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In 1999, the Committee on Women Faculty at MIT released a report claiming that there was significant gender bias at MIT. Women made up a minority of the Institute’s professorship, and on average were paid less and allotted less lab space; the report alleged this was due to a “subtle but pervasive bias” against women at the Institute. In response, the administration began a concerted effort to recruit more women and increase the pay of female professors. They succeeded, though at the cost of convincing many that women were being given an unfair advantage.

Today, the committee is gearing up for another push. A new report claims that while significant progress has been made, the subtle bias still exists, and more work (read: policy favoritism) is still needed to correct for the prejudices of MIT staff.

At face value, the activism is not unwarranted. Even if pay and lab space have equalized, women still make up less than 20 percent of the faculty of the Schools of Engineering and Science. Perhaps there is even a case to be made for bias at the admissions level: in the School of Engineering as a whole, male students outnumber females by more than 2-to-1, and in some areas, the skew is even greater; male mathematics graduate students outnumber females by almost 4-to-1, and male graduate physics students outnumber females by almost 4.5-to-1.

And yet, there is something deeply suspicious about the “subtle bias” that is supposedly responsible for keeping women down. It’s difficult to point to any hard evidence supporting the claim that prejudice is the root cause of gender gaps at MIT, and the soft evidence is mixed — for every woman with an anecdote about how the Institute has not given her what she deserves, there is a man with the opposite perception. Moreover, even if the soft evidence were not mixed, the plural of “anecdote” is still not “data” — empirically unsubstantiated claims of systemic bias are not a compelling basis upon which to make radical policy changes. And Veldman can draw all the trend lines he wishes between 1970 and today — the fact that Susan B. Anthony had to fight for suffrage does not mean that Susan Hockfield has been denied the opportunity to reach her full potential.

What then of the skewed gender ratios? Aren’t they evidence themselves of bias?

Perhaps. But that is just one hypothesis. And before we launch into a second round of increasing favoritism shown to women (while adamantly claiming that we are doing anything but), let’s entertain at least one other hypothesis, that the distribution of men and women in the upper echelons of science and engineering is not due to an invisible-and-unspoken conspiracy, but instead is due to something simpler: there are simply more smart men than there are smart women.

I hear the sounds of an angry mob already.

Let’s start by taking for granted that men and women, on average, are virtually identical in intelligence, even as it applies to areas like math or science. This is a pretty safe position — most of the literature puts men no worse than women, and a decent chunk even puts them ahead, claiming a point or two of higher IQ for men.

Let’s also take for granted one other point, that measured IQ scores are a very strong predictor of outcomes in life. The Bell Curve by Herrnstein and Murray is the magnum opus of this position, but a hundred other studies could serve in lieu of it.

Now let’s add in one final factor: suppose the variability of male intelligence is higher than that of women. This idea has quite a bit of support in the literature, and since the point I am trying to make is highly controversial (it cost Larry H. Summers ’75 his job), here is a laundry list of supporting studies: Deary, Thorpe, Wilson, Starr, and Whalley (2003) looked at a 1932 IQ test administered to nearly all 11-year-olds in Scotland and found no statistically significant difference in mean IQ, but a statistically significant difference in variability in male children. Hedges and Nowell (1995) performed a metastudy of national IQ tests from over a 32-year period and found similar results. Irwing and Lynn (2005) found that men outnumber women 2-to-1 at the 125 IQ level, and 5.5-to-1 at 155. Machin and Pekkarinen (2008) found higher variance in math and reading scores of male children in the Organisation for Economic Co-operation and Development (OECD). Deary, Irwing, Der, and Bates (2007) studied sibling pairs and their scores on the Armed Services Vocational Aptitude Battery (ASVAB) and found higher male variance. Ardin and Plomin (2006) tracked roughly 10,000 twins from age 2 to 10 and measured a higher variation in intelligence in male twins from 3 years old on.

There is, as with anything, an evolutionary argument as to why relatively higher variability of male intelligence (or male ability in general) would be advantageous. A woman who finds herself three standard deviations above her peers in intelligence can’t take much advantage of that from a procreative standpoint — no matter how many resources she is able to collect, her reproductive success is still limited by the human gestation period and a high risk of death in childbirth. But a man who finds himself well-ahead of his peers can go to town — Genghis Khan took such advantage of his alpha male dominance that there is a 0.5 percent chance you are related to him. It’s good to be the queen … but it’s even better to be the king, and this favors a relatively riskier evolutionary strategy when it comes to male offspring.

What is the consequence of equal average intelligence between the sexes, a strong linkage between intelligence and outcomes, and a higher variability in intelligence for men? For an institution like MIT, which presumably selects its professors from the top 1 percent of the population, the relative flatness of the male ability distribution means two things: firstly, men are going to outnumber women, with the degree of skew increasing as standards are raised. And secondly, within any elite group, the average male intelligence is likely to be higher than the average female intelligence.

In many ways, the IQ variability hypothesis does a better job of explaining the world than does the “subtle bias” position. Claiming that prejudice is responsible for our observed world is a delicate balancing act — one needs to show that U.S. politics, corporate boardrooms, and high technology centers are all vulnerable to invisible bigotry, but conversely that in those axes in which women outperform men, whether it is their lower rates of high school drop out, incarceration, or mental retardation, the bias is gone and the observed outcomes are somehow due to some unrelated factors. Conversely, higher male IQ variability explains nearly the whole range of outcomes; men significantly outnumber women in the tails of life’s outcomes because, quite simply, there are more men in the tails of the ability distribution.

Before we embark on a second crusade for gender equality at MIT, we should have a thorough debate about the causes of our gender gap, and this time, we shouldn’t just shout down those like Larry Summers who dare to conjecture about alternate theories. Perhaps the Committee on Women Faculty is right, and MIT is not a meritocracy. But what if they are wrong? The consequence for adopting increasing favoritism towards women will not just be the deterioration of our meritocracy, but the institutionalization of a real bias and an increasing sense that the women at MIT have not earned their place.