Why aren't there more women in science and engineering? Controversial new research suggests: They just aren't interested.
WHEN IT COMES to the huge and persistent gender gap in science and technology jobs, the finger of blame has pointed in many directions: sexist companies, boy-friendly science and math classes, differences in aptitude. ...
Now two new studies by economists and social scientists have reached a perhaps startling conclusion: An important part of the explanation for the gender gap, they are finding, are the preferences of women themselves. When it comes to certain math- and science-related jobs, substantial numbers of women - highly qualified for the work - stay out of those careers because they would simply rather do something else.
One study of information-technology workers found that women's own preferences are the single most important factor in that field's dramatic gender imbalance. Another study followed 5,000 mathematically gifted students and found that qualified women are significantly more likely to avoid physics and the other "hard" sciences in favor of work in medicine and biosciences.
It's important to note that these findings involve averages and do not apply to all women or men; indeed, there is wide variety within each gender.
Wouldn't it be great if supposedly educated people knew that goes without saying?
Perhaps spending $19 million was the point of spending $19 million? Economists are supposed to think about self-interest and incentives, but they tend to act as if a disinterested pursuit of truth is all that matters in academic politics.
The researchers are not suggesting that sexism and cultural pressures on women don't play a role, and they don't yet know why women choose the way they do. One forthcoming paper in the Harvard Business Review, for instance, found that women often leave technical jobs because of rampant sexism in the workplace.
But if these researchers are right, then a certain amount of gender gap might be a natural artifact of a free society, where men and women finally can forge their own vocational paths. And understanding how individual choices shape the gender balance of some of the most important, financially rewarding careers will be critical in fashioning effective solutions for a problem that has vexed people for more than a generation.
A few years ago, Joshua Rosenbloom, an economist at the University of Kansas, became intrigued by a new campaign by the National Science Foundation to root out what it saw as pervasive gender discrimination in science and engineering. The agency was spending $19 million a year to encourage mentoring programs, gender-bias workshops, and cooperative work environments.
Rosenbloom had no quarrel with the goal of gender equity. But as he saw it, the federal government was spending all that money without any idea what would work, because there was no solid data on what caused the disparity between men and women in scientific fields.
To help answer the question, Rosenbloom surveyed hundreds of professionals in information technology, a career in which women are significantly underrepresented. He also surveyed hundreds in comparable careers more evenly balanced between men and women. ...
Personal preference, Rosenbloom and his group concluded, was the single largest determinative factor in whether women went into IT. They calculated that preference accounted for about two-thirds of the gender imbalance in the field. The study was published in November in the Journal of Economic Psychology.
It may seem like a cliche - or rank sexism - to say women like to work with people, and men prefer to work with things. Rosenbloom acknowledges that, but says that whether due to socialization or "more basic differences," the genders on average demonstrate different vocational interests.
"It sounds like stereotypes," he said in an interview, "but these stereotypes have a germ of truth."
What exactly does the word "stereotype" mean these days among the educated? Something that we all know is true on average but only bad people mention? But do people really know that they are lying? I don't think so.By the way, what I'm increasingly fascinated by how unrebellious, how credulously trusting of authority the post-1960s generations have turned out to be. They go to school, get told obvious lies, then they go out and repeat them over and over and over. The idea that you can't trust anybody over 30 is totally foreign to the youth of recent decades. Perhaps the reason for this stability is because the schools are run by 1960s People, and the 1960s People discovered exactly what callow youths want to hear.
In the language of the social sciences, Rosenbloom found that the women were "self-selecting" out of IT careers. The concept of self-selection has long interested social scientists as an explanation for how groups sort themselves over time. Since human beings are heterogeneous, self-selection predicts that when offered a menu of options and freedom of choice, people will make diverse choices and sort themselves out in nonrandom ways. In other words, even given the same opportunities, not everybody will do the same thing - and there are measurable reasons that they will act differently from one another.It's striking how the concept of "self-selection" has to be spelled out as if it's some conceptual breakthrough in String Theory, rather than the most obvious thing in the whole entire world. This shows how lacking in basic tools our intellectual discourse is these days. My best guess is that the stupidity of modern intellectual life largely has its roots in group differences in IQ, crime rates, and the like.
The concept of self-selection sets off alarms for many feminists.
Indeed. Rational thought in general terrifies feminists ... and rightly so.
But self-selection has also emerged as the chief explanation in other recent studies of gender imbalance, including a long-term survey done by two Vanderbilt researchers, Camilla Persson Benbow and David Lubinski.Wow. Who knew?
Starting more than 30 years ago, the Study of Mathematically Precocious Youth began following nearly 2,000 mathematically gifted adolescents, boys and girls, tracking their education and careers in ensuing decades. (It has since been expanded to 5,000 participants, many from more recent graduating classes.) Both men and women in the study achieved advanced credentials in about the same numbers. But when it came to their career paths, there was a striking divergence.
Math-precocious men were much more likely to go into engineering or physical sciences than women. Math-precocious women, by contrast, were more likely to go into careers in medicine, biological sciences, humanities, and social sciences. Both sexes scored high on the math SAT, and the data showed the women weren't discouraged from certain career paths.
The survey data showed a notable disparity on one point: That men, relative to women, prefer to work with inorganic materials; women, in general, prefer to work with organic or living things. This gender disparity was apparent very early in life, and it continued to hold steady over the course of the participants' careers.
Here's something more interesting:
Benbow and Lubinski also found something else intriguing: Women who are mathematically gifted are more likely than men to have strong verbal abilities as well; men who excel in math, by contrast, don't do nearly as well in verbal skills. As a result, the career choices for math-precocious women are wider than for their male counterparts. They can become scientists, but can succeed just as well as lawyers or teachers. With this range of choice, their data show, highly qualified women may opt out of certain technical or scientific jobs simply because they can.So, if you are, say, Margaret Thatcher, and have an Oxford degree in Chemistry, well, that's nice but you have other options in life.
Why this difference? There's a big surplus of males in Benbow and Lubinski's sample of the mathematically gifted, so this suggests that women who are good at math tend to be good at math because they have a high overall g factor. In contrast, males tend to have more specialized mental skills useful in math, such as 3-d imagination skills, which doesn't correlate as highly with the g factor as most other cognitive traits.