclusions that are externally valid. But how can we be sure the lessons learned in these theories and models have external validity, when based on highly specific unrealistic assumptions? As a rule, the more specific and concrete the structures, the less generalisable the results. Admitting that we in principle can move from (partial) falsehoods in theories and models to truth in real world target systems does not take us very far, unless a thorough explication of the relation between theory, model and the real world target system is made. If models assume representative actors, rational expectations, market clearing and equilibrium, and we know that real people and markets cannot be expected to obey these assumptions, the warrants for supposing that conclusions or hypothesis of causally relevant mechanisms or regularities can be bridged, are obviously non-justifiable. To have a deductive warrant for things happening in a closed model is no guarantee for them being preserved when applied to the real world.

Diego Maradona (1960-2020)

Maradona was more than just an extraordinary footballer. He was also a complicated social icon. That further distinguishes him from other footballers, though Pele also has some of that…

He was both rewarded by and terribly exploited by the system. The system treated him like a “race horse”. They wanted him to play at all cost and pumped him with drugs. They did not care about the physical and psychological costs to him. That contributed to his addiction …

He came from great poverty, from a shanty town. He never hid that and insisted on keeping the connection. I’m told he had tattoos of Fidel Castro and Che Guevara. He also had a relationship with the Pope (Francisco, not Benedict II or John Paul II). That politics speaks well of him, even if it was not carried through with the consistency of an intellectual or political activist …

Did you know that in Argentina, before inflation made them irrelevant, they used to call the 10 (diez) peso note a “Diego”? That is how much people loved him.

Thomas Palley

Logic and truth in economics

To be ‘analytical’ and ‘logical’ is something most people find recommendable. These words have a positive connotation. Scientists think deeper than most other people because they use ‘logical’ and ‘analytical’ methods. In dictionaries, logic is often defined as “reasoning conducted or assessed according to strict principles of validity” and ‘analysis’ as having to do with “breaking something down.”

But that’s not the whole picture. As used in science, analysis usually means something more specific. It means to separate a problem into its constituent elements so to reduce complex — and often complicated — wholes into smaller (simpler) and more manageable parts. You take the whole and break it down (decompose) into its separate parts. Looking at the parts separately one at a time you are supposed to gain a better understanding of how these parts operate and work. Built on that more or less ‘atomistic’ knowledge you are then supposed to be able to predict and explain the behaviour of the complex and complicated whole.

In economics, that means you take the economic system and divide it into its separate parts, analyse these parts one at a time, and then after analysing the parts separately, you put the pieces together.

The ‘analytical’ approach is typically used in economic modelling, where you start with a simple model with few isolated and idealized variables. By ‘successive approximations,’ you then add more and more variables and finally get a ‘true’ model of the whole.

This may sound like a convincing and good scientific approach.

But there is a snag!

The procedure only really works when you have a machine-like whole/system/economy where the parts appear in fixed and stable configurations. And if there is anything we know about reality, it is that it is not a machine! The world we live in is not a ‘closed’ system. On the contrary. It is an essentially ‘open’ system. Things are uncertain, relational, interdependent, complex, and ever-changing.

Without assuming that the underlying structure of the economy that you try to analyze remains stable/invariant/constant, there is no chance the equations of the model remain constant. That’s the very rationale why economists use (often only implicitly) the assumption of ceteris paribus. But — nota bene — this can only be a hypothesis. You have to argue the case. If you cannot supply any sustainable justifications or warrants for the adequacy of making that assumption, then the whole analytical economic project becomes pointless non-informative nonsense. Not only have we to assume that we can shield off variables from each other analytically (external closure). We also have to assume that each and every variable themselves are amenable to be understood as stable and regularity producing machines (internal closure). Which, of course, we know is as a rule not possible. Some things, relations, and structures are not analytically graspable. Trying to analyse parenthood, marriage, employment, etc, piece by piece doesn’t make sense. To be a chieftain, a capital-owner, or a slave is not an individual property of an individual. It can come about only when individuals are integral parts of certain social structures and positions. Social relations and contexts cannot be reduced to individual phenomena. A cheque presupposes a banking system and being a tribe-member presupposes a tribe.  Not taking account of this in their ‘analytical’ approach, economic ‘analysis’ becomes uninformative nonsense.

Using ‘logical’ and ‘analytical’ methods in social sciences means that economists succumb to the fallacy of composition — the belief that the whole is nothing but the sum of its parts.  In society and in the economy this is arguably not the case. An adequate analysis of society and economy a fortiori cannot proceed by just adding up the acts and decisions of individuals. The whole is more than a sum of parts.

Mainstream economics is built on using the ‘analytical’ method. The models built with this method presuppose that social reality is ‘closed.’ Since social reality is known to be fundamentally ‘open,’ it is difficult to see how models of that kind can explain anything about what happens in such a universe. Postulating closed conditions to make models operational and then impute these closed conditions to society’s real structure is an unwarranted procedure that does not take necessary ontological considerations seriously.

In face of the kind of methodological individualism and rational choice theory that dominate mainstream economics we have to admit that even if knowing the aspirations and intentions of individuals are necessary prerequisites for giving explanations of social events, they are far from sufficient. Even the most elementary ‘rational’ actions in society presuppose the existence of social forms that it is not possible to reduce to the intentions of individuals. Here, the ‘analytical’ method fails again.

The overarching flaw with the ‘analytical’ economic approach using methodological individualism and rational choice theory is basically that they reduce social explanations to purportedly individual characteristics. But many of the characteristics and actions of the individual originate in and are made possible only through society and its relations. Society is not a Wittgensteinian ‘Tractatus-world’ characterized by atomistic states of affairs. Society is not reducible to individuals, since the social characteristics, forces, and actions of the individual are determined by pre-existing social structures and positions. Even though society is not a volitional individual, and the individual is not an entity given outside of society, the individual (actor) and the society (structure) have to be kept analytically distinct. They are tied together through the individual’s reproduction and transformation of already given social structures.

Since at least the marginal revolution in economics in the 1870s it has been an essential feature of economics to ‘analytically’ treat individuals as essentially independent and separate entities of action and decision. But, really, in such a complex, organic and evolutionary system as an economy, that kind of independence is a deeply unrealistic assumption to make. To simply assume that there is strict independence between the variables we try to analyze doesn’t help us the least if that hypothesis turns out to be unwarranted.

To be able to apply the ‘analytical’ approach, economists have to basically assume that the universe consists of ‘atoms’ that exercise their own separate and invariable effects in such a way that the whole consist of nothing but an addition of these separate atoms and their changes. These simplistic assumptions of isolation, atomicity, and additivity are, however, at odds with reality. In real-world settings, we know that the ever-changing contexts make it futile to search for knowledge by making such reductionist assumptions. Real-world individuals are not reducible to contentless atoms and so not susceptible to atomistic analysis. The world is not reducible to a set of atomistic ‘individuals’ and ‘states.’ How variable X works and influence real-world economies in situation A cannot simply be assumed to be understood or explained by looking at how X works in situation B. Knowledge of X probably does not tell us much if we do not take into consideration how it depends on Y and Z. It can never be legitimate just to assume that the world is ‘atomistic.’ Assuming real-world additivity cannot be the right thing to do if the things we have around us rather than being ‘atoms’ are ‘organic’ entities.

If we want to develop new and better economics we have to give up on the single-minded insistence on using a deductivist straitjacket methodology and the ‘analytical’ method. To focus scientific endeavours on proving things in models is a gross misapprehension of the purpose of economic theory. Deductivist models and ‘analytical’ methods disconnected from reality are not relevant to predict, explain or understand real-world economies

To have ‘consistent’ models and ‘valid’ evidence is not enough. What economics needs are real-world relevant models and sound evidence. Aiming only for ‘consistency’ and ‘validity’ is setting the economics aspirations level too low for developing a realist and relevant science.

Economics is not mathematics or logic. It’s about society. The real world.

Models may help us think through problems. But we should never forget that the formalism we use in our models is not self-evidently transportable to a largely unknown and uncertain reality. The tragedy with mainstream economic theory is that it thinks that the logic and mathematics used are sufficient for dealing with our real-world problems. They are not! Model deductions based on questionable assumptions can never be anything but pure exercises in hypothetical reasoning.

The world in which we live is inherently uncertain and quantifiable probabilities are the exception rather than the rule. To every statement about it is attached a ‘weight of argument’ that makes it impossible to reduce our beliefs and expectations to a one-dimensional stochastic probability distribution. If “God does not play dice” as Einstein maintained, I would add “nor do people.” The world as we know it has limited scope for certainty and perfect knowledge. Its intrinsic and almost unlimited complexity and the interrelatedness of its organic parts prevent the possibility of treating it as constituted by ‘legal atoms’ with discretely distinct, separable and stable causal relations. Our knowledge accordingly has to be of a rather fallible kind.

If the real world is fuzzy, vague and indeterminate, then why should our models build upon a desire to describe it as precise and predictable? Even if there always has to be a trade-off between theory-internal validity and external validity, we have to ask ourselves if our models are relevant.

‘Human logic’ has to supplant the classical — formal — logic of deductivism if we want to have anything of interest to say of the real world we inhabit. Logic is a marvellous tool in mathematics and axiomatic-deductivist systems, but a poor guide for action in real-world systems, in which concepts and entities are without clear boundaries and continually interact and overlap. In this world, I would say we are better served with a methodology that takes into account that the more we know, the more we know we do not know.

‘Mathiness’ in economics

 In practice, what math does is let macro-economists locate the FWUTVs [facts with unknown truth values] farther away from the discussion of identification … Relying on a micro-foundation lets an author say, “Assume A, assume B, …  blah blah blah … And so we have proven that P is true. Then the model is identified.” …

Distributional assumptions about error terms are a good place to bury things because hardly anyone pays attention to them. Moreover, if a critic does see that this is the identifying assumption, how can she win an argument about the true expected value the level of aether? If the author can make up an imaginary variable, “because I say so” seems like a pretty convincing answer to any question about its properties.

Paul Romer

Yes, indeed, modern mainstream economics — and especially its mathematical-statistical operationalization in the form of econometrics — fails miserably over and over again. ‘Modern’ mainstream economics is based on the belief that deductive-axiomatic modelling is a sufficient guide to truth. That belief is, however, totally unfounded as long as no proofs are supplied for us to believe in the assumptions on which the model-based deductions and conclusions build. ‘Mathiness’ masquerading as science is often used by mainstream economists to hide the problematic character of the assumptions used in their theories and models. But — without showing the model assumptions to be realistic and relevant, that kind of economics indeed, as Romer puts it, produces nothing but “blah blah blah.”

The belief that mathematical reasoning is more rigorous and precise than verbal reasoning, which is thought to be susceptible to vagueness and ambiguity, is pervasive in economics. In a celebrated attack on … Paul Krugman, the Chicago economist John Cochrane wrote, ‘Math in economics serves to keep the logic straight, to make sure that the “then” really does follow the “if,” which it so frequently does not if you just write prose.’ But there is a difficulty here which appears to be much more serious in economics than it is in natural sciences: that of relating variables which are written down and manipulated in mathematical models to things that can be identified and measured in the real world … Concepts such as ‘investment specific technology shocks’ and ‘wage markup’ which are no more observable, or well defined, than toves or borogoves. They exist only within the model, which is rigorous only in the same sense as ‘Jabberwocky’ is rigorous; the meaning of each term is defined by the author, and the logic of the argument follows tautologically from these definitions.

Without strong evidence, all kinds of absurd claims and nonsense may pretend to be science. Using math can never be a substitute for thinking. Or as Romer has it in his showdown with ‘post-real’ economics:

Math cannot establish the truth value of a fact. Never has. Never will.

Has mainstream economics — really — gone through a pluralist and empirical revolution?

In an issue of the journal Fronesis yours truly and a couple of other academics (e.g. Julie Nelson, Tony Lawson, and Phil Mirowski) made an effort at introducing its readers to heterodox economics and its critique of mainstream economics. Rather unsurprisingly this hasn’t pleased the Swedish economics establishment.

On the mainstream economics blog Ekonomistas, professor Daniel Waldenström rode out to defend the mainstream with the nowadays standard defence — heterodox critics haven’t understood that mainstream economics today has gone through a pluralist and empirical revolution. Since heterodox critics haven’t noticed that, their views on the mainstream project is more or less irrelevant.

Well, the problem with that defence is that it has pretty little with reality to do.

When mainstream economists today try to give a picture of modern economics as a pluralist enterprise, they silently ‘forget’ to mention that the change and diversity that gets their approval only takes place within the analytic-formalistic modelling strategy that makes up the core of mainstream economics. You’re free to take your analytical formalist models and apply it to whatever you want — as long as you do it with a modeling methodology that is acceptable to the mainstream. If you do not follow this particular mathematical-deductive analytical formalism you’re not even considered doing economics. If you haven’t modeled your thoughts, you’re not in the economics business. But this isn’t pluralism. It’s a methodological reductionist straightjacket.

To most mainstream economists you only have knowledge of something when you can prove it, and so ‘proving’ theories with their models via deductions is considered the only certain way to acquire new knowledge. This is, however, a view for which there is no warranted epistemological foundation. Outside mathematics and logics, all human knowledge is conjectural and fallible.

Validly deducing things in closed analytical-formalist-mathematical models — built on atomistic-reductionist assumptions — doesn’t much help us understand or explain what is taking place in the real world we happen to live in. Validly deducing things from patently unreal assumptions — that we all know are purely fictional — makes most of the modelling exercises pursued by mainstream macroeconomists rather pointless. It’s simply not the stuff that real understanding and explanation in science is made of. Had mainstream economists not been so in love with their smorgasbord of models, they would have perceived this too. Telling us that the plethora of models that make up modern macroeconomics ‘are not right or wrong,’ but ‘just more or less applicable to different situations,’ is nothing short of hand waving.

Take macroeconomics as an example. Yes, there is a proliferation of macromodels nowadays — but it almost exclusively takes place as a kind of axiomatic variation within the standard DSGE modelling framework. And — no matter how many thousands of models mainstream economists come up with, as long as they are just axiomatic variations of the same old mathematical-deductive ilk, they will not take us one single inch closer to giving us relevant and usable means to further our understanding and explanation of real economies.

Most mainstream economists seem to have no problem with this lack of fundamental diversity — not just path-dependent elaborations of the mainstream canon — and the vanishingly little real world relevance that characterise modern macroeconomics. To these economists there is nothing basically wrong with ‘standard theory.’ As long as policy makers and economists stick to ‘standard economic analysis’ — DSGE — everything is fine. Economics is just a common language and method that makes us think straight and reach correct answers.

Most mainstream neoclassical economists are not for pluralism. They are fanatics insisting on using an axiomatic-deductive economic modelling strategy. To yours truly, this attitude is nothing but a late confirmation of Alfred North Whitehead’s complaint that “the self-confidence of learned people is the comic tragedy of civilisation.”

Daniel Waldenström — like so many other mainstream economists today — seems to maintain that new imaginative empirical methods — such as natural experiments, field experiments, lab experiments, RCTs — help us to answer questions concerning the validity of economic theories and models.

Yours truly beg to differ. There are few real reasons to share his optimism on the alleged pluralist and empirical revolution in economics.

I am basically — though not without reservations — in favour of the increased use of experiments and field studies within economics. Not least as an alternative to completely barren ‘bridge-less’ axiomatic-deductive theory models. My criticism is more about aspiration levels and what we believe that we can achieve with our mediational epistemological tools and methods in the social sciences.

The increasing use of natural and quasi-natural experiments in economics during the last couple of decades has led several prominent economists to triumphantly declare it as a major step on a recent path toward empirics, where instead of being a deductive philosophy, economics is now increasingly becoming an inductive science.

In randomised trials the researchers try to find out the causal effects that different variables of interest may have by changing circumstances randomly — a procedure somewhat (‘on average’) equivalent to the usual ceteris paribus assumption).

Besides the fact that ‘on average’ is not always ‘good enough,’ it amounts to nothing but hand waving to simpliciter assume, without argumentation, that it is tenable to treat social agents and relations as homogeneous and interchangeable entities.

Randomisation is used to basically allow the econometrician to treat the population as consisting of interchangeable and homogeneous groups (‘treatment’ and ‘control’). The regression models one arrives at by using randomised trials tell us the average effect that variations in variable X has on the outcome variable Y, without having to explicitly control for effects of other explanatory variables R, S, T, etc., etc. Everything is assumed to be essentially equal except the values taken by variable X.

Just as e.g. econometrics, randomisation promises more than it can deliver, basically because it requires assumptions that in practice are not possible to maintain.

Like econometrics, randomisation is basically a deductive method. Real target systems are seldom epistemically isomorphic to our axiomatic-deductive models/systems, and even if they were, we still have to argue for the external validity of the conclusions reached from within these epistemically convenient models/systems. Causal evidence generated by randomisation procedures may be valid in ‘closed’ models, but what we usually are interested in, is causal evidence in the real target system we happen to live in.

‘Ideally controlled experiments’ tell us with certainty what causes what effects — but only given the right ‘closures.’ Making appropriate extrapolations from (ideal, accidental, natural or quasi) experiments to different settings, populations or target systems, is not easy. ‘It works there ‘s no evidence for ‘it will work here.’ Causes deduced in an experimental setting still have to show that they come with an export-warrant to the target population/system. The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ and ‘precise’ methods — and ‘on-average-knowledge’ — is despairingly small.

So, no, I find it hard to share Waldenström’s and other mainstream economists’ enthusiasm and optimism on the value of the latest ’empirical’ trends in mainstream economics. I would argue that although different ’empirical’ approaches have been — more or less — integrated into mainstream economics, there is still a long way to go before economics has become a truly empirical science.

Heterodox critics are not ill-informed about the development of mainstream economics. Its methodology is still the same basic neoclassical one. It’s still non-pluralist. And although more and more economists work within the field of ’empirical’ economics, the foundation and ‘self-evident’ bench-mark is still of the neoclassical deductive-axiomatic ilk.

Sad to say, but we still have to wait for the revolution that will make economics an empirical and truly pluralist and relevant science. Until then — if you’r familiar with the Swedish language — why not read Fronesis and get a glimpse of the future to come? Mainstream economics belongs to the past.