How Stereotypes Impair Women's Careers in Science

A new article published in the Proceedings from the National Academy of Sciences explores the possible link between negative sex-based stereotypes and the fact that comparatively few women become scientists or engineers.

To isolate the potential effect of discrimination, the researchers--Ernesto Reuben (Columbia Business School, Columbia University), Paola Sapienza (Kellogg School of Management, Northwestern University), and Luigi Zingales (Booth School of Business, University of Chicago)--designed an experiment in which subjects were asked to perform an arithmetic task that, on average, both genders perform equally well (summing as many sets of four two-digit numbers as possible in a period of four minutes).  The subjects were informed of their performance.  Some subjects were then randomly selected to act as candidates applying for a job, while the rest of the subjects were to act as "employers" whose task was to evaluate pairs (male-female) of candidates, choosing one of the two candidates as their "employee" and estimating each candidate's performance on a second arithmetic exercise.  All subjects were asked to perform an Implicit Association Test (IAT) to measure implicit sex-math/science stereotypes.

Four treatments were used in the experiment:

  1. "Cheap talk": candidates communicated to the employer their expected performance on the second arithmetic task before the employer chose one of the pair as their employee.
  2. "Past performance": employers were told the actual performance of each candidate in the first arithmetic task before choosing one candidate as employee.
  3. "Decision then cheap talk": employers chose a candidate first, then saw the candidates' self-reported expected performance and were asked to update their choice of candidate and estimates of performance.
  4. "Decision then past performance": employers made their initial decisions based only on the candidates' appearance and then updated their decisions after being informed by the experimenter of the candidates' actual performance on the arithmetic test.

Results (discussed in a recent New York Times article) show that:

  • With no information about the job “applicants” other than their appearance, the employers (of both sexes) were twice as likely to hire a man over a woman.
  • In Treatment 1 ("Cheap talk"), where the job candidates were allowed to predict their own performance, men tended to exaggerate their acumen, while women downplayed theirs. But the employers (especially those with strong implicit stereotypes about women and mathematics, as measured by the IAT) failed to compensate for that difference, and were again twice as likely to choose a man.
  • When employers were given hard data about the applicants’ ability to perform the tasks in question, employers were still one-and-a-half times more likely to hire a man. When they knowingly chose the lower-performing candidate, two-thirds of the time they were choosing the male applicant.
  • Implicit stereotypes (as measured by the IAT) predict not only the initial bias in beliefs but also the sub-optimal updating of gender-related expectations when performance-related information comes from the subjects themselves.

This study indicates that, while hard evidence helps to reduce prejudice against women about math, it is important for employers (as well as teachers, professors, and graduate school admissions committees) to understand their own pre-existing beliefs: "The very people who are biased against women about math, they're also less likely to believe that men boast.  So they're picking up a negative stereotype of women, but not a negative stereotype of men," Zingales explains.  The results may also explain "why many women opt out of science and technology majors before they even reach graduation — they may assume that the negative response they are getting is based on their actual performance. "People don't even learn," he said, "that they are equally capable."

Read more: resources:
Gender in STEM Education: A Data-Driven Learning Guide (
Gender and Occupation: A Data-Driven Learning Guide (
Occupational Sex Segregation and Earnings Differences (
Women's Education (
Frederique Laubepin


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