Much has been made about the lack of female engineers and computer scientists in Silicon Valley. Some have chalked it up to basic disinterest. Others have relied on the old and easy stereotype that men are, simply, better at math.
A new study published in the Proceedings from the National Academy of Sciences, however, suggests that the problem is neither lack of interest nor lack of skill. It is, plain and simple, a matter of gender discrimination--that hiring managers repeatedly favor men for math and science jobs over their often equally or more talented female counterparts.
The researchers, all business school professors out of Columbia University, Northwestern University, and the University of Chicago, set up a mock hiring experience in which managers were asked to select which job "applicants" they thought would perform best at a math task. Based solely on the applicants' appearances, the hiring managers, who were both male and female, were twice as likely to hire a man.
In a second experiment, the applicants were permitted to talk about their skills. As might be expected, men overstated their skills, while women understated them. Again, hiring managers chose men twice as often. The results were no more promising when the managers had hard data to work with, either. Even when the managers were given evidence that showed each applicant's skill level, they were still 1.5 times more likely to choose men. Perhaps more shocking: in cases where the managers hired a lower performing candidate, two-thirds of the time, the candidate was male.
Finally, the managers were given a test to determine what associations they make with men and with women. According to the researchers, the same people who associated women with poor math skills did not associate men with boasting. "So they're picking up a negative stereotype of women, but not a negative stereotype of men," University of Chicago's Luigi Zingales told The New York Times.
Zingales asserts that the fact that women are not equally celebrated for their equal performance is one reason why women may drop out of math and science fields.
So, what to do about your own biases? The first step is to recognize them. Look past the candidate's bravado and at his (or her!) actual talent.