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It’s an elaborate and crowded time to share with you gender and the workplace. But it’s a significant conversation nonetheless. Women remain vastly underrepresented in traditionally male (and high-paying) industries such as for example computer science, engineering, and science. And according to recent data, significantly less than 13 percent of Silicon Valley engineers are female, women represent just 4.2 percent of senior venture capitalists, and a measly 3 percent of tech startups are founded by women. And there’s the ample anecdotal evidence detailing Silicon Valley’s testosterone-fueled "brogrammer" culture.
The question of how exactly to achieve better gender balance is a topic of hot debate. Proposals range between banning the term “bossy”, to creating active mentor networks, to counseling women against the dangers of “having a chip on the shoulder.” And there are those that believe that the complete issue is merely overblown hype.
By two new studies published the other day in the Proceedings of the National Academy of Science, it’s really not. Gender bias against ladies in traditionally male dominated fields, both studies suggest, is both subtle and pervasive, an insidious influence it doesn’t have a straightforward, one-stop antidote. (Banning "bossy" isn’t likely to cut it.)
A guy and woman deliver a pitch… In the first study, researchers at Harvard Business School showed slideshows of U.S. entrepreneurial pitch competitions to experienced business investors. Some investors watched a slideshows narrated by a lady, although some watched one narrated by a male. The investors couldn’t actually start to see the person actually presenting; they could only hear his / her voice.
Related: ‘Visible and Engaged’: Women on Breaking In to the Tech Industry
In both cases, the pitch script was identical. To make sure that results weren’t suffering from a presenter’s presenting and public speaking ability, researchers also pilot tested the voiceovers in order that male-and-female-narrated pitches didn’t differ on variables like enthusiasm, confidence and pleasantness.
Again, this content of the pitches were identical. Not surprisingly, "the male-narrated pitch was rated as more ‘logical’ and ‘fact-based,’" says Alison Wood Brooks, the study’s lead researcher and social psychologist at Harvard Business School. Overall, investors were 60 percent much more likely to purchase a pitch presented by a guy.
Adding another layer to the experiment, the investors received a photo of an individual to accompany the narrated pitch. Works out the attractiveness of the presenter mattered – not for women. While a handsome man was much more likely to attract investor dollars, an attractive female presenter held no advantage over her plainer peers.
While this initially surprised Brooks, she says the findings illustrate the ‘lack of fit’ theory. "When people imagine a business owner, they often imagine a guy, perhaps a physically attractive man," she says. "They don’t usually imagine a female." For a few investors, Brooks theorizes, a lady presenter clashes against their preconceived picture of what a business owner should appear to be – her attractiveness, therefore, is irrelevant.
Related: Facebook’s Sheryl Sandberg: Eliminate Bias That Women ‘Aren’t Designed to Lead’
For Woods, this study illustrates the issue of trying to accomplish gender equality in traditionally male-dominated fields. "We think many people concur that women and men deserve equity at work and in entrepreneurship," she says, but biases could be sneaky; often, they operate fully beneath our conscious awareness. How exactly to fix this? Sadly, Woods says, there’s really no easy solution. "It’ll be difficult to un-do subconscious biases just like the one we’ve captured inside our paper…they are culturally constructed over lengthy intervals."
Who you hire to accomplish math? In the next study, three business school professors took on the problem of gender bias and math by asking nearly 200 volunteers to judge 96 candidate pairs and hire one individual to complete some arithmetic tasks that women and men, typically, performed equally well.
Without information other than the work candidates’ appearance, the volunteers (or "managers") were doubly likely to select a man over a female. Interestingly, this is true for both male and female "managers."
Next, the experimenters allowed job candidates to predict how they might perform the task accessible. While men tended to boast and inflate their ability, women often downplayed theirs. Most managers, both male and female, didn’t compensate because of this and once again, these were twice as more likely to hire a guy as a female.
Finally, the experimenters told the managers how each applicant had done on a previous math test. Despite having this highly predictive, objective indicator of success, gender discrimination didn’t disappear altogether; managers were still thirty percent more likely to employ a man for the work. So when they knowingly find the lower-performing candidate, two-thirds of that time period these were choosing the male applicant.
Each manager was also given an “implicit association test,” or I.A.T., to measure their gender bias in terms of math and science. “The more biased someone was against woman and math, the less these were in a position to adjust and recognize that men boast,” says Luigi Zingales, the study’s lead researcher. “For anyone who is biased against women, you penalize them for a poor stereotype that they aren’t proficient at math nevertheless, you don’t penalize men for boasting.”
Related: What Must Happen for More Women, Minorities to find yourself in Computer Science
In economics, you will find a distinction between rational discrimination (“Which sounds terrible, Zingales says, “but implies that you are biased against several people because you have relevant information regarding them”) pitched against a complete stereotype, where in fact the bias is unfounded.
You can view complete stereotyping at the job in the 3rd experiment. Even though managers had usage of highly predictive data, many thought we would ignore it. An unbiased person, Zingales says, should be able to choose a candidate predicated on past performance alone. But bias against woman and math can cloud a person’s capability to select the best candidate. “You don’t update fully,” he says. “You are essentially resistant to processing information, which will keep you from making the decision predicated on predictive data.”
So what to accomplish? Simply having more ladies in the office could also work to diminish gender bias; too little women, be it in a company, field or region (Silicon Valley) could be self-perpetuating. “An employer can only just observe the performance the ladies he hires. If he hires doubly a lot of men as women, he has far better information regarding the men than about the ladies, and he’s less inclined to update his position.”
It’s a classic chicken or egg problem, but Zingales believes strides could be made if employers simply recognize that bias exists to begin with. If he was ever devote charge of owning a firm, he says, he’d make it mandatory for all those responsible for hiring to take an I.A.T test. Bias, while often subconscious, can nonetheless be powerful. “Awareness goes quite a distance,” he says.
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