There have been a number of blog posts in the last week or so about a study that looked at implicit (rather than explicit) discrimination in hiring practices. Both Jenny Rohm and Athene Donald have had interesting things to say.
The abstract says
Despite efforts to recruit and retain more women, a stark gender disparity persists within academic science. Abundant research has demonstrated gender bias in many demographic groups, but has
yet to experimentally investigate whether science faculty exhibit a bias against female students that could contribute to the gender disparity in academic science. In a randomized double-blind study (n = 127), science faculty from research-intensive universities rated the application materials of a student—who was randomly assigned either a male or female name—for a laboratory manager position. Faculty participants rated the male applicant as signiﬁcantly more competent and hireable than the (identical) female applicant. These participants also selected a higher starting salary and offered more career mentoring to the male applicant. The gender of the faculty participants did not affect responses, such that female and male faculty were equally likely to exhibit bias against the female student. Mediation analyses indicated that the female student was less likely to be hired because she was viewed as less competent.
This is scary stuff. The researchers explicitly make the point that they don’t think it’s due to explicit bias:
… we are not suggesting that these biases are intentional or stem from a conscious desire to impede the progress of women in science. Past studies indicate that people’s behavior is shaped by implicit or unintended biases, stemming from repeated exposure to pervasive cultural stereotypes that portray women as less competent but simultaneously emphasize their warmth and likeability compared with men.
So even though I have no explicit bias against women, it’s highly likely that I have an implicit bias. As I said, scary.
What can we do about this? There are some fairly obvious things, but I simply don’t know whether they are enough.
First, be aware of the possibility of bias, and look out for it. Following a link trail from Athene Donald’s blog led me to a really useful definition of discrimination used by Shara Yurkiewicz:
A preference is discrimination when:
1) decisions such as those about hiring people and setting their pay rate are based on generalizations about the demographic groups to which individuals belong
2) individuals have no control over the group to which they belong – and it is apparent from their appearance.
3) it is nearly impossible to predict how an individual will do the job based on the group to which he or she belongs.
In fact I had an argument along these lines with the speaker at a dinner I went to last night. He used the old joke about men’s and women’s thought processes being completely different. I found the joke marginally offensive, and told him so afterwards. He claimed that it was based on facts — men, on the whole, are more analytic, less touchy feely, and so on. I said on average, maybe, but what we have is two overlapping distributions. He agreed, but the root of the problem is that we disagree over the extent of the overlap. I am firmly of the opinion that the distributions are both pretty wide, with a large overlap, which according to the definition above puts us firmly in discrimination territory.