Women did not evolve against risk-taking and tech startups.

This is cross-posted at Geek Feminism.

There is a common idea that women are underrepresented in tech startups because we are “nurturing and not risk-taking enough by nature”, an idea often proposed and upvoted in Hacker News discussions. Roy F. Baumeister, Professor of Psychology, also argues something similar in his defense of Lawrence Summers’ hypothesis that fewer women than men have high innate ability in science. Professor Baumeister argues that men evolved to take risks, and women evolved to play it safe, because we are allegedly descendants of risk-taking men and risk-averse women.

However, there are a few problems with this explanation of why women are underrepresented among tech entrepreneurs. One problem is that top venture capitalist John Doerr consciously and deliberately invests in tech startups run by white men over women and racial minorities, and even encourages other VCs to follow his lead. Even more, it is understood that this is “the way the venture-capital industry operates”. While other industries call this “stereotyping” or “profiling”, VCs call it “pattern recognition”. In other words, there is systemic discrimination in the tech industry based on gender, as well as race and age.

Another problem with the hypothesis of female risk-aversion is that outside of the tech industry, women have been launching new businesses at twice the rate of men for three decades:

The phenomenal growth of women-owned businesses has made headlines for three decades—women consistently have been launching new enterprises at twice the rate of men, and their growth rates of employment and revenue have outpaced the economy.

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Asians are not all rich.

There is a ridiculous post at 8asians titled Asian Men Have The Highest Salary that was linked from Racialicious for some reason. The author, Tim, writes:

If you were looking for another reason to date an Asian male, the U.S. Bureau of Labor Statistics just came up with one. In their latest report on earnings in the 2nd quarter of 2010, Asian men topped the charts again, making $901 in median weekly earnings. Asian women were second at $854, followed by white men at $838.

Let us ignore the often sexist stereotype that women are attracted to rich men for now and look at the issue of race and income. The data I have comes from Ontario, Canada, and the great thing about Canadian race statistics is that Asians are not all lumped together.

Average Employment Income By Racialized Group, Ontario, 2005

Men Women Total
Japanese 73079 40572 56170
Non-racialized 50255 31682 41335
Chinese 41283 30129 35717
Multiple Visible Minority 39940 29156 34613
South Asian 37198 24897 31711
Arab 36894 24258 31963
Southeast Asian 36506 24549 30619
Visibly Minority not included elsewhere 35943 26583 31201
Filipino 35323 28798 31472
Black 33497 27485 30337
Latin American 33218 22518 28145
Korean 32828 22326 27757
West Asian 30212 21660 26502

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Gender difference in math ability variability driven by social inequality, not biology – study

Gender gap in maths driven by social factors, not biological differences (Not Exactly Rocket Science):

Since 1894, some scientists have suggested that men have a greater variability in intellectual ability than women, a simple statistical quirk that would result in more male prodigies. This was the controversial hypothesis that Lawrence Summers mentioned in his now-infamous speech at the National Bureau of Economic Research Conference in 2005:

[…]

To test that, Hyde looked at data from maths tests in Minnesota and compared the numbers of boys and girls who scored in the top 5% of their year. The ratio was 1.45, meaning that for every two girls in this elite group, there were around three boys. In the top 1%, the ratio was 2.06, meaning two boys for every girl. That seems to vindicate the Variability Hypothesis, but those figures only applied to white American children. In other ethnic groups or, indeed, in other countries, the picture was very different.

For Asian-Americans the ratio was actually 0.91, meaning more girls than boys in the top 1%. International studies have found similar trends. One analysis of tests from the Program for International Student Assessment (PISA) showed that 15-year-old girls matched or outnumbered their male peers in the top tiers within Iceland, Thailand and the UK. Two studies found that 15-year-old boys and girls were equally varied in their mathematical skills in most of the countries taking part in PISA and the Trends in International Mathematics and Science Study (TIMSS). In some, like the Netherlands, girls actually turned out to have the wider range of ability.

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