Progress Daily

James W. Vaupel says that how tall your parents are compared to the average height explains 80 to 90% of how tall you are compared to the average person. But “only 3% of how long you live compared to the average person can be explained by how long your parents lived. … Even twins, identical twins, die at different times.” On average more than 10 years apart.

Dr. Kaare Christensen studied twins. The idea was to compare identical twins, who share all their genes, with fraternal twins, who share some of them. He took advantage of detailed registries that included all the twins in Denmark, Finland and Switzerland born from 1870 to 1910. That study followed the twins until 2004 to 2005, when nearly all had died.

They restricted themselves to twins of the same sex, which obviated the problem that women tend to live longer than men. That left them with 10,251 pairs of same-sex twins, identical or fraternal.

The genetic influence was much smaller than most scientists had assumed. The vast majorities of identical twins die years apart.

There was almost no genetic influence on age of death before 60. The studies of twins found almost no genetic influence on age of death, except among people who live to be very old.

A woman whose sister lived to be 100 has a 4% chance of living that long. That is better than the 1% chance for women in general, but still not very great because the absolute numbers are so small.

A man whose sister lived to be 100 has just a 0.4% chance of living that long. Men in general have a 0.1% chance of reaching 100.

Dr. Paul Lichtenstein analyzed cancer rates in 44,788 pairs of Nordic twins, and found that only a few cancers — breast, prostate and colorectal — had a noticeable genetic component. If one identical twin got one of those cancers, the chance that the other twin would get it was generally less than 15%, about five times the risk for the average person but not a very big risk over all.

Parkinson’s has no detectable heritable component, studies repeatedly find. Heart disease appears to be indiscriminate, striking almost everyone eventually.

Chronic diseases strike almost at random among the elderly.

Matt McGue contrasts life spans with personality, which, he says, is about 50% heritable, or attention-deficit hyperactivity disorder, which is 70 to 80% heritable, or body weight, which is 70% heritable.

Researchers have isolated a gene for a cholesterol-carrying protein that affects risk for heart disease as well as Alzheimer’s disease. Those who have that gene have double the chances of living to 100. (Only about 2% of people born in 1910 could expect to reach 100.)

Dr. Christensen found that birth order made no difference in health or longevity.

“Live Long? Die Young? Answer Isn’t Just in Genes,” by Gina Kolata

The most common behavioral traits that affect investment decisions adversely include:

Overconfidence Individuals tend to assign overly narrow “confidence intervals” (estimated ranges of values) to financial measures such as stock indices. In one recent survey, when individuals reported confidence intervals of 98% (a high indicator of certainty) they were right only 60% of the time.

In another survey, over 90% of people judged their interpersonal skills, driving skills and sense of humour to be above average. People are also poor at estimating probabilities: research shows that events they consider certain occur only 80% of the time.

Frame dependence Several experiments have shown that certain people find it tremendously difficult to move out of their existing frame of thought.

“Hedging your bets to protect against risk” by Didier Cossin and Dinos Constantinou

Social scientists have confirmed time and again that people generally overestimate their abilities and knowledge. More than 80% of drivers think they’re among the safest 30% of those driving. When asked at conferences to write down how much money they will have at retirement versus the amount the average person in the room will have, money managers and business executives consistently judge that they’ll end up with about twice the average.

Brad Barber and Terrance Odean have found that investors generally overestimate the precision of their knowledge about a security’s value, and the probability that their assessment is more accurate than that of others. The result is more active trading but not better performance: “those who trade the most realise by far the worst performance.”

Lin Peng and Wei Xiong have found that overconfident, time-pressed investors put too much weight on market- or sector-level information and not enough on company-specific data. The authors argue that this sloppiness was a key contributor to the internet stock bubble.

“The undermining effects of overconfidence” by Whitney Tilson

Stephen Slivinski has just published his first book, Buck Wild: How Republicans Broke the Bank and Became the Party of Big Government.

Slivinski tabulates that under the average united government — i.e., one-party control of the presidency, House, and Senate — the growth of real per-capita government spending is 3.4%. Under divided government, this growth rate is only 1.5% — less than half as fast.

United Democratic government and united Republican government look virtually the same: 3.3% government growth under Democrats vs. 3.6% under Republicans.

The most libertarian configuration of all turns out to be Democratic president + Republican Congress. Average rate of government growth: .4%. (Slivinski doesn’t mention the other plausible libertarian benefit of this combination — less interventionist foreign policy.)

“The Libertarian Case for Divided Government” by Bryan Caplan

Hernando de Soto, in The Mystery of Capital (2000), argues that the poor have more assets — shacks, stalls, plots — than you might think. But because they lack title to these assets, they cannot pass them on, divide them up, or offer them as collateral for a loan to expand their makeshift businesses and fully express their entrepreneurial energies.

Sebastian Galiani and Ernesto Schargrodsky have found a case that they believe tests de Soto’s theory (”Property Rights for the Poor.” Pdf file here). In 1981 about 1,800 families occupied a stretch of wasteland in the municipality of Quilmes on the outskirts of Buenos Aires. The squatters outlasted Argentina’s military junta, which tried several times to evict them, and in 1984, after the return of democracy, the provincial government passed a law expropriating the land from its rightful owners so that the squatters could enjoy formal ownership of it.

But the victory of the Buenos Aires squatters was only partial. Eight of the former landowners accepted the government’s compensation in 1986, one did not relent until 1998, and the remaining four are still contesting it in Argentina’s Dickensian courts. As a result, several hundred families now own their land, but their neighbours still squat uneasily on theirs. This is unfortunate for the squatters, but a rare opportunity for economists to test the power of property rights. The families lucky enough to win title can be compared with a ready-made control group: the otherwise identical families that did not.

The landowning families invested more in their homes, which had noticeably better walls and roofs, and they were also more likely to lay concrete pavements. But the titled households enjoyed no better access to bank loans, credit cards or bank accounts, and only 4% of them managed to acquire a mortgage. Argentine banks tend to lend only to workers with high wages and a stable job. Titled or not, the former squatters still fell well below the official poverty line. The cost of making and enforcing a loan contract might exceed the modest sums they were able to borrow.

The credit market has also been slow to respond to a much bigger urban-titling movement in Peru, carried out by the government with the help of Mr de Soto’s think-tank, the Institute for Liberty and Democracy (ILD). The campaign had awarded over 1.5m titles by July 2006. But it did not do them all at once. Erica Field and Maximo Torero have compared 536 households served before March 2000 with another 1,180 households that had yet to be reached by that date, on the assumption that little else distinguished the two groups (”Do Property Titles Increase Credit Access Among the Urban Poor?” Pdf file here).

Households with title were more likely to secure a loan from the government-backed Materials Bank, which buys bricks, mortar and other materials for building and improving homes. They also paid lower interest rates on loans from private sources, including commercial banks and microlenders like Mibanco. But their odds of getting a private loan in the first place did not improve. More than a third could not get a loan or would not take one, for fear of losing their property.

Paradoxically, this fear may not be sharp enough. There are two sides to collateral: enforcing the bank’s right to repossess an asset is as important as recognising the owner’s right to possess it. But titling programmes, the authors write, “unavoidably signal to lenders that a government prioritises housing for the poor, and hence is more likely to side with borrowers in enforcing credit contracts.”

The ILD has always pushed for broader changes in the legal system so that it can handle the kind of collateral the poor provide, at a cost that makes it worthwhile to do so. Credit also appears to have grown quite quickly in Peru after 2000, the year of the survey used by Ms Field and Mr Torero. The World Bank’s own studies show that mortgages worth $136m were approved in 2003, compared with $66m three years earlier. Likewise, formal credit increased from $249m to $367m in the same period, although the bank notes the difficulty in showing why this happened.

“The mystery of capital deepens,” The Economist

Economists shy away from nonlinear forecasting because it is risky. A lot of the telecom bubble can be described as a forecast for Internet growth of 10x per year when in fact it was closer to 3x per year. If you build capacity for three years ahead using the 10x model, you will multiply capacity by 1000x, when the 3x reality means that demand only increases by 27x.

Nonlinear forecasts can be wildly wrong in the quantity dimension without being far off in the time dimension. Even at 3x growth, demand will catch up to capacity within a few years. The telecom industry’s mistake was not so much the amount of capacity that they built as the high-leverage way in which it was financed.

In The Age of Spiritual Machines, Ray Kurzwei focused on the economic implications of Moore’s Law. Imagine that we add up the total intelligence on earth by summing up the amount provided by human beings and the amount provided by computers. Today, the proportion supplied by computers might be much less than 1%. Yet Kurzweil would be confident that the proportion supplied by computers will be 99% by the end of the century. That is because the capability of a typical computer is doubling about every two years, while the capability of the typical human grows more slowly.

Kurzweil might project that a computer will have the same mental capacity as a human in the year 2030. If the computer only has 1/8 the capacity of a human at that date, you might think that he is spectacularly wrong. However, if computer intelligence doubles every two years, then the computer will catch up six years later — and once it catches up, it will zoom past.

Longevity has been increasing for the past hundred years, from about 45 years at the beginning of the 20th century to close to 77 years at the beginning of the 21st. However, if we reach the point where longevity increases at a rate greater than one year per year, then from that point on people will live forever.

Suppose that when you reach the age of 80 the “war on aging” has progressed to the point where longevity is 100. At that point, scientists have another twenty years to come up with new ways to extend life. This might take you to the age of 130, which gives scientists another thirty years — and you live forever.

As with all new technologies, the best anti-aging techniques will likely go through a period during which they are unproven and expensive. That there could be a relatively short window — five to ten years — such that people who die before the start of that window just miss out on immortality, people who naturally live just after the end of the window will all be immortal. During this “window,” longevity will go from, say, 85, to infinity. That is nonlinear!

“Nonlinear Thinking” by Arnold Kling

Without realizing it, people will perceive things according to how they want to see them, a new study suggests.

In five separate tests conducted by David A. Dunning and a graduate student, Emily Balcetis, 412 volunteers from Cornell were presented with an ambiguous picture that could be interpreted as two distinct figures — either a horse’s head or the body of a seal, for example. They were told they would be assigned to a taste test of either fresh-squeezed orange juice or a gelatinous, clumpy and rather unappealing veggie smoothie, depending on whether they saw a farm animal or sea creature.

More often than not the participants chose the figure that would lead them to the juice.

The trick to making the study meaningful was making sure the test subjects didn’t know what was going on, Dunning said, noting that the generally high IQ of Cornell students made cheating a real possibility.

“The figures we used were chosen so we knew the people weren’t just lying or tricking us,” Dunning told LiveScience. “We also tracked automatic, unconscious eye movements which were out of their control.”

Not only did participants routinely see the figure that produced favorable results, their eye motions indicated that they were never aware of the alternate option being available.

“Determining whether a person walking towards you is smiling or smirking, how close the finish line seems in a race or how loud a partner — a wife, husband, lover -— is yelling during an argument,” Dunning gave as examples that could arise in life.

“Desire Controls What We See, Study Finds,” by Heather Whipps

A study published in the Royal Society Journal Biology Letters suggests that brain size evolution may be driven by predator-prey relationships.

While there has been a consistent increase in relative brain size, and therefore intelligence, over most mammal groups throughout evolutionary time, predator-prey relationships have led to an intelligence divide, said lead researcher Susanne Shultz.

Shultz and colleague R.I.M. Dunbar studied data on animals from five forest communities in two continents. The animals came from Taï National Park in West Africa, the Ituri forest in the Republic of Congo, Mahale National Park in Tanzania, Kibale National Park in Uganda and Manu National Park in Peru.

The predators, which included leopards, chimpanzees, jaguars, pumas and other animals, were found to most often go for tinier brained prey, such as the small antelope, mongooses and the red river hog.

When the scientists ran all of the data through several statistical tests, they determined relative brain size was the most important predictor of biases in predator diets. Body size and prey group size were two other contributing factors, but nothing trumped brain size in predicting what a predator would choose for its dinner.

Shultz even believes humans may have been prey for so long that it added to our brainpower.

“As far as human evolution, coming out of the trees and onto the savannah entails high predation risk,” she explained. “It is possible that both living in large groups and living in risky environments both contributed to the increase in brain size seen in our ancestors.”

There are drawbacks to having a big brain.

“Large brains are extremely costly to both develop and maintain,” Shultz said. “They have high caloric demands.”

Larger-brained animals also tend to have slower development trajectories than smaller-brained critters, due to low reproductive rates, higher energy requirements and other factors.

Pea-Brains Make Best Prey, Study Finds,” by Jennifer Viegas

On back page of The Economist there is a column showing the GDP growth rates of 27 developing countries. In a typical copy from the late 1990s as many as one-third to one-half of these could have minus signs in front of them. Today, every one of these developing countries’ growth rates is positive. The slowest growth rate, in Brazil, is still a respectable 3.4 percent.

In the 1990s, the GDP of developing countries grew at an average of 3.6%. In 2003, developing countries’ economies grew by 5.6%. In 2004, they achieved a sizzling 7.1%, then settled back to a still-impressive 6.4% in 2005. Rich countries are growing, too — the OECD economies grew at 3.2% in 2004 and 2.7% in 2005 — but at more a pedestrian pace.

China’s economy grew at 11.3% in the second quarter of 2006. India’s busy economy is growing at about 8%. US GDP grew at a 5.6% annual rate in the first quarter.

When poor countries grow faster than rich ones, the result is convergence, a narrowing of worldwide economic inequality. Economists have long predicted convergence, which should be caused by technology transfer, international trade and capital flows, and (to a small extent) foreign aid and migration. But convergence has never seemed to materialize.

In the 1990s, strong growth in China and India caused a slight narrowing in worldwide individual inequality, despite the ongoing crisis in Africa, economic collapse in the former Soviet bloc, and stagnation in Latin America and the Middle East. Today economic growth has spread to all major regions of the world. The biggest turnaround is in the former Soviet bloc, where average annual economic growth from the fall of the USSR to 1998 was negative 6.9%, but, from 1999 onward, has bounced back to positive 6.9%.

Except Western Europe is beating its 1990s average. Latin America averaging 3.4% GDP growth in the 1990s. It grew at 6% in 2004, 4.4% in 2005, and is expected to grow 5% in 2006. The Middle East and North Africa region grew at 3.8% in the 1990s. It grew at 4.7% in 2004 and 4.8% in 2005. Sub-Saharan Africa has improved from 2.9% average GDP growth in the 1990s to 5.6% in 2004 and 5.3% in 2005. East Asia (excluding Japan) is beating its 1990s average of 8.5%; in 2004, it grew at 9.1%, in 2005, 8.8%.

High commodity prices help to spread economic growth from high-performing sweatshop and ex-sweatshop countries to struggling commodity exporters. China is now the world’s biggest importer of steel, copper, stone, soybeans, and the second largest importer of oil.

According to the World Bank’s 2005 Little Data Book, in every developing country in the world, the birth rate (births per woman) in 2003 is equal to or lower than in 1990. The only countries where the birth rates rose were rich countries: Denmark, France, and the Netherlands.

When the birthrate falls, the first effect is that there are fewer kids to support and educate. At the same time, there are still relatively few old people. This situation is called a low dependency ratio, meaning that people who don’t work (children and seniors) are a small share of the population. A low dependency ratio means low public spending. At the same time, workers are saving for their old age, so savings rates are high. Entrepreneurs turn savings into investment, and capital formation accelerates. As workers become scarcer, and capital more abundant, wages and living standards rise. Eventually, the dependency ratio rises again, as a baby-boom of workers turns into a baby-boom of retirees, and growth slows down again.

This process is called the demographic transition. It was part of the reason for the explosive growth performance of East Asia over the past generation. Now, with birthrates falling everywhere, a worldwide demographic transition is underway. This also explains the “savings glut” which Ben Bernanke (now Fed Chairman) posited last year as an explanation for why dollar interest rates remain low, in spite of America’s huge budget and trade deficits.

“Don’t Look Now, But the World Economy Is Booming,” by Nathan Smith

The Neolithic Revolution began in the Middle East about 10,000 years ago. In the next few millennia humankind invented the wheel, the metal tool, and agriculture. The Sumerians eventually put these inventions together, added writing, and became the world’s first civilization. Afterward Sumeria’s heirs in Europe and Asia frantically copied one another’s discoveries.

American Neolithic development occurred later than that of the Middle East, possibly because the Indians needed more time to build up the requisite population density. Without beasts of burden they could not capitalize on the wheel (for individual workers on uneven terrain skids are nearly as effective as carts for hauling), and they never developed steel. But in agriculture they handily outstripped the children of Sumeria. Every tomato in Italy, every potato in Ireland, and every hot pepper in Thailand came from this hemisphere. Worldwide, more than half the crops grown today were initially developed in the Americas.

Maize, as corn is called in the rest of the world, was a triumph with global implications. Indians developed an extraordinary number of maize varieties for different growing conditions, which meant that the crop could and did spread throughout the planet. Central and Southern Europeans became particularly dependent on it; maize was the staple of Serbia, Romania, and Moldavia by the nineteenth century. Indian crops dramatically reduced hunger and may have caused an Old World population boom.

In the Americas, Indian agriculture long sustained some of the world’s largest cities. The Aztec capital of Tenochtitlán dazzled Hernán Cortés in 1519; it was bigger than Paris, Europe’s greatest metropolis.

France experienced seven nationwide famines in the fifteenth century and thirteen in the sixteenth. During epidemics in London the dead were heaped onto carts and trundled through the streets. The infant death rate in London orphanages, according to one contemporary source, was 88%.

Indians had ailments of their own, notably parasites, tuberculosis, and anemia. The daily grind was wearing; life-spans in America were only as long as or a little longer than those in Europe, if the evidence of indigenous graveyards is to be believed.

Indians created small plots, as Europeans did (about 1.5 million acres of terraces still exist in the Peruvian Andes), but they also reshaped entire landscapes to suit their purposes. A principal tool was fire, used to keep down underbrush and create the open, grassy conditions favorable for game. Rather than domesticating animals for meat, Indians retooled whole ecosystems to grow bumper crops of elk, deer, and bison. The first white settlers in Ohio found forests as open as English parks — they could drive carriages through the woods. Along the Hudson River the annual fall burning lit up the banks for miles on end; so flashy was the show that the Dutch in New Amsterdam boated upriver to goggle at the blaze. In North America, Indian torches had their biggest impact on the Midwestern prairie, much or most of which was created and maintained by fire. Millennia of burning shaped the plains into vast buffalo farms. When Indian societies disintegrated, forest invaded savannah in Wisconsin, Illinois, Kansas, Nebraska, and the Texas Hill Country.

1491, by by Charles C. Mann