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GUANGZHOU, China — For the first time, Chinese will buy more cars this year than Americans. Demand is so high that drivers put their names on long waiting lists for the most popular models.

“I’m disappointed, but what can I do?” asked Zhang Ge Lu, a 28-year-old interior designer. He came recently with two friends to a row of dealerships here in southeastern China to buy a black Toyota RAV4, only to be told that he would have to wait two months for delivery.

And it is not just cars. For more and more consumer goods, China is surpassing the United States as the world’s biggest market — from cars to refrigerators to washing machines, even desktop computers.

The Chinese market is “on full tilt — booming is an understatement these days,” said John Bonnell, the director of Asia vehicle forecasting at J.D. Power & Associates.

China is pulling ahead at this particular moment partly because Americans, debt-laden and worried about their jobs, are pulling back. After decades of gorging on consumption, Americans are saving. And the Chinese, whom economists thought were addicted to saving, are spending more.

Among China’s 1.3 billion people, rising incomes are finally making large numbers of Chinese prosperous enough to make big-ticket purchases.

The question is: will they keep spending? The Beijing government is increasing consumption with rebates, subsidies and heavy bank lending. Whether China can turn the spending spree into the seeds of a true consumer society matters not just to China, but to the world.

For years, the West has pushed China to increase domestic consumption and reduce its dependence on exports — that’s because its overdependence on exports has distorted global trade.

To keep its export machine humming, China kept its currency undervalued to make its goods more competitive in foreign markets. The county beggared its own citizens, keeping salaries and bank deposit interest rates artificially low to support exporters.

China’s trade surpluses and extensive intervention in currency markets have led it to amass $2.27 trillion in reserves, mainly in United States Treasuries, mortgage-backed securities and other dollar-denominated investments, helping to keep interest rates low and finance Americans’ borrowing. Chinese parsimony enabled American profligacy.

If the Chinese buy more and Americans save more, a more stable global economic exchange can take shape. In the meantime, China’s rapid consumption growth is good news for the whole world. For the first time, China, not the United States, is a locomotive helping to pull the global economy out of a slump. But China’s tiny appetite for American exports means that the main benefit has gone to commodity exporters and to businesses in China.

Automakers are on track to sell 12.8 million cars and light trucks in China this year, virtually all of them made in China (although many are foreign brands), compared with 10.3 million in the United States. Appliance manufacturers expect to sell 185 million refrigerators, washing machines and other pieces of kitchen and laundry equipment in China this year, compared with 137 million in the American market.

In desktop computers, China moved solidly ahead of the United States in the third quarter, buying 7.2 million compared with 6.6 million in the United States.

Retail sales are growing 17 percent a year in China after adjusting for inflation, almost twice as fast as the overall economy.

Americans have been cutting back on purchases of everything from shoes to furniture to jewelry. But Chinese households are crossing a series of income thresholds at which cars and other big-ticket purchases become affordable.

At the same time, Chinese banks are stepping up consumer lending. The proportion of car sales financed with loans has doubled this year, to nearly 25 percent, although most Chinese still head for dealerships with bricks of 100-renminbi notes, each note worth about $14.62. Credit card spending rose 40 percent in the first nine months of the year compared with the same period last year, yet China still has just one credit card for every eight people, compared to two credit cards for each American man, woman and child.

While it is spreading creature comforts, China’s lending-based prosperity may also be sowing the seeds of future economic problems. China’s Banking Regulatory Commission recently told banks to show restraint in lending for the rest of the year, fearful that some of this year’s loans could become bad debts in the next several years, as happened with the mortgage lending spree in the United States.

The regulator threatened to block banks’ overseas investments and branch openings unless they can demonstrate adequate capital to cover risks.

The size of China’s consumer market, notwithstanding its growth, will make it hard for China to rescue the world economy by itself. Total consumer spending in China is still less than a sixth of American consumer spending at current prices and exchange rates. That is mainly because China has relatively few restaurants, hotels and other service businesses, even as sales of manufactured goods have risen.

The average price tags on most Chinese products are much lower than in Western markets. For many products, including some in which China leads in the sheer number of goods, the total dollar value of sales in China is still smaller than in the United States.

The average new car sells for $17,000 in China compared with almost $30,000 in the United States, according to J.D. Power. This is because Chinese consumers buy more subcompacts and fewer sport utility vehicles. While the Chinese market is one-quarter larger in the number of cars sold, the American market is still about two-thirds larger in dollar terms.

Similarly, the United States market for household appliances is a third larger in dollars, even though the Chinese market is a third larger in the number of appliances. Cooking ranges in China are sold for countertop installation without a lot of other equipment, for example.

“You don’t have the cook-a-turkey-in-the-oven type of product in China, because we don’t have that kind of cooking,” said Philip S. Carmichael, the president of Asian operations at Haier, China’s biggest appliance manufacturer.

But in some sectors, Chinese buyers are already proving more lavish than Americans. The average flat-panel television sold in China is bigger than in the United States, according to AU Optronics of Taiwan, the world’s third-largest manufacturer of flat-panel televisions.

When car sales began surging early this year, many auto executives attributed the boom to government incentives. To stimulate the economy, the government has offered rebates for rural families to buy cars and household appliances, and has cut sales taxes on cars with small engines.

But the boom has broadened to categories that barely qualify for incentives.

S.U.V. sales rose 72 percent in October from a year earlier. At Nissan, sales of cars with larger engines that do not qualify for the sales tax reduction are growing even faster than sales of small-engine cars.

Auto sales jumped 42 percent in the first 11 months of this year compared with sales in the same period last year. And sales are still accelerating, soaring 96 percent in November compared with the same month a year ago. Auto sales in the United States plunged 37 percent last month on the same basis.

China’s consumers have the potential to buy even more in the years ahead. The savings rate is close to 40 percent — and will remain high unless and until Beijing creates a social safety net for things like health care or retirement, which would encourage Chinese to spend more today.

And though annual incomes still average just $2,775 a person in cities and $840 in rural areas, Western economists predict the economy will grow almost 12 percent in each of the next two years and the renminbi is widely expected to appreciate someday, further increasing consumers’ buying power.

Hilda Wang contributed reporting.

FOR farmers, working out the optimal amount of seed, fertiliser, pesticide and water to scatter on a field can make, or break, the subsequent harvest. Regular laboratory analyses of soil and plant samples from various parts of the field can help—but such expertise is costly, and often unavailable. A new and cheaper method of doing this analysis, though, is now on offer. Precise prescriptions for growing crops can be obtained quickly, and less expensively, by measuring electromagnetic radiation reflected from farmland. The data are collected by orbiting satellites.

The spectrum of this radiation—which can be in the form of either natural sunlight or artificial radar—can reveal, with surprising precision, the properties of the soil, the quantity of crop being grown, and the levels in those crops of chlorophyll, various minerals, moisture and other indicators of their quality. If recent and forecast weather data are added to the mix, detailed maps can be produced indicating exactly how, where and when crops should be grown. The service usually costs less than $15 per hectare for a handful of readings a year, and can increase yields by as much as 10%.

Such precision farming using satellite-based intelligence is in its infancy. Even so, it is catching on quickly. Five times a year, for example, a satellite-surveillance service provided by a cereal-growers’ co-operative called Sevépi (based in Douains, France) e-mails its members a map of their fields, divided into three or four colour-coded zones per hectare. For each zone, one of about 50 fertiliser formulae is recommended. On top of this, if the stems of the wheat are tall and rain is expected, an appropriate dose of growth-regulator is recommended for each zone. (Long, fragile stems snap more easily in downpours.) Farm vehicles equipped with global-positioning-system locaters automatically mix and apply the prescribed dose to each area.

France is the leader in this sort of surveillance. More farmland is analysed by satellite there than in any other country, according to Infoterra (a subsidiary of EADS Astrium, a European space giant), the firm that is France’s largest provider of such information. Moreover, Henri Douche, head of Infoterra’s agriculture sales in Toulouse, reckons the amount of monitored farmland will increase as the climate changes and farmers can no longer rely on the past as a guide to the future. When confounded by the yield variations that new weather patterns bring, even technophobic farmers will sign up, he says.

Inexpensive data on the productivity of land is valuable to governments, too. Areas where fertilisers and pesticides are being applied excessively can be pinpointed, studied and regulated by environmental and land-use agencies. Guy Lafond, an agronomist with Agriculture and Agri-Food Canada, a government agency, says satellite data are proving useful for a study of fields with declining productivity in Saskatchewan. Overkill with nitrate fertilisers (which are also a source of greenhouse gases) appears partly responsible. And according to RapidEye, a German satellite operator, insurance companies are also studying satellite data with a view to selling crop-insurance policies to governments of countries that might be threatened by famine.

In March, RapidEye began selling data that help forecast harvests. Too often, farmers limit productivity by managing fields uniformly, says Frederik Jung-Rothenhäusler, head of product development at the firm’s headquarters in Brandenburg an der Havel. The company’s data, which cover both Europe and the Americas, break field productivity down into patches just five metres square.

Nor need the boon of precision farming be restricted to rich countries. In Africa, where many soils have become badly depleted of nutrients, better fertiliser management would go a long way. As a consequence, the World Agroforestry Centre in Nairobi has begun cataloguing the radiation signature—and thus agricultural potential—of about 100,000 samples of African soils. It is giving this detailed information to the International Centre for Tropical Agriculture, based in Colombia, so that it can build a database called the Digital Soil Map. When ready, this will provide farmers with free forecasts, developed with regularly updated satellite imagery, across farmland in 42 African countries. For a hunger-ravaged continent, that is good news indeed.

From the WSJ Opinion, November 18, 2009

By Michael J. Boskin

While the economy has finally started to grow, the disturbingly high unemployment rate is increasing pressure from the left to double down on this year’s poorly designed fiscal stimulus. Since the stimulus bill was signed, the ranks of the unemployed have grown by over three million (over four million if involuntary part-time and discouraged workers are included). The unemployment rate, which the Obama administration projected the stimulus would contain at 8%, is now 10.2%.

There is little likelihood that another round of similar fiscal stimulus would yield much more than the paltry return on the first one. The original transfer payments and tax rebates barely nudged consumer spending, and the federal spending has been painfully slow. The delayed infrastructure spending—the shovels are still in the shed—will have a bigger impact, though less than claimed. Some of the funds to state and local government did reduce layoffs. The stimulus bill surely ranks dead last compared to the natural dynamics of the business cycle, the Fed’s zero interest rate policy, and the automatic stabilizers in the tax code (which have reduced taxes proportionally more than income) as far as explanations for the improvement in the economy.

But to evaluate the stimulus properly we should consider not just what we got for the $787 billion cost but the effects of alternative policies that might have been enacted.

My Stanford colleague Pete Klenow and Rochester economist Mark Bils estimated that cutting the payroll tax by six percentage points (of the 12.4% Social Security component) would, under standard assumptions, increase employment by three million to four million workers—an amount equal to all the job losses since the stimulus was passed.

The payroll tax cut would have reduced firms’ costs by roughly the same amount as from the entire decline in employment. It would have cost less than half as much as the stimulus bill, gotten far more income into paychecks quickly and, most important, greatly reduced incentives for firms to lay off workers. In fact, it would have created incentives to hire.

Even using the administration’s claims of one million jobs “created or saved,” the stimulus program passed in early February is millions of jobs short of what a cheaper payroll tax suspension would have delivered (see nearby chart).

Yet the president and Congress are preparing vast new taxes on employment in the health-care reform and other legislation. Raising the federal top tax rate to 45% (from the current 35% with a 5.4% surcharge plus the expiration of the Bush tax cuts) will hit successful small businesses especially hard. The tax hike on capital gains and dividends hidden in the fine print of the health-care legislation will also raise the cost of equity capital, further weakening businesses (including banks) desperate for private capital. Many firms will also face either an 8% additional payroll tax or be forced to pay a higher share of health insurance premiums. Such tax increases will hit employment and wages hard.

It would be far better to junk part of the remaining stimulus in favor of a one-year partial payroll tax cut. Also accelerate spending that needs to be done eventually, such as replenishing depleted military equipment used up in Iraq and Afghanistan and adding a desperately needed two Army brigades.

There are five large interrelated headwinds to jobs and growth. First, continued deleveraging, unresolved toxic assets and weak banks are constraining credit, especially for small business that is the source of most hiring. Second, household balance sheets depressed from declines in home values and portfolios are likely to constrain consumption growth. Third, government industrial-policy micromanagement with subsidies and mandates from pay to products is forcing noncommercial decisions on wide swaths of the economy from financial services and autos to energy and health care. Such policies have never worked before—ask the Japanese, Koreans and Europeans. Fourth, the explosion of spending, deficits and debt foreshadows even higher prospective taxes on work, saving, investment and employment. That not only will damage our economic future but is harming jobs and growth now. Fifth, the massive liquidity injections by the Fed raise the specter of future inflation.

By far the best response to these headwinds is to curtail the huge current and contemplated future government control of the economy with a clear, predictable exit strategy—before the programs become permanently entrenched, develop powerful dependent constituencies, and greatly increase the risk of rising interest rates, inflation and taxation. Doing so would more rapidly improve the outlook for permanent private-sector employment, investment and growth than any conceivable second stimulus. It would also allocate capital and labor to their highest value in providing goods and services that people actually want and need, not what government bureaucrats want them to have.

The jobs agenda must begin with a Hippocratic oath: First do no harm to employment. That means jettisoning or at least delaying job-killing energy and health-care legislation with their mandates, taxes and costs that especially hammer small businesses.

Also wind down, as soon as possible, the emergency measures which healthy businesses, households and investors fear will become permanent competitive impediments. Start with the Troubled Asset Relief Program, which the Treasury uses as a permanent revolving fund even for nonfinancial bailouts.

Financial regulation should focus on disclosure, transparency, effective clearing, capital adequacy and new bankruptcy procedures. We also need a Plan B, modeled on the Resolution Trust Corporation cleanup of the savings and loans, in the event the losses on toxic assets are too large for time, profitability and economic recovery to manage. And the Fed must forestall future inflation by withdrawing its immense liquidity injections as soon and predictably as feasible (its initial steps are commendable).

Finally, if possible, we should complement these pro-employment policies with long-run fiscal reform: control entitlement cost growth, e.g., with price rather than wage indexing of Social Security, and real tax reform with the widest possible tax bases and lowest possible rates. America’s corporate tax rate, the second highest among advanced economies, is especially damaging.

That is a far more consistent common-sense recipe for more and better jobs, far sooner than the current contradictory and ineffective policy mess emanating from Washington.

—Mr. Boskin is a professor of economics at Stanford University and a senior fellow at the Hoover Institution. He chaired the Council of Economic Advisers under President George H.W. Bush.

By Carolyn Cui

Electronic trading was supposed to bring improvements to the insular world of cocoa-futures trading, where candy companies and cocoa dealers spent decades jostling in trading pits at the New York Board of Trade.

Two years into the electronic era, the cocoa market is in disarray. Market makers have disappeared. Overall volume has shrunk 20%.

Prices have swung sharply for months, creating headaches for chocolate makers, many of whom no longer use the exchange to hedge costs.

The problems in this obscure corner of the financial markets have hit consumers. Cadbury PLC has shrunk its Dairy Milk candy bars, while Mars Inc. reduced its Galaxy chocolate bars. Many candy companies have raised prices, too.

Previously, the New York Board of Trade’s cocoa trading pit opened at 8 a.m. New York time and closed at 11:50 a.m. It was populated by a group of roughly 50 floor brokers, who acted as middlemen between big buyers and sellers of cocoa. Known in the pits as “locals,” the group gleaned informational tidbits that they believed made them smarter traders.

“It was a fantastic time,” said Paul Dapolito, president of Dapco Brokerage, which was the largest cocoa broker by trading volume. An average floor trader made $250,000 a year, and some could make as much as $1 million. Young traders usually went for beers after the market closed.

Then, IntercontinentalExchange Inc. purchased the New York Board of Trade in 2007, and converted it to an all-electronic exchange.

The move to all-electronic trading meant that virtually any one could get involved in the cocoa markets, buying and selling futures contracts online. The hope was to make the cocoa markets more like other commodities, such as oil, where contracts are traded almost around the clock.

But the move had the opposite effect on commodities like cocoa: Many cocoa floor traders and brokers, who made up about 40% of the market, have quit. Dapco Brokerage, which used to handle 30% of the cocoa trading on the floor, went out of business a year ago.

Its departure scared off other electronic-commodities traders, who have stayed out of the $4.3 billion market because of high volatility and thin liquidity.

The liquidity crunch became more pronounced as the exchange extended the trading hours in 2007 in an effort to attract traders in Europe and Asia.

Trading now begins at 4 a.m. New York time and ends at 2 p.m. About 80% of trading still is concentrated between 8 a.m. and noon, when U.S. traders are awake. Sometimes, fewer than 100 contracts change hands each hour in the early mornings.

That has made the market for cocoa a highly volatile one. Cocoa surged 65% in the first half of 2008, to a 28-year high of $3,360 a ton on July 1, then tanked 43% in four months before recovering 77% to hit $3,392 in late October, the highest since June 1979. On Wednesday, cocoa settled at $3,199, up 3.7%.

Soaring prices of cocoa drove chocolate makers to raise prices and cut the size of candy bars since mid-2007.

In August 2008, Hershey Co. raised prices an average of 11% to offset “significant increases” in the cost of raw materials such as cocoa, sugar and peanuts, said Hershey Chief Executive David West in a statement at the time. Andrew Bonfield, Cadbury’s chief financial officer, said on a September analysts’ call that reducing bar sizes enabled the company to avoid raising prices.

During the first 10 months of 2009, cocoa’s daily trading volumes fell 14% to a level not seen since 2005.

People who depend on the market are looking for changes, including a reduction in trading hours. Wednesday, a group including Mars, Hershey, Nestlé SA, Archer Daniels Midland Co., Cargill Inc., and hedge funds Armajaro (USA) Inc. and Perennial Capital pushed the exchange at a meeting to shorten the trading hours from 8 a.m. to 1 p.m., a time when most transactions occur.

ICE has resisted calls to reduce trading hours. The exchange brands itself as a global marketplace and wants to keep hours long enough to compete with NYSE Liffe, which has a similar cocoa contract in London trading during similar hours.

The exchange is careful to “balance the concerns of the market where we think we can improve the markets, be it hours or contract sizes,” ICE spokeswoman Kelly Loeffler said.

ICE declined to comment on the meeting.

Nicholas Gentile, who started in the cocoa pit as a broker in 1992, trades far less cocoa than he once did. Instead of rubbing shoulders with other traders in the pits, looking to pick up pieces of information that may give him an advantage, Mr. Gentile spends his days in his office, staring at screens and reading research reports.

Now that he is confined to an office, Mr. Gentile sees no incentive to trade cocoa exclusively. Without the floor buzz, “you trade everything the same way,” said Mr. Gentile, who trades about 5% of the cocoa contracts than he used to.

The thinning market, he said, has limited his ability to trade “spreads,” or bets on the differential between two contracts. Mr. Gentile said he now trades more grains.

Mr. Gentile gets up at 3:50 a.m. every day and sometimes doesn’t brush his teeth or shower until the afternoon. “I know we were spoiled working on the floor,” he said.

(Sept. 21, 2009) Corn at $3—the notion was unimaginable a year ago when corn was at $5.49, or even two months ago, when it was at $4.48.

Taken together with a drop in soymeal prices, which fell from around $400 to the $340s, it represents huge cost savings for poultry producers and signals that poultry output will likely stop contracting. Input costs are at the point where cheaper breast prices will be OK, given leg quarters in the 40-cent range and wings in the $1.40 range. Egg and turkey producers could also be inspired to ease off the brakes. Dairy and hog producers may even start breathing again by 2010.

So what will happen with prices? That depends. With unemployment jumping to 9.7 percent, consumer demand won’t be bouncing back anytime soon. However, Asian economies are already perking up, and a weak U.S. dollar combined with cheap U.S. commodity prices could mean an imminent rebound in export sales. Keep an eye on crude oil prices as a trendsetter. Altogether, there’s the potential that we’ll see another commodity bubble in 2010.

Beef—The U.S. Department of Agricuture’s August cattle report showed 9.644 million head of cattle on feed, down 2.3 percent from a year ago and a record low for this time of year. July placements were up a larger-than-expected 13 percent from a year ago, as lower corn prices are drawing more young cattle off pasture and onto feedlots. July feedlot marketings were down 5 percent from a year ago, reflecting sluggish beef demand.

The seasonal peak in fed cattle supplies occurred in July and the bottom will come in November. However, middle-meat prices look to be relatively flat, with chucks and rounds seasonally higher. This year’s holiday celebrations—especially New Year’s Eve—will likely be subdued. Rib-eyes and tenders are headed higher, but will remain well below year-ago levels. The USDA now expects choice steers to average $85 per hundredweight in 2009, down from $92.27 in 2008. Steer prices are expected to rebound back to $92 in 2010.

Coffee–Coffee managed to trade higher in July based on risk premiums for a potential freeze in Brazil. When no damaging weather occurred, prices began to falter in August. Coffee futures, at $1.20 in early September, are down from $1.31 a month ago. But coffee is likely to be near a bottom, and further downside potential is limited. Look for roasters to start buying on market dips and building their supply positions for the upcoming winter consumption season in North America and Europe. Global Arabica, or mild, supplies are historically tight, but many consumers have traded down to cheaper Robusta blends.

Dairy—The USDA temporarily increased price support payments by 15 percent for nonfat dry milk and 16 percent for cheese. That move allowed block to jump from lows of $1.09 in mid-July to $1.40 in mid-August, before settling back to the mid-$1.20s. Uncertainty is reigning as buyers and sellers try to project beyond Oct. 31. Will the inflated “temporary” price support policy be extended into November, or possibly even raised, and by how much and for how long? The market will remain unsettled until these questions are answered.

Economic recovery in 2010 and slightly lower milk production should help boost prices for all products next year. Block cheese is projected to average more than $1.24 this year and climb to $1.56 in 2010. Butter is expected to average $1.20 in 2009 and strengthen to $1.50 in 2010. Milk prices should recover from 2009 lows next year, but should remain well below the highs of 2007 and 2008. The Class III price is expected to average $10.80 per hundred-weight in 2009 and rise to $14.25 in 2010. The all-milk price average is expected to be $12.20 this year and projected to rise to $15.15 in 2010.

Grain—In its August Supply & Demand Report, the USDA raised its 2009 U.S. corn forecast from 12.29 billion bushels to 12.76 billion bushels and bumped corn yield from 153.4 bushels per acre to 159.5 bushels per acre. U.S. 2009-10 corn-ending stocks were increased from 1.550 billion bushels to 1.621 billion bushels. World corn-ending stocks were increased from 139 million tons to 141 million tons. Corn futures, which had run up to $3.58 in August, bottomed at $3 on Sept 4. However, a projected 2009-10 increase in ethanol usage from 4.1 billion bushels to 4.2 billion bushels will likely keep a floor under corn prices near current levels. The USDA reduced its 2009-10 corn price forecast from $4.30 per bushel to $3.75 to $3.50 per bushel over the past two months.

The 2009 U.S. wheat forecast was raised from 2.112 billion bushels to 2.184 billion bushels, and the yield increased from 41.9 bushels per acre to 43.3 bushels per acre. U.S. 2009-10 wheat-ending stocks were increased from 706 million bushels to 743 million bushels. World wheat-ending stocks were increased from 181 million tons to 184 million tons. Chicago wheat futures dropped from $5.49 to $4.29 in five weeks. Global wheat prices could rally if weather risks in Australia and other major producing countries are realized, reducing expected global production. The 2009-10 forecast at $5.20 per bushel is down 10 cents from last month’s estimate and significantly below the 2008-09 price of $6.78.

Poultry—In first half of 2009, broiler meat production was down 5.8 percent from a year earlier. But only a small decline in output is expected for the second half and that, combined with lower exports, will raise net poultry supplies by year’s end. That will help keep breast prices relatively inexpensive for the balance of the year and maybe even provide a little opportunity on wing and leg quarters by November.

The big news in poultry is the pending $2.5 billion offer by Brazilian company JBS to buy Pilgrim’s Pride out of Chapter 11 bankruptcy protection. The deal would include full payment of all Pilgrim’s creditors to the tune of $2.2 billion. While the deal will be under government scrutiny, it represents a great offer, given that Pilgrim’s stock at a little over $5 per share gives the company a market capitalization of just $360 million. JBS acquired Smithfield’s beef division last year and Swift & Co. in 2007.

Soy oil—Year-end U.S. soybean stocks are expected to hit a 32-year low. But the new harvest starts in late September and is expected to be record large–potentially raising ending stocks from 110 million bushels at the close of 2008-09 to well over 300 million for 2009-10. For soy oil, much will depend on soymeal demand, which will dictate how many of those beans will be crushed. Soybean crush has been dropping, but cheaper, new-harvest soybeans should combine with a weaker dollar and drive better soymeal export demand later this year and in 2010.

Soy oil futures hit highs of more than 38 cents in mid-August, but the recent sell-off in commodities knocked prices back to the 34-cent range in early September. Fundamentally, prices should drift lower. But most of the trading lately has been either more technically driven, or tied to moves in crude oil or the dollar.

John T. Barone is president of Market Vision Inc. in Fairfield, N.J., and can be reached for comment at jbarone@mktvsn.com.

By Felix Salmon 02.23.09

In the mid-’80s, Wall Street turned to the quants—brainy financial engineers—to invent new ways to boost profits. Their methods for minting money worked brilliantly… until one of them devastated the global economy.

Road Map for Financial Recovery: Radical Transparency Now! A year ago, it was hardly unthinkable that a math wizard like David X. Li might someday earn a Nobel Prize. After all, financial economists—even Wall Street quants—have received the Nobel in economics before, and Li’s work on measuring risk has had more impact, more quickly, than previous Nobel Prize-winning contributions to the field. Today, though, as dazed bankers, politicians, regulators, and investors survey the wreckage of the biggest financial meltdown since the Great Depression, Li is probably thankful he still has a job in finance at all. Not that his achievement should be dismissed. He took a notoriously tough nut—determining correlation, or how seemingly disparate events are related—and cracked it wide open with a simple and elegant mathematical formula, one that would become ubiquitous in finance worldwide.

For five years, Li’s formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.

His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored.

Then the model fell apart. Cracks started appearing early on, when financial markets began behaving in ways that users of Li’s formula hadn’t expected. The cracks became full-fledged canyons in 2008—when ruptures in the financial system’s foundation swallowed up trillions of dollars and put the survival of the global banking system in serious peril.

David X. Li, it’s safe to say, won’t be getting that Nobel anytime soon. One result of the collapse has been the end of financial economics as something to be celebrated rather than feared. And Li’s Gaussian copula formula will go down in history as instrumental in causing the unfathomable losses that brought the world financial system to its knees.

How could one formula pack such a devastating punch? The answer lies in the bond market, the multitrillion-dollar system that allows pension funds, insurance companies, and hedge funds to lend trillions of dollars to companies, countries, and home buyers.

A bond, of course, is just an IOU, a promise to pay back money with interest by certain dates. If a company—say, IBM—borrows money by issuing a bond, investors will look very closely over its accounts to make sure it has the wherewithal to repay them. The higher the perceived risk—and there’s always some risk—the higher the interest rate the bond must carry.

Bond investors are very comfortable with the concept of probability. If there’s a 1 percent chance of default but they get an extra two percentage points in interest, they’re ahead of the game overall—like a casino, which is happy to lose big sums every so often in return for profits most of the time.

Bond investors also invest in pools of hundreds or even thousands of mortgages. The potential sums involved are staggering: Americans now owe more than $11 trillion on their homes. But mortgage pools are messier than most bonds. There’s no guaranteed interest rate, since the amount of money homeowners collectively pay back every month is a function of how many have refinanced and how many have defaulted. There’s certainly no fixed maturity date: Money shows up in irregular chunks as people pay down their mortgages at unpredictable times—for instance, when they decide to sell their house. And most problematic, there’s no easy way to assign a single probability to the chance of default.

Wall Street solved many of these problems through a process called tranching, which divides a pool and allows for the creation of safe bonds with a risk-free triple-A credit rating. Investors in the first tranche, or slice, are first in line to be paid off. Those next in line might get only a double-A credit rating on their tranche of bonds but will be able to charge a higher interest rate for bearing the slightly higher chance of default. And so on.

The reason that ratings agencies and investors felt so safe with the triple-A tranches was that they believed there was no way hundreds of homeowners would all default on their loans at the same time. One person might lose his job, another might fall ill. But those are individual calamities that don’t affect the mortgage pool much as a whole: Everybody else is still making their payments on time.

But not all calamities are individual, and tranching still hadn’t solved all the problems of mortgage-pool risk. Some things, like falling house prices, affect a large number of people at once. If home values in your neighborhood decline and you lose some of your equity, there’s a good chance your neighbors will lose theirs as well. If, as a result, you default on your mortgage, there’s a higher probability they will default, too. That’s called correlation—the degree to which one variable moves in line with another—and measuring it is an important part of determining how risky mortgage bonds are.

Investors like risk, as long as they can price it. What they hate is uncertainty—not knowing how big the risk is. As a result, bond investors and mortgage lenders desperately want to be able to measure, model, and price correlation. Before quantitative models came along, the only time investors were comfortable putting their money in mortgage pools was when there was no risk whatsoever—in other words, when the bonds were guaranteed implicitly by the federal government through Fannie Mae or Freddie Mac.

Yet during the ’90s, as global markets expanded, there were trillions of new dollars waiting to be put to use lending to borrowers around the world—not just mortgage seekers but also corporations and car buyers and anybody running a balance on their credit card—if only investors could put a number on the correlations between them. The problem is excruciatingly hard, especially when you’re talking about thousands of moving parts. Whoever solved it would earn the eternal gratitude of Wall Street and quite possibly the attention of the Nobel committee as well.

To understand the mathematics of correlation better, consider something simple, like a kid in an elementary school: Let’s call her Alice. The probability that her parents will get divorced this year is about 5 percent, the risk of her getting head lice is about 5 percent, the chance of her seeing a teacher slip on a banana peel is about 5 percent, and the likelihood of her winning the class spelling bee is about 5 percent. If investors were trading securities based on the chances of those things happening only to Alice, they would all trade at more or less the same price.

But something important happens when we start looking at two kids rather than one—not just Alice but also the girl she sits next to, Britney. If Britney’s parents get divorced, what are the chances that Alice’s parents will get divorced, too? Still about 5 percent: The correlation there is close to zero. But if Britney gets head lice, the chance that Alice will get head lice is much higher, about 50 percent—which means the correlation is probably up in the 0.5 range. If Britney sees a teacher slip on a banana peel, what is the chance that Alice will see it, too? Very high indeed, since they sit next to each other: It could be as much as 95 percent, which means the correlation is close to 1. And if Britney wins the class spelling bee, the chance of Alice winning it is zero, which means the correlation is negative: -1.

If investors were trading securities based on the chances of these things happening to both Alice and Britney, the prices would be all over the place, because the correlations vary so much.

But it’s a very inexact science. Just measuring those initial 5 percent probabilities involves collecting lots of disparate data points and subjecting them to all manner of statistical and error analysis. Trying to assess the conditional probabilities—the chance that Alice will get head lice if Britney gets head lice—is an order of magnitude harder, since those data points are much rarer. As a result of the scarcity of historical data, the errors there are likely to be much greater.

In the world of mortgages, it’s harder still. What is the chance that any given home will decline in value? You can look at the past history of housing prices to give you an idea, but surely the nation’s macroeconomic situation also plays an important role. And what is the chance that if a home in one state falls in value, a similar home in another state will fall in value as well?
Here’s what killed your 401(k) David X. Li’s Gaussian copula function as first published in 2000. Investors exploited it as a quick—and fatally flawed—way to assess risk. A shorter version appears on this month’s cover of Wired.

Enter Li, a star mathematician who grew up in rural China in the 1960s. He excelled in school and eventually got a master’s degree in economics from Nankai University before leaving the country to get an MBA from Laval University in Quebec. That was followed by two more degrees: a master’s in actuarial science and a PhD in statistics, both from Ontario’s University of Waterloo. In 1997 he landed at Canadian Imperial Bank of Commerce, where his financial career began in earnest; he later moved to Barclays Capital and by 2004 was charged with rebuilding its quantitative analytics team.

Li’s trajectory is typical of the quant era, which began in the mid-1980s. Academia could never compete with the enormous salaries that banks and hedge funds were offering. At the same time, legions of math and physics PhDs were required to create, price, and arbitrage Wall Street’s ever more complex investment structures.

In 2000, while working at JPMorgan Chase, Li published a paper in The Journal of Fixed Income titled “On Default Correlation: A Copula Function Approach.” (In statistics, a copula is used to couple the behavior of two or more variables.) Using some relatively simple math—by Wall Street standards, anyway—Li came up with an ingenious way to model default correlation without even looking at historical default data. Instead, he used market data about the prices of instruments known as credit default swaps.

If you’re an investor, you have a choice these days: You can either lend directly to borrowers or sell investors credit default swaps, insurance against those same borrowers defaulting. Either way, you get a regular income stream—interest payments or insurance payments—and either way, if the borrower defaults, you lose a lot of money. The returns on both strategies are nearly identical, but because an unlimited number of credit default swaps can be sold against each borrower, the supply of swaps isn’t constrained the way the supply of bonds is, so the CDS market managed to grow extremely rapidly. Though credit default swaps were relatively new when Li’s paper came out, they soon became a bigger and more liquid market than the bonds on which they were based.

When the price of a credit default swap goes up, that indicates that default risk has risen. Li’s breakthrough was that instead of waiting to assemble enough historical data about actual defaults, which are rare in the real world, he used historical prices from the CDS market. It’s hard to build a historical model to predict Alice’s or Britney’s behavior, but anybody could see whether the price of credit default swaps on Britney tended to move in the same direction as that on Alice. If it did, then there was a strong correlation between Alice’s and Britney’s default risks, as priced by the market. Li wrote a model that used price rather than real-world default data as a shortcut (making an implicit assumption that financial markets in general, and CDS markets in particular, can price default risk correctly).

It was a brilliant simplification of an intractable problem. And Li didn’t just radically dumb down the difficulty of working out correlations; he decided not to even bother trying to map and calculate all the nearly infinite relationships between the various loans that made up a pool. What happens when the number of pool members increases or when you mix negative correlations with positive ones? Never mind all that, he said. The only thing that matters is the final correlation number—one clean, simple, all-sufficient figure that sums up everything.

The effect on the securitization market was electric. Armed with Li’s formula, Wall Street’s quants saw a new world of possibilities. And the first thing they did was start creating a huge number of brand-new triple-A securities. Using Li’s copula approach meant that ratings agencies like Moody’s—or anybody wanting to model the risk of a tranche—no longer needed to puzzle over the underlying securities. All they needed was that correlation number, and out would come a rating telling them how safe or risky the tranche was.

As a result, just about anything could be bundled and turned into a triple-A bond—corporate bonds, bank loans, mortgage-backed securities, whatever you liked. The consequent pools were often known as collateralized debt obligations, or CDOs. You could tranche that pool and create a triple-A security even if none of the components were themselves triple-A. You could even take lower-rated tranches of other CDOs, put them in a pool, and tranche them—an instrument known as a CDO-squared, which at that point was so far removed from any actual underlying bond or loan or mortgage that no one really had a clue what it included. But it didn’t matter. All you needed was Li’s copula function.

The CDS and CDO markets grew together, feeding on each other. At the end of 2001, there was $920 billion in credit default swaps outstanding. By the end of 2007, that number had skyrocketed to more than $62 trillion. The CDO market, which stood at $275 billion in 2000, grew to $4.7 trillion by 2006.

At the heart of it all was Li’s formula. When you talk to market participants, they use words like beautiful, simple, and, most commonly, tractable. It could be applied anywhere, for anything, and was quickly adopted not only by banks packaging new bonds but also by traders and hedge funds dreaming up complex trades between those bonds.

“The corporate CDO world relied almost exclusively on this copula-based correlation model,” says Darrell Duffie, a Stanford University finance professor who served on Moody’s Academic Advisory Research Committee. The Gaussian copula soon became such a universally accepted part of the world’s financial vocabulary that brokers started quoting prices for bond tranches based on their correlations. “Correlation trading has spread through the psyche of the financial markets like a highly infectious thought virus,” wrote derivatives guru Janet Tavakoli in 2006.

The damage was foreseeable and, in fact, foreseen. In 1998, before Li had even invented his copula function, Paul Wilmott wrote that “the correlations between financial quantities are notoriously unstable.” Wilmott, a quantitative-finance consultant and lecturer, argued that no theory should be built on such unpredictable parameters. And he wasn’t alone. During the boom years, everybody could reel off reasons why the Gaussian copula function wasn’t perfect. Li’s approach made no allowance for unpredictability: It assumed that correlation was a constant rather than something mercurial. Investment banks would regularly phone Stanford’s Duffie and ask him to come in and talk to them about exactly what Li’s copula was. Every time, he would warn them that it was not suitable for use in risk management or valuation.

In hindsight, ignoring those warnings looks foolhardy. But at the time, it was easy. Banks dismissed them, partly because the managers empowered to apply the brakes didn’t understand the arguments between various arms of the quant universe. Besides, they were making too much money to stop.

In finance, you can never reduce risk outright; you can only try to set up a market in which people who don’t want risk sell it to those who do. But in the CDO market, people used the Gaussian copula model to convince themselves they didn’t have any risk at all, when in fact they just didn’t have any risk 99 percent of the time. The other 1 percent of the time they blew up. Those explosions may have been rare, but they could destroy all previous gains, and then some.

Li’s copula function was used to price hundreds of billions of dollars’ worth of CDOs filled with mortgages. And because the copula function used CDS prices to calculate correlation, it was forced to confine itself to looking at the period of time when those credit default swaps had been in existence: less than a decade, a period when house prices soared. Naturally, default correlations were very low in those years. But when the mortgage boom ended abruptly and home values started falling across the country, correlations soared.

Bankers securitizing mortgages knew that their models were highly sensitive to house-price appreciation. If it ever turned negative on a national scale, a lot of bonds that had been rated triple-A, or risk-free, by copula-powered computer models would blow up. But no one was willing to stop the creation of CDOs, and the big investment banks happily kept on building more, drawing their correlation data from a period when real estate only went up.

“Everyone was pinning their hopes on house prices continuing to rise,” says Kai Gilkes of the credit research firm CreditSights, who spent 10 years working at ratings agencies. “When they stopped rising, pretty much everyone was caught on the wrong side, because the sensitivity to house prices was huge. And there was just no getting around it. Why didn’t rating agencies build in some cushion for this sensitivity to a house-price-depreciation scenario? Because if they had, they would have never rated a single mortgage-backed CDO.”

Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been—which implied that the risk was being moved elsewhere. Where had the risk gone?

They didn’t know, or didn’t ask. One reason was that the outputs came from “black box” computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula’s weaknesses, weren’t the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem.

“The relationship between two assets can never be captured by a single scalar quantity,” Wilmott says. For instance, consider the share prices of two sneaker manufacturers: When the market for sneakers is growing, both companies do well and the correlation between them is high. But when one company gets a lot of celebrity endorsements and starts stealing market share from the other, the stock prices diverge and the correlation between them turns negative. And when the nation morphs into a land of flip-flop-wearing couch potatoes, both companies decline and the correlation becomes positive again. It’s impossible to sum up such a history in one correlation number, but CDOs were invariably sold on the premise that correlation was more of a constant than a variable.

No one knew all of this better than David X. Li: “Very few people understand the essence of the model,” he told The Wall Street Journal way back in fall 2005.

“Li can’t be blamed,” says Gilkes of CreditSights. After all, he just invented the model. Instead, we should blame the bankers who misinterpreted it. And even then, the real danger was created not because any given trader adopted it but because every trader did. In financial markets, everybody doing the same thing is the classic recipe for a bubble and inevitable bust.

Nassim Nicholas Taleb, hedge fund manager and author of The Black Swan, is particularly harsh when it comes to the copula. “People got very excited about the Gaussian copula because of its mathematical elegance, but the thing never worked,” he says. “Co-association between securities is not measurable using correlation,” because past history can never prepare you for that one day when everything goes south. “Anything that relies on correlation is charlatanism.”

Li has been notably absent from the current debate over the causes of the crash. In fact, he is no longer even in the US. Last year, he moved to Beijing to head up the risk-management department of China International Capital Corporation. In a recent conversation, he seemed reluctant to discuss his paper and said he couldn’t talk without permission from the PR department. In response to a subsequent request, CICC’s press office sent an email saying that Li was no longer doing the kind of work he did in his previous job and, therefore, would not be speaking to the media.

In the world of finance, too many quants see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years’ worth of data and come up with probabilities for things that may happen only once every 10,000 years. Then people invest on the basis of those probabilities, without stopping to wonder whether the numbers make any sense at all.

As Li himself said of his own model: “The most dangerous part is when people believe everything coming out of it.”