Tag Archives: economics

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Incomprehensible Bullshit

The mathematics professor Alan Sokal famously shamed much of the humanities profession by publishing ‘Transgressing the Boundaries: Towards a Transformative Hermeneutics of Quantum Gravity’  — a paper intended as ambiguous gobbledegook — in the peer-reviewed postmodern cultural studies Journal Social Text in 1996.

Sokal’s paper was a cleverly and artfully constructed piece of trolling. Sokal did it by conforming to the stylistic trappings of postmodernists like Jacques Derrida, Jean Baudrillard and Luce Irigaray — mimicking their dense and obscure verbiage, misusing concepts from science like quantum gravity (of which there exists no widely accepted scientific theory), and shrouding his argument in a great deal of ambiguity. The piece described the notion of a “postmodern science”, one that discarded the notion of objective truth.

The fallout from this paper underlined the divide between science (which seeks objective truth), and postmodernism (which does not seek objective truth). But more than that, it exposed postmodernism and cultural studies as being ambiguous, self-absorbed, and incomprehensible, to the extent that its own journals were tricked into publishing an article intended as nonsense.

Yet this issue — of baffling with incomprehensible bullshit — is not just a problem in postmodernism. Mathematics recently had a Sokal moment (and frankly, it is surprising that it took this long). Via the LRB:

Last month That’s Mathematics! reported another landmark event in the history of academic publishing. A paper by Marcie Rathke of the University of Southern North Dakota at Hoople had been provisionally accepted for publication in Advances in Pure Mathematics. ‘Independent, Negative, Canonically Turing Arrows of Equations and Problems in Applied Formal PDE’ concludes:

Now unfortunately, we cannot assume that

It is difficult, as a non-specialist, to judge the weight of that ‘unfortunately’. Thankfully, the abstract is a model of concision:

Let ρ = A. Is it possible to extend isomorphisms? We show that D´ is stochastically orthogonal and trivially affine. In [10], the main result was the construction of p-Cardano, compactly Erdős, Weyl functions. This could shed important light on a conjecture of Conway–d’Alembert.

Baffled? You should be. Each of these sentences contains mathematical nouns linked by the verbs mathematicians use, but the sentences scarcely connect with each other. The paper was created using Mathgen, an online random maths paper generator. Mathgen has a set of rules that define how papers are arranged in sections and what kinds of sentence make up a section and how those sentences are made up from different categories of technical and non-technical words. It creates beautifully formatted papers with the conventional structure, complete with equations and citations but, alas, totally devoid of meaning.

So mathematicians and mathematics journals are also susceptible to being trolled by their own bullshit, their own conventions, syntax and “rigour”. If a mathematics journal and the peer-review process can be fooled by a meaningless paper spat out by a computer program, how much well-intentioned but bad or meaningless mathematics has also slipped through the peer review process?

And what about the other subjects that have adopted mathematical symbols as their lexicon, like economics?

I have written at length about some of the problems connected to the very great increase of mathematical terminology in economics — and remain highly sceptical of the use of assumptive models in economics.  The social sciences are particularly unsuited to simplified mathematical modelling — unlike the physical sciences, the phenomena they seek to explain tend to be far less linear in observable causation, and so far more susceptible to wildness. No model or theory less than reality itself can fully represent human behaviour and human action; each transaction in an economy is unique, and arises from a different set of circumstances, representing a constantly varying order of human preferences. This tendency toward nonlinear causality is why transparency is critical to bullshit detection in the social sciences. Just as a sheen of ambiguous, obscure and poorly-defined English can make theories incomprehensible and closed-off from scrutiny and understanding, so too can a sheen of obscure and specialised mathematics.

Well-written work — whether in plain language or mathematics — requires comprehensible explanations and definitions, so that a non-specialist with a moderate interest in the subject can quickly and easily grasp the gist of the concepts, the theory, the reasoning, and the predictions. Researchers can use as complex methods as they like — but if they cannot explain them clearly in plain language then there is a transparency problem. Without transparency, academia — whether cultural studies, or mathematics, or economics — has sometimes produced self-serving ambiguous sludge. Bad models and theories produce bad predictions that can inform bad policy and bad investment decisions.  It is so crucial that ideas are expressed in a comprehensible way, and that theories and the thought-process behind them are not hidden behind opaque or poorly-defined words or mathematics.

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Explaining Hyperinflation

This is a post in three sections. First I want to outline my conception of the price level phenomena inflation and deflation. Second, I want to outline my conception of the specific inflationary case of hyperinflation. And third, I want to consider the predictive implications of this.

Inflation & Deflation

What is inflation? There is a vast debate on the matter. Neoclassicists and Keynesians tend to define inflation as a rise in the general level of prices of goods and services in an economy over a period of time.

Prices are reached by voluntary agreement between individuals engaged in exchange. Every transaction is unique, because the circumstance of each transaction is unique. Humans choose to engage in exchange based on the desire to fulfil their own subjective needs and wants. Each individual’s supply of, and demand for goods is different, and continuously changing based on their continuously varying circumstances. This means that the measured phenomena of price level changes are ripples on the pond of human needs and wants. Nonetheless price levels convey extremely significant information — the level at which individuals are prepared to exchange the goods in question. When price levels change, it conveys that the underlying economic fundamentals encoded in human action have changed.

Economists today generally measure inflation in terms of price indices, consisting of the measured price of levels of various goods throughout the economy. Price indices are useful, but as I have demonstrated before they can often leave out important avenues like housing or equities. Any price index that does not take into account prices across the entire economy is not representing the fuller price structure.

Austrians tend to define inflation as any growth in the money supply. This is a useful measure too, but money supply growth tells us about money supply growth; it does not relate that growth in money supply to underlying productivity (or indeed to price level, which is what price indices purport and often fail to do). Each transaction is two-way, meaning that two goods are exchanged. Money is merely one of two goods involved in a transaction. If the money supply increases, but the level of productivity (and thus, supply) increases faster than the money supply, this would place a downward pressure on prices. This effect is visible in many sectors today — for instance in housing where a glut in supply has kept prices lower than their pre-2008 peak, even in spite of huge money supply growth.

So my definition of inflation is a little different to current schools. I define inflation (and deflation) as growth (or shrinkage) in the money supply disproportionate to the economy’s productivity. If money grows faster than productivity, there is inflation. If productivity grows faster than money there is deflation. If money shrinks faster than productivity, there is deflation. If productivity shrinks faster than money, there is inflation.

This is given by the following equation where R is relative inflation, ΔQ is change in productivity, and ΔM is change in the money supply:

R= ΔM-ΔQ

This chart shows relative inflation over the past fifty years. I am using M2 to denote the money supply, and GDP to denote productivity (GDP and M2 are imperfect estimations of both the true money supply, and the true level of productivity. It is possible to use MZM
for the money supply and industrial output for productivity to produce different estimates of the true level of relative inflation):

Inflation and deflation are in my view a multivariate phenomenon with four variables: supply and demand for money, and supply and demand for other goods. This is an important distinction, because it means that I am rejecting Milton Friedman’s definition that inflation is always and only a monetary phenomenon.

Friedman’s definition is based on Irving Fisher’s equation MV=PQ where M is the money supply, P is the price level, Q is the level of production and V is the velocity of money. To me, this is a tenuous relationship, because V is not directly observed but instead inferred from the other three variables. Yet to Friedman, this equation stipulates that changes in the money supply will necessarily lead to changes in the price level, because Friedman assumes the relative stability of velocity and of productivity. Yet the instability of the money velocity in recent years demonstrates empirically that velocity is not a stable figure:

And additionally, changes in the money supply can lead to changes in productivity — and that is true even under a gold or silver standard where a new discovery of gold can lead to a mining-driven boom. MV=PQ is a four-variable equation, and using a four-variable equation to establish causal linear relationships between two variables is tenuous at best.

Through the multivariate lens of relative inflation, we can grasp the underlying dynamics of hyperinflation more fully.

Hyperinflation

I define hyperinflation as an increase in relative inflation of above 50% month-on-month. This can theoretically arise from either a dramatic fall in ΔQ or a dramatic rise in ΔM.

There are zero cases of gold-denominated hyperinflation in history; gold is naturally scarce. Yet there have been plenty of cases of fiat-denominated hyperinflation:

This disparity between naturally-scarce gold which has never been hyperinflated and artificially-scarce fiat currencies which have been hyperinflated multiple times suggests very strongly that the hyperinflation is a function of governments running printing presses. Of course, no government is in the business of intentionally destroying its own credibility. So why would a government end up running the printing presses (ΔM) to oblivion?

Well, the majority of these hyperinflationary episodes were associated with the end of World War II or the breakup of the Soviet Union. Every single case in the list was a time of severe physical shocks, where countries were not producing enough food, or where manufacturing and energy generation were shut down out of political and social turmoil, or where countries were denied access to import markets as in the present Iranian hyperinflation. Increases in money supply occurred without a corresponding increase in productivity — leading to astronomical relative inflation as productivity fell off a cliff, and the money supply simultaneously soared.

Steve Hanke and Nicholas Krus of the Cato Institute note:

Hyperinflation is an economic malady that arises under extreme conditions: war, political mismanagement, and the transition from a command to market-based economy—to name a few.

So in many cases, the reason may be political expediency. It may seem easier to pay workers, and lenders, and clients of the welfare state in heavily devalued currency than it would be to default on such liabilities — as was the case in the Weimar Republic. Declining to engage in money printing does not make the underlying problems — like a collapse of agriculture, or the loss of a war, or a natural disaster — disappear, so avoiding hyperinflation may be no panacea. Money printing may be a last roll of the dice, the last failed attempt at stabilising a fundamentally rotten situation.

The fact that naturally scarce currencies like gold do not hyperinflate — even in times of extreme economic stress — suggests that the underlying mechanism here is of an extreme exogenous event causing a severe drop in productivity. Governments then run the printing presses attempting to smooth over such problems — for instance in the Weimar Republic when workers in the occupied Ruhr region went on a general strike and the Weimar government continued to print money in order to pay them. While hyperinflation can in theory arise either out of either ΔQ or ΔM, government has no reason to inject a hyper-inflationary volume of money into an economy that still has access to global exports, that still produces sufficient levels of energy and agriculture to support its population, and that still has a functional infrastructure.

This means that the indicators for imminent hyperinflation are not economic so much as they are geopolitical — wars, trade breakdowns, energy crises, socio-political collapse, collapse in production, collapse in agriculture. While all such catastrophes have preexisting economic causes, a bad economic situation will not deteriorate into full-collapse and hyperinflation without a severe intervening physical breakdown.

Predicting Hyperinflation

Hyperinflation is notoriously difficult to predict, because physical breakdowns like an invasion, or the breakup of a currency union, or a trade breakdown are political in nature, and human action is anything but timely or predictable.

However, it is possible to provide a list of factors which can make a nation or community fragile to unexpected collapses in productivity:

  1. Rising Public and-or Private Debt — risks currency crisis, especially if denominated in foreign currency.
  2. Import Dependency — supplies can be cut off, leading to bottlenecks and shortages.
  3. Energy Dependency — supplies can be cut off, leading to transport and power issues.
  4. Fragile Transport Infrastructure — transport can be disrupted by war, terrorism, shortages or natural disasters.
  5. Overstretched Military — high cost, harder to respond to unexpected disasters.
  6. Natural Disaster-Prone — e.g. volcanoes, hurricanes, tornadoes, drought, floods.
  7. Civil Disorder— may cause severe civil and economic disruption.

Readers are free to speculate as to which nation is currently most fragile to hyperinflation.

However none of these factors alone or together — however severe — are guaranteed to precipitate a shock that leads to the collapse of production or imports.

But if an incident or series of incidents leads to a severe and prolonged drop in productivity, and so long as government accelerates the printing of money to paper over the cracks, hyperinflation is a mathematical inevitability.

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Penis Length, LIBOR & Soviet Growth

Healthy markets require solid data based on reality.

It is hard enough to determine what, when and how to invest even with solid data. We live in an unpredictable and chaotic world, and the last thing that investors need is misinformation and distortions. That is why the LIBOR manipulation scandal is so infuriating; as banks skewed the figures, they skewed entire marketplaces. The level of economic distortion is incalculable — as LIBOR is used to price hundreds of trillions of assets, the effects cascaded across the entire financial system and the wider world. An unquantifiable number of good trades were made bad, and vice verse. Yet in truth we should not expect anything else from a self-reported system like LIBOR. Without real checks and balances to make sure that the data is sturdy, data should be treated as completely unreliable.

Unsurprisingly, it is emerging that many more self-reported figures may have been skewed by self-reporting bullshittery.

The Telegraph noted:

The Libor scandal could be repeated in a number of other “self-certifying” markets where prices are determined, he said

“Self-certification is clearly open to abuse, so this could occur elsewhere,” he said.

A Financial Services Authority inquiry into Libor should be extended to other self-certifying markets, he said. The Treasury said last night that the review, led by Martin Wheatley, was free to examine markets other than Libor.

An expansion of the FSA review could take in a number of other interest-rate-related data as well as some complex financial instruments measuring the difference between banks’ borrowing costs and that of the US government.[i.e. the Ted spread]. Some markets in gold and oil are also based on self-certification.

This all reminds me of this:

When humans have an incentive to exaggerate or lie — either to bolster their ego by lying about penis size, or to cream an easy profit by rigging rates — it seems they have a propensity to do so.

Hopefully there will be one beneficial side-effect of the LIBOR rigging — self-reporting will die. It seems inevitable that market participants will pay a premium for solid, independent data. But sadly, any auditor can be bribed. And in a generation’s time, the LIBOR-rigging scandal of 2008 (and probably much earlier) may just be an antique detail known to only a savvy few. Scepticism, caution and portfolio robustification will always remain essential tools for savvy investors who don’t want to lose their shirt and shoes.

It was scepticism that was the difference between economists who refused to buy into the notion of Soviet prosperity in spite of impressive (and entirely self-reported) figures emerging from the Soviet Union, and those Western economists like Paul Samuelson (perhaps spurred on by ideological fervour) who predicted again and again in textbooks spanning thirty years that the USSR would overtake the USA in GDP:

Alex Tabarrok notes:

In the 1961 edition of his famous textbook of economic principles, Paul Samuelson wrote that GNP in the Soviet Union was about half that in the United States but the Soviet Union was growing faster.  As a result, one could comfortably forecast that Soviet GNP would exceed that of the United States by as early as 1984 or perhaps by as late as 1997 and in any event Soviet GNP would greatly catch-up to U.S. GNP.  A poor forecast — but it gets worse because in subsequent editions Samuelson presented the same analysis again and again except the overtaking time was always pushed further into the future so by 1980 the dates were 2002 to 2012.  In subsequent editions, Samuelson provided no acknowledgment of his past failure to predict and little commentary beyond remarks about “bad weather” in the Soviet Union.

The reason for his prediction? Apparently, bad data.

“No incentive to amend data to show strong Russian proletarian outperforms weak American capitalist, Comrade!”

Matthew Ashton writes:

To his credit Samuelson was always fairly open about it when his predictions failed to come true, stating that he was using the best data available at the time and he changed his mind as the evidence changed. I’d argue that in some cases, especially concerning evidence coming out of the Soviet Union, he possibly should have been a bit more sceptical as to its accuracy, however almost everyone in economics is guilty of that.

While Western markets have been rigged, one can only wonder how bad the state of misreporting, fraud and delusion is in the various economies where central planning plays an even larger role than here in the West.

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The Pseudoscience of Economics

Modern economics is obsessed with modelling. An overwhelming majority of academic papers on the subject work like so: they take data, and use data to construct formal mathematical models of economic processes. Models mostly describe a situation, and describe how that situation would be changed by a given set of events; a very simple example is that as the supply of a good diminishes, its price will increase. Another is that deficit spending increases the national income. A mathematical model is a predictive tool created to demonstrate the outcome of events in a massively simplified alternate universe.

As someone who rather enjoys voyages of the imagination, the use of mathematical models in economics is intriguing. The pretension that through using formal mathematical techniques and process  we can not only accurately understand, but accurately predict the result of changes in the economy is highly seductive. After all,we can accurately predict the future, right?

Wrong. The wonderful and terrible and confounding thing about our world is that it is a deeply unpredictable place, at least in the economic sphere where each number (for instance “aggregate demand” or “aggregate supply”) in an equation may loosely refer to millions of huge, complex and dynamic events. When you’re using huge simplifications to describe reality, those simplifications may miss the important details, and your projections may go askew.

Not all modelling is equal. Newton’s model of gravitation (since superseded by Einstein’s relativity) makes relatively accurate predictions about how gravitation works, and what would happen to an object dropped 500 metres above the Earth. NASA used Newton’s equations to fly to the Moon. Of course, even in physics there are occasionally divergences and oddities (which is why there are quite often unrepeatable or anomalous experimental results, for instance the recent experiment that seemed to show neutrinos travelling faster than the speed of light). So economics — with its fixation on creating models of situations, and using these models to attempt to predict the future, mimics physics, chemistry and biology, where data is collected, and used to produce theories of physical processes which allow a modestly accurate representation of the future.

The key qualitative difference, though, is that mathematical economic theories don’t accurately predict the future. Ben Bernanke — the chairman of the Federal Reserve, and one of the most-cited academic economists in the world told the world that subprime housing was contained. That is the economic equivalent of Stephen Hawking telling the world that a meteorite is going to miss the Earth, when it is really going to hit. Physicists can very accurately model the trajectories of rocks in space. But economists cannot accurately model the trajectories of prices, employment and interest rates down on the rocky ground.

The thing that I believe modern economists are most useful for is pointing out the glaring flaws in everyone else’s theories. Steve Keen has made a public name for himself by publishing a book entitled Debunking Economics, in which he explains the glaring and various flaws in modern economic modelling (DSGE, New Classical, etc).

Economics is a complex and multi-faceted subjects. Economists must be in some measure, philosophers, historians, linguists, mathematicians, statisticians, political scientists, sociologists and psychologists, and many other things. The trouble is that at some stage in the last century the multi-faceted multi-dimensional economics (like that of Xenophon) was hijacked by mathematicians who tried to turn this huge and delicate subject into an equation. Yet economics — and economic decisions, from the macro to the micro level — is a human subject. It is subtle and psychological and sporadic. A human subject requires human language, human emotion, human intuition.

The grand theoretical-mathematical approach to economics is fundamentally flawed. Trying to smudge the human reality of economics and politics into cold mathematical shackles is degenerative.

So what to do if you want to understand the economy?

Follow the data, consider the history (similarities and differences between the past and the present) and explain your conclusions simply, as you would to a child. Consider philosophical definitions: what is money? What is demand? What is supply? What is value? How does demand affect supply? What are the global patterns of trade? Why have they emerged this way and not an alternative way? Consider possibilities. Admit the limitations of your knowledge and explore the boundaries. Stop forcing the construction of absolutes, grand frameworks, grand theories. No theory will ever be robust to everything nature will throw at it, but simple microeconomic heuristics (opportunity cost, cost-benefit analysis) combined with data-focussed historical analysis may be more robust than cold, dead mathematics.

As Heraclitus noted:

No man ever steps in the same river twice

No two situations are identical. And in this universe even tiny differences can have huge effects on the outcome of a situation. This is the butterfly effect, a term coined by Edward Lorenz, and derived from the theoretical example of a hurricane’s formation being contingent on whether or not a distant butterfly had flapped its wings several weeks before.

The pseudo-scientific school of mathematical economics hungers and craves for a perfect world, where each river is the same, where there is no butterfly effect, where human preferences are expressed in equation form, where there is no subtlety or ambiguity or uncertainty.

It is a dreamworld constructed by and for people with Asperger’s Syndrome.

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Economics for the Muppet Generation

Mark McHugh of Across the Street provides a succinct summation of the problem America faces:

McHugh continues:

  • From 1947 to 1974 US income per capita grew more than National debt per capita 25 times.
  • In the last 30 years, National debt per capita has grown more than income per capita 24 times.
  • The last time income per capita grew more than national debt per capita was 2001.
  • Ben Bernanke arrived at the Federal Reserve in 2002.

So simple, even a muppet can understand what the problem is, right?

Not exactly. We know what the problem is: national incomes aren’t rising, even while we get deeper and deeper into hock trying to maintain our standard of living. We know that this pattern is totally unsustainable; unless incomes rise, that debt will become increasingly impossible to service. What is less clear is the cause of this stagnation.

So what changed between 1990 and 2005 that led the nation debt per capita to so quickly overtake national incomes per capita?

While I am mindful that correlation does not necessarily imply causation, that data fits pretty beautifully. The explanation for this trend would be that as America has become more and more consumptive, and less and less productive that more and more capital went offshore to pay for consumption, and thus less and less contributed to the national income, even as Bernanke ponied up trillions in new reserves, and even as the shadow banking system created trillions in pseudo-money.

So where’s America’s money?

Here:


So is this a criticism of free trade? Should America have been more protectionist of her industries and her domestic manufacturing? Not necessarily; what the Washingtonian elites refer to as “free trade” is heavily subsidised. The status quo that Washington has made seems to heavily favour China and disfavour America. Imports from China are subsidised by American military largesse; every dollar America pushes into its military-industrial complex pushes shipping costs like insurance a little lower. So while labour costs in the Orient are naturally cheaper (due to population density, and development level), that doesn’t necessarily mean that Chinese goods are naturally cheaper in the American market. Under a genuinely free system — where America was not subsidising shipping costs — would made-in-America be more competitive compared to Chinese goods? Would China have built up a less  mountainous supply of American cash? I think so.

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Is This Why Americans Hate Economics?

Do you hate economics? Stephen Moore at the Wall Street Journal wrote a long a grisly post stripping things down:

Christina Romer, the University of California at Berkeley economics professor and President Obama’s first chief economist, once relayed the old joke that “there are two kinds of students: those who hate economics and those who really hate economics.” She doesn’t believe that, but it’s true. I’m surprised how many students tell me economics is their least favorite subject. Why? Because too often economic theories defy common sense. Alas, the policies of this administration haven’t boosted the profession’s reputation.

Consider what happened last week when Laura Meckler of this newspaper dared to ask White House Press Secretary Jay Carney how increasing unemployment insurance “creates jobs.” She received this slap down: “I would expect a reporter from The Wall Street Journal would know this as part of the entrance exam just to get on the paper.”

Mr. Carney explained that unemployment insurance “is one of the most direct ways to infuse money into the economy because people who are unemployed and obviously aren’t earning a paycheck are going to spend the money that they get . . . and that creates growth and income for businesses that then lead them to making decisions about jobs—more hiring.”

That’s a perfect Keynesian answer, and also perfectly nonsensical. What the White House is telling us is that the more unemployed people we can pay for not working, the more people will work. Only someone with a Ph.D. in economics from an elite university would believe this.

I have two teenage sons. One worked all summer and the other sat on his duff. To stimulate the economy, the White House wants to take more money from the son who works and give it to the one who doesn’t work. I can say with 100% certainty as a parent that in the Moore household this will lead to less work…

How did modern economics fly off the rails? The answer is that the “invisible hand” of the free enterprise system, first explained in 1776 by Adam Smith, got tossed aside for the new “macroeconomics,” a witchcraft that began to flourish in the 1930s during the rise of Keynes. Macroeconomics simply took basic laws of economics we know to be true for the firm or family—i.e., that demand curves are downward sloping; that when you tax something, you get less of it; that debts have to be repaid—and turned them on their head as national policy.

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