Progress Daily

According to Andrew Smithers‘ two favourite stock market valuation measures, the q ratio (which compares share prices with the replacement cost of net assets) and the cyclically adjusted price/earnings ratio (which averages profits over ten years), the US market is still overvalued. According to these measures, Wall Street fell to merely average, not low, valuations in the recent crash.

“The end is nigh (again),” The Economist

The cost of sequencing DNA has fallen from about $1 per base pair in the mid-1990s to a tenth of a cent today. Rob Carlson started tracking the price of DNA synthesis a decade ago. He found a steady decline, from over $10 per base pair to, lately, well under $1. This decline recalls Moore’s law, which, when promulgated in 1965, predicted the exponential rise of computing power.

A study by Amir Barnea, Henrik Cronqvist and Stephan Siegel (”Nature or Nurture: What Determines Investor Behavior?“), based on characteristics for approximately 40,000 identical and non-identical twins from the Swedish Twin Registry and associated investing data for the period 1998-2006, concludes that genetics explain up to 45% of individual variation in stock market participation, asset allocation and portfolio risk choices.

Genetic influence is robust to differences in age, education and net worth. Genetics explain more of the variation in individual investing behavior than does an extensive set of individual characteristics combined.

Family environment has an effect on the investing behavior of younger individuals, but this effect disappears as they acquire non-family experiences.

“The Genetics of Investing (Not the Algorithms),” CXO

Research by Paola Sapienza & Luigi Zingales (”Gender differences in financial risk aversion and career choices are affected by testosterone“) suggests that testosterone levels may explain why men dominate risky financial professions.

The researchers measured the amount of testosterone in the saliva of aspiring bankers (MBA students from the University of Chicago). They also estimated the students’ exposure to the hormone before they were born by measuring the ratios of their index fingers to their ring fingers (a long ring finger indicates high testosterone exposure) and by measuring how accurately they could determine human emotions by observing only people’s eyes, which also correlates with prenatal exposure to testosterone.

The students had to decide between a 50:50 chance of getting $200 or a gradually increasing sure payout, which ranged from $50 up to $120. The point at which a participant decided to switch from the gamble to the sure thing was reckoned a reasonable approximation of his appetite for risk.

Women and men with the same levels of testosterone generally switched at the same time, demonstrating similar risk preferences. Women who had more testosterone were more risk-loving than women with less, while the data for men at the lower end of the spectrum displayed a similar relationship. Curiously, the relationship between testosterone and risk taking was not as strong for men with moderate to high levels.

The correlation was strongest when the salivary measure of testosterone was used.

The researchers then followed the subjects’ progress after they graduated, to see what sort of careers they entered. Men were more likely than women to choose a risky job in finance but the difference was accounted for entirely by their levels of salivary testosterone.

“Risky business, The Economist

Africa is the fastest-growing and fastest-urbanising continent. Its population has grown from 110m in 1850 to 1 billion today. The average African woman born today is expected to have about 5 children, compared with just 1.7 in East Asia. In 1950 there were two Europeans for every African; by 2050, on present trends, there will be two Africans for every European.

The past 15 years have seen Africa’s fastest-ever period of economic growth, & it appears to be following the rest of the world through a demographic transition (falling birth rates as people get richer), which should increase the size (relative, as well as absolute) of their working-age population.

According to a study by the Harvard Initiative for Global Health (”Realizing the Demographic dividend: is Africa any different?” By David E. Bloom), the share of the working-age population will rise in 27 of 32 African countries between 2005 and 2015. On other continents this phenomenon has led to increased productivity.

Africa today produces less food per head than at any time since independence. Only 4% of Africa’s farmland is irrigated.

According to the UN Population Division, Africa’s overall population is 8% lower today than it would have been if its fertility rate had stayed at its 1970s level.

The use of modern contraceptives in sub-Saharan Africa is only 12% (though it has doubled since 1994). The rate in Asia and Latin America is over 40%, so contraceptive use is likely to rise sharply.

“The baby bonanza,” The Economist

In most species, improved circumstances increase reproductive effort, yet as economic development gets going, country after country has experienced the “demographic transition,” in which fertility (the number of children borne by a woman over her lifetime) drops from around 8 to one-and-a-half.

Unless trends change, by the middle of the century, populations in the most developed countries will have shrunk and the number of retired individuals supported by each person of working age will increase significantly.

A study by Mikko Myrskyla, Hans-Peter Kohler, & Francesco Billari (”Advances in development reverse fertility declines“) suggests that the trend may change: as development continues, the demographic transition goes into reverse.

The researchers looked at two years 1975 and 2005, and analyzed countries using two factors, total fertility rate (the number of children that would be born to a woman in a particular country over the course of her life if she experienced the age-specific fertility rates observed in that country during the calendar year in question) and human development index or “HDI” (a measure used by the UN that includes life expectancy, average income per person, and level of education), which has a maximum possible value of one.

In 1975, of the 107 countries analyzed, the best was Canada, with an HDI of 0.89. By 2005, two dozen countries (of 240 analzyed) had HDIs above nine. In 1975, a graph plotting fertility rate against the HDI fell as the HDI rose. By 2005, though, once the HDI rose above 0.9 or so, it turned up, producing a “J-shaped” curve (the mirror image of a letter J).

In many countries with very high levels of development (around 0.95) fertility rates are now approaching two children per woman.

The nadir of fertility appears to be 1.3 children per woman but this new data suggests the ultimate outcome of development may be zero population growth.

“The best of all possible worlds?,” The Economist

The percentage of foreign-born residents of the US rose from 5% in 1970 to 12% in 2006, during which time income inequality increased.

This wave of immigration exerted a mild downward pressure on the wages of native-born low-skilled workers but its primary impact on inequality was due to the increased number of less-skilled workers. According to the Robert Lerman (”US Wage-Inequality Trends and Recent Immigration“), excluding recent immigrants from the analysis would eliminate roughly 30% of the increase in adult male annual earnings inequality between 1979 and 1996. These immigrants experienced large wage gains as a result of relocating to the US, however. When Lerman included recent immigrants and their native-country wages in his calculations, he found equality had increased
rather than decreased.

In 1950, the labor force participation rate for women was 34%. By 1970 it had climbed to 43%, and by 2005 it had jumped to 59%. This exacerbated household income inequality: Now richer men are married to richer wives. Between 1979 and 1996, the proportion of working-age men with working wives rose by approximately 25% among those in the top fifth of the male earnings distribution, and their wives’ total earnings rose by over 100%. According to a 1999 estimate by Gary Burtless (”Effects of Growing Wage Disparities and Changing Family Composition on the US Income Distribution“), this phenomenon explained about 13% of the rise in income inequality since 1979.

“Nostalgianomics,” by Brink Lindsey

According to data compiled the US Department of Labor’s Bureau of Labor Statistics, total private-sector employment rose by nearly 20 million between 1993 and 2002. A breathtaking total of 330 million jobs were added, while 310 million jobs were lost.

Between 1983 and 2002, the number of managerial and specialized professional jobs rose from 24 million to 43 million — from 23% of total employment to 31%.

Between 1980 and 2003, American manufacturing output climbed a dizzying 93 percent. Yes, production fell during the recent recession, but it is now recovering: the industrial production index for manufacturing rose 2.2 percent in 2003.

Manufacturing’s share of gross domestic product declined from 27% in 1960 to 14% in 2002, during which time the percentage of workers employed in manufacturing fell from 28% to 12%. The primary cause of these trends is the superior productivity of American manufacturers. Output per hour in the overall nonfarm business sector rose 50% between 1980 and 2002; by contrast, manufacturing output per hour shot up 100%. In other words, goods are getting cheaper and cheaper relative to services. Since this faster productivity growth has not been matched by a corresponding increase in demand for manufactured goods, the result is that Americans are spending relatively less on manufactures.

Similarly, in 1870, 48% of total US employment was in farming. By 2002 the figure had fallen to 2%.

“10 Truths About Trade,” by Brink Lindsey

Income is very heritable.

A study by Samuel Bowles & Herbert Gintis (”The Inheritance of Inequality,” 2002) found that identical twins’ incomes have a correlation of .56, versus .36 for fraternal twins. Using standard formulae, this implies that genetic factors explain 40% of the variance of income, family environment 16%, and non-shared environment 44%.

Using different data, David Cesarini gets income correlations of .54 for identicals versus .27 for fraternals, implying that genes explain 56% of the variance, shared environment -1%, and non-shared environment 45%.

“Is Greed in the Genes?,” by Bryan Caplan

Nicos Nicolaou’s research team used quantitative genetics techniques to compare the entrepreneurial activity of 870 pairs of monozygotic (MZ) and 857 pairs of same-sex dizygotic (DZ) twins from the UK, and found relatively high heritability for entrepreneurship, with little effect of family environment and upbringing.

Because MZ twins share all of their genetic composition and DZ twins share half of their genetic material on average, differences in the concordance between entrepreneurial activity of MZ and DZ twins can be attributed to genetic factors.

Studies of MZ and DZ twins raised together and apart have shown that the MZ twins raised apart are consistently more similar than DZ twins raised together.

Many parents are misinformed or make erroneous evaluations about the zygosity of their twins, leading some parents to raise their DZ twins as MZ twins and other parents to treat their MZ twins as DZ twins. Studies have shown that in cases where parents made erroneous conclusions about the zygosity of their twins, it was actual, rather than perceived zygosity that predicted similarity between twins.

The researchers estimate that 48% of the variance in the propensity to become self-employed is explained by genetic factors. None of the variance in self-employment can be attributed to shared environmental factors, while 52% can be attributed to non-shared environmental factors plus measurement error.

The researchers re-ran the analysis adjusting the model for potential confounders - income, education, marriage, age, race, and immigrant status - that have been shown to be associated with entrepreneurship. The heritability estimates for self-employment, drop only to 41% when these variables are included.

To identify the mechanism through which genetic factors operate, the researchers examined the respondents attitude toward entrepreneurship through an item that asked them to indicate, on a scale from one (a very good career) to five (a very bad career), their view of entrepreneurship as a career. The best fitting model to explain the variance in attitudes included shared and unshared environmental factors. None of the variance was explained by genetic factors, indicating that the mechanism through which these factors affect the tendency to become an entrepreneur is not through attitudes toward the vocation.

“Is the Tendency to Engage in Self-employment Genetic?” (.pdf file here), by Nicos Nicolaou, Scott Shane, Tim D. Spector, and others