April 3, 2014 | Research Feature

Competing With Bias

Research shows how discrimination works in the hiring process and suggests that science and tech firms that leave it unaddressed may overlook talented female candidates.

In 2005, economist Larry Summers, then president of Harvard University, made a speech in which he noted one possible reason women are not as prevalent in science, technology, engineering, and mathematics (STEM) fields as men: women’s abilities were simply different and not as good at those of men. Many observers thought Summers was being unnecessarily provocative and the larger purpose of his speech — a discussion of how to diversify the ranks of scientists and engineers — was forgotten in the controversy that ensued. In that speech, Summers had also noted the other two plausible explanations for the lack of diversity in STEM industries: perhaps women prefer not to go into those fields, or, alternately, simple discrimination — skilled STEM women are overlooked for lesser or comparably skilled STEM men.

Young girls do in fact demonstrate plenty of interest and talent in STEM subject areas, and, as teens, score on par with their male peers on standardized tests in those areas, putting to rest the idea that the discrepancies can be chalked up to aptitude. But by the time they reach college, young women are not actively pursuing STEM majors and are outnumbered by male students in most STEM fields. So, ten years after Summers’s speech, the question remains: Why aren’t there many women in STEM fields?

New research from Professor Ernesto Reuben asks: Does discrimination exist in STEM industries? If so, what can be done to reduce it? Working with Paola Sapienza of Northwestern University and Luigi Zingales of the University of Chicago, Reuben found that bias is thriving and has potentially far-reaching effects — but that it can, to some degree, be offset.

In a series of experiments, the researchers mimicked how employers get information about candidates during the job interview process. First, they asked subjects to complete a task that men and women perform equally well but about which there is a pervasive stereotype that men perform better: correctly completing as many math problems as possible in four minutes. After subjects received their scores, the researchers randomly assigned these same subjects to play the role of either employer or candidate, with employers tasked with hiring candidates to complete another math-based task. (Employers earned more from participating in the experiment if they chose the candidates who had performed best on the first task, while candidates earned more if they were chosen by the employers.)

Employers each met two candidates in person at the same time and had to choose which of the candidates to hire to complete another set of math problems. But the information employers had about each candidate varied. In some cases, each candidate told the employer how well he or she thought he or she will perform on the math task, and then the employer made the hiring decision. (This would roughly correspond to an interview in which a candidate is selling her ability and skills.) In other cases, the researchers — a less subjective source of information — told the employers about candidates’ past performance before asking them to decide between candidates. In two other variations, employers chose between candidates based on sight alone first, before receiving any other information. In these cases, after employers made their initial choices, candidates then told employers their expected future performance, or the researchers told employers the candidates’ true past performance.

As the last step in the study, all the subjects took the Implicit Association Test (IAT), an assessment that measures associations of words and images through response times. “The test assumes that if, for example, you’re less quickly able to associate the words ‘female’ and ‘math’ together than the words ‘female’ and ‘humanities’ then your brain is trying to resolve a conflict about the association between women with math,” Reuben explains. “So the IAT gives us a measure of the relative bias of each of the subjects.”

The results were not encouraging: both male and female employers were strongly biased against female candidates in all variations of the experiment, choosing women significantly less than half the time (half being the rate women would be expected to be chosen in the absence of discrimination). Even in the variations where employers could change their choices after learning more about candidates’ performances from the researchers or through the self-reported scores, male candidates were still favored by at least 13 percentage points. And when the candidates self-reported, 9 out of 10 times when the employer chose the poorer performing candidate, that candidate was a male. (This is likely because when candidates self-report, the men tend to overestimate their future performance, a tendency Reuben has already documented.)

When employers got information from the researchers, the bias was reduced but not eliminated. Generally, those employers with IAT results indicating greater bias were among the most biased in the experiments; when they updated their hiring decisions after getting new information, they still chose inferior male candidates over superior female candidates with surprising frequency.

“Our experiment shows that discrimination can be costly to employers,” Reuben says. “You would think that in a marketplace competing for talent, firms that discriminate and don’t hire optimal talent would just disappear.”

It’s probably difficult to avoid discriminating, Reuben acknowledges. “You can’t not interview candidates, because you’d lose other valuable information — can they speak articulately and knowledgeably on the spot, are they a fit in terms of personality, and other considerations about someone’s potential success in a given firm’s culture. Those are all important subjective measures of performance.”

So what’s an HR department or a hiring manager to do? “Be aware. You can overcome your bias if you process the information you get properly — don’t give weight to stereotypes — ignore your bias, and concentrate on the information you do have,” he says. “If you’re comparing two women to each other, you’re probably going to make a better decision than if you’re comparing a woman to a man. So, when possible, you should evaluate candidates separately by gender. And that implies that firms might even want to ultimately consider a quota system.”

Ernesto Reuben is assistant professor of management at Columbia Business School.

Read the Research

Ernesto Reuben, Paola Sapienza, Luigi Zingales

"How stereotypes impair women's careers in science"

View abstract/citation  Download PDF  

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