Wednesday, 20 August 2014

In the Hands of Economists, the More Precise the Number, the Bigger the Lie.

In this second-part of the excerpt we started yesterday, author of three best-selling books already, Bill Bonner eviscerates what passes for modern economics in this excerpt from ‘Hormageddon: How Too Much of a Good Thing Leads to Disaster.’

Excerpt #2 from Hormageddon: How Too Much of a Good Thing Leads to Disaster
By Bill Bonner

The Original Economists

There was a time when economists were not so conceited, not so bold and arrogant, not so ambitious… and not such dumbbells. The original practitioners of the trade saw themselves as natural or moral philosophers.

It was ‘moral’ in the sense that when you make a mistake you have to pay for it. You don’t watch where you’re going and you step on a rake, the handle comes up and hits you in the face. You go away on a trip and forget to pay the electric bill, you come home and the lights don’t work. There is no complex mathematics that will bring the lights back on. There are no abstract theories—such as countercyclical fiscal policies—that will do it either. The solution is simple: you have to pay the bill. You have to suffer the consequences of your own mistake to set it straight. That’s moral philosophy.

But when your washing machine breaks down, you turn it off and try to fix it. This is a mechanical—not moral—system, and not a particularly complex one at that. Sometimes even a few whacks with a hammer and some choice swear words can work wonders. Percussive maintenance works!

The trouble is, economies are not washing machines. They are complex, moral systems. An economic system requires a deft, nuanced touch. But economists come up with theories and ‘fixes’ that are as clumsy as a wrench and as blunt as a hammer. They almost always lead to trouble.

The Ur-economists of old knew better. They observed animals and nature and tried to draw out the laws and principles that helped understand them. Same thing for man and his natural economy. They watched. They reflected. They attempted to make sense of it in the same way a naturalist makes sense of a beehive or an ant colony. ‘How does it work?’ they asked themselves.

In the 18th and 19th century, they were able to formulate “laws” which they believed described the way a human economy functioned.

The Wealth of Nations was Adam Smith’s observation about how wealth was created. How did people know what to produce? How did they know what price to sell it at? How did they know when to shift to other things, or when to increase production? He saw individuals guided by an ‘invisible hand’ that led them to follow their own interests and thereby respond to the needs and desires of others.

Later, other economists focused on prices. Prices had an information content that was essential for everyone, that allowed producers and consumers to get on the same page. These economists understood that when you manipulate the numbers you confuse them both.

Among the other phenomena that these proto-economists discovered were Say’s Law and Gresham’s Law.

French businessman and economist, Jean-Baptiste Say, discovered that “products are paid for with products,” not merely with money. He meant that you needed to produce things to buy things; you could not just produce money… has anyone ever mentioned this to the Federal Reserve?

Long before Say, a 16th century English financier named Sir Thomas Gresham noticed that if people had good money and bad money of equal purchasing power, they’d spend the bad money and hoard the good money.

Economists were like astronomers. When they discovered something new they named it after themselves. They were just observers back then and they needed some reward. No one hired them to ‘run’ an economy or to ‘improve’ one. They would have thought the idea absurd. How could they know what people wanted? How could a single person, or a single generation, improve an infinitely complex system that had evolved over thousands of years?

Central planners can rig the economy to produce anything—tanks, education, bridges, bureaucrats, assassinations, you name it. But none of these things are priced in the open market, the way the original economists observed them at the birth of their discipline. These machinations are exceedingly annoying to the invisible hand. The reason is that it needs to see what things are really worth to us or it cannot properly allocate capital and guide consumers. Things that are not priced by willing buyers and sellers are like dark matter in the economic universe. They provide no light, no clarity, nothing that can help consumers, taxpayers, or investors decide what to do with their money. Many of the products and services commanded or provided by non-market entities are probably worthless; or worse, actually of negative value. That’s when the invisible hands starts drinking early.

By the Numbers

What is the meaning of life? In the Hitchhiker’s Guide to the Galaxy, Arthur Dent searches the interplanetary system for the answer. Finally, he finds a computer, Deep Thought, that tells him: “Forty-two.”

Wouldn’t it be nice if meaning could be digitized? Unfortunately for the deep thinkers in the economics profession, the important things in life involve qualitative judgments. Understanding them requires analogue thinking, not digital calculations.

Numbers are a good thing. Economics is full of numbers. It is perfectly natural to use numbers to count, to weigh, to study and compare. They make it easier and more precise to describe quantities. Instead of saying I drank a bucket of beer you say, I drank two 40s. Then instead of saying ‘I threw up all over the place,’ you say, I threw up on an area 4 feet square.

But in economics we reach the point of diminishing returns with numbers very quickly. They gradually become useless. Later, when they are used to disguise, pervert and manipulate, they become disastrous. Hormegeddon by the numbers. Ask Deep Thought the meaning of life then and the answer is likely to be “Negative Forty-Two.”

Exactly what point does the payoff from numbers in the economics trade become a nuisance? Probably as soon as you see a decimal point or a Greek symbol. I’m not above eponymous vanity either. So I give you Bonner’s Law:

In the hands of economists, the more precise the number, the bigger the lie.

For an economist, numbers are a gift from the heavens. They turn them, they twist them, they use them to lever up and screw down. They also use them to scam the public. Numbers help put nonsense on stilts.

Numbers appear precise, scientific, and accurate. By comparison, words are sloppy, vague, subject to misinterpretation. But words are much better suited to the economist’s trade. The original economists understood this. Just look at Wealth of Nations—there are a lot of words in that thing. After all, we understand the world by analogy, not by digits. Besides, the digits used by modern economists are most always fraudulent.

“Math makes a research paper look solid, but the real science lies not in math but in trying one’s utmost to understand the real workings of the world,” says Professor Kimmo Eriksson of Sweden’s Malardalen University.

He decided to find out what effect complicated math had on research papers. So, he handed out two abstracts of research papers to 200 people with graduate degrees in various fields. One of the abstracts contained a mathematical formula taken from an unrelated paper, with no relevance whatever to the matter being discussed. Nevertheless, the abstract with the absurd mathematics was judged most impressive by participants. Not surprisingly, the further from math or science the person’s own training, the more likely he was to find the math impressive.

This is from a research paper paid for by the Federal Reserve. It purports to tell us that when a house next door to you sells at an extremely low firesale price, your house gets marked down too:

This motivates estimation of the following linear probability model:

I attempted to put in another illustration, a model in which economists believe they calculate the effect of large-scale asset purchases by the Fed (aka Quantitative Easing), but my trusty laptop computer rebelled. It wouldn’t copy the formula. The ‘clipboard’ wasn’t big enough, or so it claimed at least. I suspect the real reason was moral and political indignation was; a laptop knows a digital fraud when it sees one.

Without coming to any conclusion about how good these formulae actually are, let us look at some of their components. Whereas the classical economist—before Keynes and econometrics—was a patient onlooker; the modern, post-Keynes economist has had ants in his pants. He has not the patience to watch his flock, like a preacher keeping an eye on a group of sinners, or a botanist watching plants. Instead, he comes to the jobsite like a construction foreman, hardhat in hand ready to open his tool chest immediately; to take out his numbers.

Measuring Quantity vs. Quality

If you are going to improve something you must be able to measure it. Otherwise how do you know that you have made an improvement? But that is the problem right there. How do you measure improvement? How do you know that something is ‘better?’ You can’t know. ‘Better’ is a feature of quality. It can be felt. It can be sensed. It can be appreciated or ignored. But it can’t actually be measured.

What can be measured is quantity. And for that, you need numbers. But when we look carefully at the basic numbers used by economists, we first find that they are fishy. Later, we realize that they are downright fraudulent. These numbers claim to have meaning. They claim to be specific and precise. They are the basis of weighty decisions and far-reaching policies that pretend to make things better. They are the evidence and the proof that led to thousands of Ph.D awards, thousands of grants, scholarships and academic tenure decisions. More than a few Nobel Prize winners also trace their success to the numbers arrived at on the right side of the equal sign.

1… 2… 3… 4… 5… 6… 7… 8… 9…

There are only 9 cardinal numbers. The rest are derivative or aggregates. These numbers are useful. In the hands of ordinary people they mean something. ‘Three tomatoes’ is different from ‘five tomatoes.’

In the hands of scientists and engineers, numbers are indispensable. Precise calculations allow them to send a spacecraft to Mars and then drive around on the Red Planet.

But a useful tool for one profession may be a danger in the hands of another. Put a hairdresser at the controls of a 747, or let a pilot cook your canard à l’orange, and you’re asking for trouble. So too, when an economist gets fancy with numbers, the results can be catastrophic.

On October 19, 1987, for example, the bottom dropped out of the stock market. The Dow went down 23%. “Black Monday,” as it came to be called, was the largest single-day drop in stock market history.

The cause of the collapse was quickly traced to an innovation in the investment world called “portfolio insurance.” The idea was that if quantitative analysts—called ‘quants’—could accurately calculate the odds of a stock market pullback, you could sell insurance—very profitably—to protect against it. This involved selling index futures short while buying the underlying equities. If the market fell, the index futures would make money, offsetting the losses on stock prices.

The dominant mathematical pricing guide at the time was the Black-Scholes model, named after Fischer Black and Myron Scholes, who described it in a 1973 paper, “The Pricing of Options and Corporate Liabilities.” Later, Robert C. Merton added some detail and he and Scholes won a Nobel Prize in 1997 for their work. (Black died in 1995.)

Was the model useful? It was certainly useful at getting investors to put money into the stock market and mathematically-driven hedge funds. Did it work? Not exactly. Not only did it fail to protect investors in the crash of ‘87, it held that such an equity collapse was impossible. According to the model, it wouldn’t happen in the life the universe. That it happened only a few years after the model became widely used on Wall Street was more than a coincidence. Analysts believe the hedging strategy of the funds who followed the model most closely—selling short index futures—actually caused the sharp sell-off.

“Beware of geeks bearing formulas,” said Warren Buffet in 2009.


Bill Bonner is an American author of books and articles on economic and financial subjects, the founder and president of Agora Publishing, co-founder and regular contributor to The Daily Reckoning, and author of a daily financial column, Diary of a Rogue Economist.

He is author and co-author of Financial Reckoning Day: Surviving The Soft Depression of The 21st Century, Empire of Debt and Mobs, Messiahs and Markets.

Click here to order Bill Bonner’s forthcoming book: Hormegeddon: How Too Much of a Good Thing Leads to Disaster.

This excerpt, and Part One that appeared yesterday, first appeared at the Casey Daily Despatch.

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