| Home | My photos | Forex | My trading | Contacts |
|
|
You Can't Become Rich In Your Pocket Until You Become Rich In Your Mind | ||||
|
Economists systematically ignore data that fail to fit their preconceptionsTHE VIRTUAL REALITY OF CLASSICAL ECONOMICS ECONOMICS: THEORY VS. REALITY The history of Western philosophy can be read as brilliant individuals, starting from premises that are plausible and arguing meticulously to conclusions that are preposterous: motion is impossible; absolute beauty is real but my desk is not; the most perfect being imaginable must exist; the indubitable fact that I think guarantees both the existence of God and the veracity of my perceptions; it is impossible to have empirical knowledge; it is not even possible to have evidence; nothing can exist unless it is perceived; all truth is ultimately subjective; it is logically impossible to dream. It is easy, if not entirely fair, to poke fun at philosophy. Yet this shows how readily we can be misled by plausible assumptions and cogent argument. Where the conclusions are absurd, it is easy to realize something must have gone wrong and return to consider the assumptions and argument more carefully. This is part of the value of philosophy. But where the conclusions are politically correct, critical analysis is more difficult and we can end up embracing ridiculous views. Where the conclusions have practical consequences, there is risk of traumatic effect. Economic thought often fits this pattern. For example, it is reasonable that labor and leisure are mutual tradeoffs. The higher the price of labor, the greater is the incentive to work rather than to enjoy leisure. And the ensuing argument, including the observation that it is always possible to offer to work for so little that one will be hired, is cogent, if not entirely convincing. But the conclusion that all unemployment is voluntary, accepted by some economists, turns on an extraordinary extension of the normal meaning of “voluntary” and is as ludicrous as any conclusion reached by philosophers. Its implication that unemployment insurance is unnecessary, if taken seriously, risks trauma. Economists commonly pay more attention to the structure of an economic argument than to the accuracy, or even common sense, of its conclusion. You need assumptions with certain features to take advantage of the mathematical tools of modern economics, so you tailor your assumptions to the mathematics. The problem lies with the distance between those assumptions and reality. You would think that classical economists look under streetlights to find their keys, no matter where they drop them — for you need light, to find your keys. So you tailor your field of vision to take advantage of the light just as you tailor your assumptions to take advantage of the mathematics. Not surprisingly, there are many examples of failing to find the keys under the streetlight. Too often, economists are so taken in by the beauty of their theoretical models that they pay insufficient attention to reality. In the spirit of diligently searching the ground under the streetlight, sophisticated models with superficially plausible assumptions may appear attractive. But it can be foolhardy to take them too seriously. The management team of Long Term Capital Management included two Nobel laureates, famous for their pioneering work in the pricing of options and derivative instruments. Still, that hedge fund lost so much money that its bailout had to be orchestrated by the Federal Reserve with the help of 14 banks and investment firms. Could there have been discrepancies between the theoretical models and the real world? This is not an isolated example. Where economists’ models conflict with historical experience, they ignore history. How different is this self-assured confidence in the efficacy of mathematical models from the caution of Alfred Marshall, the founder of mathematical economics. “I go more and more on the rules 1) Use mathematics as a shorthand language rather than as an engine of inquiry. 2) Keep to them until you have done. 3) Translate into English. 4) Then illustrate by examples that are important in real life. 5) Burn the mathematics. 6) If you can’t succeed in 4, burn 3. This last I do often” (quoted in Ormerod, Butterfly Economics, p. 60). The methodological poverty of modern economic modeling stems from the ability of computer models to prove virtually anything. It is often possible to work backwards from the desired results to obtain the computer models that will generate them. So there is minimal significance in the fact that there is a computer model that generates a particular set of results. Benjamin Disraeli, living today, might have remarked: “There are three kinds of lies: lies, damn lies, and computer models.” Contemporary economists, mesmerized by their theoretical models, argue for flat taxes. They do so despite the fact that in the past, lowering tax rates on the highest incomes has had negative economic consequences. In the same spirit they flaunt computer models that demonstrate the benefit of free trade to all parties, even though these models contradict common experience. These models “prove” that tariffs cause inflation and stunt economic growth. While this follows from reasonable assumptions and may be true in some virtual world, it has not been true in our history. Our low inflation industrial boom of the nineteenth century began with protectionist legislation that decimated foreign trade. Throughout the century we protected domestic industry with high tariffs. Similarly, Japanese protectionism did not cause inflation (lower than ours) or stunt their economic growth (higher than ours). Economists systematically ignore data that fail to fit their preconceptions, especially when the data are politically incorrect. Presently it is politically correct to maintain that government regulation destroys the incentive to be efficient. Inversely, deregulation increases competition. Increased competition, in turn, must increase the incentive to innovate and provide better service at lower costs. So efficiency and service must improve and costs must fall when industries are deregulated. The theory sounds so very impressive. But consider two of the largest industries deregulated in the past 30 years, airlines and long-distance telephone service. Airlines appeared an ideal industry to deregulate. The business is not a natural monopoly and the ease of entry is above average, insuring vigorous competition. Yet “The Bureau of Labor Statistics calculates that inflationadjusted average fares were basically flat between 1967 and 1979 — despite sharply rising fuel prices — but rose some 50 percent in the subsequent decade.” (Kuttner, Everything for Sale, p. 259) While fares went up under deregulation, quality of service, from legroom to food to percentage of direct flights, declined. In telecommunications long-distance rates did continue to decline after deregulation, but at a slower rate than prior to deregulation. The practical results of deregulation fell far short of the theory. Economists’ models defending free trade have fared no better. The greater the ratio of our trade to our GNP, the higher has been our unemployment. But these facts do not fit the accepted orthodoxy and the political correctness of free trade, so economists pay them no attention. It is worse. Economists’ glorification of jobs created by exports is specious. It disregards the obvious fact that we lose many more jobs to imports than we create by exports. The persistent deficit in our balance of trade is an economic drag, slowing economic growth and increasing unemployment. At a time that our trade deficit was half its current level, Stone and Sandhill (Labor Market Implications of the Growing Internationalization of the U.S. Economy), and Duchin and Lange (Trading Away Jobs: The Effect of the U.S. Merchandise Trade Deficit on Employment), estimated the number of net jobs lost because of trade at between 1 and 5 million. When domestic unemployment is a serious problem, this is hardly a benefit. (Those who blame the Smoot-Hawley Act of 1929 for restricting trade and thereby causing the Great Depression forget that the $600 million decline in our net exports accounted for only 1% of our $50 billion decline in nominal GNP, that the previous Fordney-McCumber Act [1922] increased tariffs as much as Smoot-Hawley with no negative economic effect, and that Smoot-Hawley was passed only after the stock market had begun its precipitous decline. They also forget that a change in the balance of trade for one country generates an equal and opposite change in the balance of trade for its trading partners. But our trading partners went through depressions just like ours.) Economists “prove” if there is equal access to technology, then free trade will ultimately equalize wages of the trading partners. This has been an important component of arguments for free trade. But it leaves out critical considerations. Not only is its conclusion implausible, but we are all familiar with data that contradict it. Our own history shows the invalidity of this free trade argument. Within the U.S. we have had free trade, equal access to technology, and more. For centuries we have had similar language and customs as well as freedom of movement from any state to any other. In spite of this, the average resident of the richest states still earns nearly twice as much as the average resident of the poorest states. For centuries the per capita income in Paris has been twice that in Brittany, despite free trade and equal access to technology. Even if richer countries allow unrestricted access to technology, only they can provide the capital investment necessary to its profitable application. Only they can afford to build infrastructures necessary for the creation of additional wealth. So even if there is equal access to technology, free trade benefits only the rich countries. This explains why it is the rich countries that have advocated, and even insisted on, free trade, but it does not equalize wages. You would think, and hope, that this history would make classical economists reflect on their assumptions. But in the face of a formidable array of practical counterexamples to theoretical claims supporting free trade, laissez faire economists have maintained faith in their theoretical models. They have argued passionately on behalf of NAFTA. They have cavalierly dismissed the worry that free trade could pose any threat to domestic labor. (Yet now that we see the effects of a worldwide labor market, even Robert Reich has acknowledged the threat posed by global pricing of labor.) Consider, too, the impact of opening a Walmart in a Mexican community. How many mom and pop retailers and suppliers are displaced? What is the effect on them and their families? What happens to the community as a result of their inability to support themselves? Are the profits worth the dislocation and suffering, the potential destabilization of the community? (One omission of NAFTA may provide insight into the motivation underlying that agreement. U.S. drug companies manufacture many of the same pharmaceuticals in Mexico that they make in the U.S. These pharmaceuticals sell in Mexico for a small fraction of their U.S. prices. But it is illegal, even for pharmacists, to import these cheaper but identical drugs. This suggests that the prime purpose of NAFTA is to bolster profits by providing our corporations access to a large pool of cheap labor. The purpose has been justified by the claim that it makes us more competitive. But who is this “us”? Just who benefits from “our” greater competitiveness? In the same spirit, was our 1995 bailout of Mexican debt designed to benefit that country and its citizens, or was it designed to benefit the investors who had imprudently purchased Mexican government bonds — whose price had earlier reflected the high degree of risk?) Simply, globalization favors the rich, as it always has. And polls show it is the rich, and only the rich, who favor globalization. Why haven’t these obvious flaws in free trade arguments and policies shaken our faith? Our continued faith in laissez faire, its policies and its predictions, is testimony to the power of widely accepted beliefs to withstand the clearest counterexamples. There are yet other counterexamples to this faith. Since Alfred Marshall (and the notion, in non-mathematical form, can be traced back to Malthus and Ricardo) it has been a mainstay of classical economics that prices are stable at the marginal costs of production. Commodities should fit this picture ideally, for there are many independent producers and consumers and little opportunity distort the price structure of an auction market. At least in theory, a price advance should encourage new production and reduce demand, forcing prices back down. A price decline should cause production cuts and stimulate new demand, forcing prices back up. Perversely resistant to classical economic theory, most commodity prices vary regularly from below the marginal costs of production to several times those costs. Commodity (and stock and bond) prices oscillate in regular cycles with considerable amplitude and without damping. That these cycles are ubiquitous and persistent suggests they may be natural. They appear even before the Industrial Revolution. “Europe in the fifteenth, sixteenth and seventeenth centuries, although far from presenting a unified picture, was already clearly obeying a general series of rhythms, an overall order.” (Braudel, The Perspective of the World, p. 75.) Despite its incompatibility with fundamental principles of classical economics, the natural cyclicality of a metals market with constant demand can be simply explained (and without requiring the series of random exogenous shocks assumed by modern business cycle theorists). Suppose initial prices are “too high.” High prices ? (lead to) high profit projections ? more investment in new projects ? more new projects ? increased production ? greater supply ? lower prices ? lower profits (or losses) ? production cutbacks and reduced exploration and development ? decreased production ? less supply ? higher prices. Because of time lags, prices can rise or decline far from their equilibrium level. New ore bodies must be discovered, reserves proven, metallurgical testing carried out and problems with refractory metallurgy solved, projects permitted, and a mine plan designed. Capital must be raised. Machinery must be ordered, built, delivered, installed and tested. An infrastructure must be developed. These steps can take years, during which the shortage of metal drives prices far above equilibrium, encouraging the financing of many new mines. By the time the new mines begin production, so many have been financed and developed that the flood of new production depresses prices below the marginal cost of production for years. Because it is expensive to close a mine, many of these mines remain in production in spite of ongoing losses. Silver prices peaked in 1980 at $50 per ounce. But despite a price decline of more than 90% over the next 10 years, production, much of which had been planned and financed near the peak of the cycle, increased in each of those years (except 1986), by a total of 40%. By 1990 all the new silver mines were losing money. Even the older ones did poorly. Coeur D’Alene and Sunshine Mining (which recently declared bankruptcy) were in the red every year of the 1990s, and Hecla lost money in nine of the ten years. Silver prices have finally turned. But silver companies have been conserving cash for more than a decade by cutting back on exploration. Few new large deposits have been discovered, few new mines have been placed in production, and inventories have continued to decline, with COMEX inventories less than 10% of their peak levels of 1980. Given the long lead-time between capital investment and increased production and also the multi-decade price cycles, it would make sense for companies to concentrate their expansion plans near the troughs of those cycles. But the financial markets are too shortsighted and financiers extrapolate trends linearly. They assume prices will remain stable or continue in the direction of the past few years. Laissez faire gives them no reason to do otherwise; so most plans to increase capacity are made at cycle peaks, at the worst possible time. This is part of a broader pattern. In the late 1970s, when energy prices were high and rising, unlimited capital was available for even marginal energy projects. When energy prices declined in the 1980s, principals folded and funds invested in this sector were lost. Now we have come half circle. It is no longer energy that excites investors. Instead, it is the technology sector that has been soaring. As a result, money has been thrown at technology stocks, from Internet companies with little prospect of ever earning a dime to semiconductor manufacturers who decided to add to capacity at the worst possible time. It is likely that most funds invested in this sector will be lost. |
|
|||||||||||||||
Previous Issues
|
| ©2007 Olesia | Home • My photos • Forex • News • My trading • Contacts |