If you’d stop believing the myth that women are too slow out of the gate when they are supposed to add numbers, and if you start believing the fact that men are too fast, we might be able to make a little progress on gender balance in technical careers. It won’t be enough to fix it, but it will at least help us trudge in the right direction.
The myth to bust says that women are worse than men at math. The fact we need to acknowledge is that men overestimate their skills.
Mythbusting in this case requires you to believe that you don’t always know what you believe.
It works like this: You have some beliefs. If you think carefully, if you look in the mirror, if you engage in introspection, you can find out what your beliefs are. Except not always. There are some beliefs we hold that we can’t discover by ourselves. These ones are simply unavailable via introspection. It’s annoying to admit it, but the evidence is strong.
The pursuit of quality and excellence requires that we develop specific strategies to counter the effects of implicit bias.
It’s like a blindspot. We can’t see the effects on our vision where the optical nerve passes through the optic disc — our literal blindspot — but we can do experiments to bring it out. It’s this way with those introspectively inaccessible attitudes, too; we need experiments to realize they are there.
Are women in your blindspot?
The phenomenon is called implicit bias. If we take an implicit association test, we can uncover our deeply held beliefs about many different topics. For example, if we test our associations of math and science with men and women, most of us we realize that we assume men are more qualified than women in these areas.
Could this bias affect how employers hire people for jobs requiring skills in math and science? Could the implicit bias leading us to doubt women’s skills be part of the explanation for their relative absence in math and science careers? New research suggests this is true.
Researchers created an experiment simulating the process of hiring someone to perform math tasks. The employers were put into three different situations.
- Either they only knew the sex of the two candidates they had to choose between.
- Or they knew the sex and how the candidates themselves thought they would perform on the task they were being hired for.
- Or they knew the sex and the actual performance of the candidates on another math task exactly like the one they were being hired to do.
The facts about discrimination
When employers had to choose between one man and one woman, a complete lack of sex discrimination would lead to hiring 50% women and 50% men. In fact, the first group of employers chose the woman about 35% of the time and the man about 65% of the time.
When employers had no information beyond appearance, they were twice more likely to choose male candidates than female candidates.
When they knew the predictions of the candidates, it hardly made any difference. But the employers who received objective information about the candidates’ earlier performance, hired women 43% of the time.
As an added twist, the employers who only knew the sex of the candidates were given a chance to revise their choice. One group had the chance to revise based on the predictions of the candidates and one group had the chance to revise based on the objective information about past performance. In both cases, many employers changed their decisions, but more did so when provided with objective information.
Hiring the wrong person
Not surprisingly, employers sometimes hire the wrong person. In this experiment, that means that they hire the person who in fact ends up doing worse on the math task.
Employers made suboptimal hiring decisions across conditions. Suboptimal hiring decisions were associated strongly with sex bias. Suboptimal decisions were made in favor of the male candidate significantly more often than in favor of the female candidate
Bad decisions, in other words, favor men. How do we know?
If we look just at the hiring mistakes, we can measure how many times a less qualified woman was chosen over a more qualified man, and how many times a less qualified man was chosen over a more qualified woman. To take the most extreme example, when employers are told what the candidates themselves say about their future performance, about 90% of the mistakes are hiring a less qualified man over a more qualified woman. 90%!
Do men overestimate their skills?
The experiment also allows us to evaluate how good the candidates were at predicting their future performance.
Men tended to overestimate their future performance on the arithmetic task, and women tended to underestimate it.
Employers also took implicit association tests, and those showing stronger stereotypes in their test results were less likely to be skeptical of men’s self-reporting. Their implicit bias not only makes them doubt women’s abilities; it impedes their capacity to filter the boasting of men.
The research I’m describing here enhances our understanding of gender-skewing in careers related to math and science. The effects of implicit bias are made explicit; we can no longer deny that it holds women back. As if that weren’t bad enough, the stronger our biases, the more likely we are to believe men when they overestimate their skills.
We also start to get ideas about what we can do from this research. When objective information about past performance is part of a hiring decision, the effects of discrimination are reduced. They aren’t eliminated, mind you, but they are less dramatic.
We know that women face the effects of implicit bias throughout their careers. The pursuit of quality and excellence requires that we develop specific strategies to counter this discrimination. Research is essential for identifying these strategies, and with the research discussed here, the next step might be just a little bit easier.
How stereotypes impair women’s careers in science. Ernesto Reuben, Paola Sapienza and Luigi Zingales. Proceedings of the National Academy of Sciences.
The blindspot metaphor is from Mahzarin Banaji and Anthony G. Greenwald’s excellent book on implicit bias, Blindspot: Hidden biases of good people.
Subsequently published at Science2.0 as Where women don’t belong: 2 strategies you and I both use to keep women out of science.
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