Social Norms and Gendered Expectations
March 26, 2015
Several years ago Clifford Nass, a late professor of Human-Computer Interaction at Stanford University, did a study where participants were taught a subject by a male or female voice on a computer. The two subjects taught were “love and relationships” and “physics.” Participants were randomly assigned to one of the four combinations of voice+topic to learn the material. At the end of the session, they completed a computer-based questionnaire where they were asked how effective each voice was at teaching the given topic.
Even though the material was identical between the voices, participants rated the male voice better at teaching “physics” and the female voice better at teaching “love and relationships.” When asked if gender played a role in their assessment of their tutor, participants uniformly said that would be ludicrous. This was a voice on a computer, after all. Every participant denied harboring any gender stereotypes, yet the evidence of gendered expectations was undeniable when looking at the data .
This article talks about social norms and gender biases, how they manifest in the workplace, and how we can start fixing them.
Social Norms and Gender Roles
Social norms are the social rules, either explicit or implicit, that define our expectations of appropriate behavior between people. They include things like shaking hands when you meet someone, standing a certain distance away from another person when you speak, and which way you face in a crowded elevator. Gender norms are a subset of social norms, and these are the behavioral expectations around a person's sex . Historically, social gender roles in the workplace have been largely binary—masculine and feminine.* There are many well-documented gender stereotypes associated with masculinity and femininity. These include things like women wear dresses, men like cars, women are nurturing, men are good at math, etc. It's important to note that these gender stereotypes are not necessarily true; they are simply things that a critical mass of people generally believe about male and female genders.
*Male and female are not the only genders that exist. The analysis portion of this article focuses on these two genders because of their historical significance in the tech and business worlds.
How are these biases created?
Humans are masters of pattern recognition; we use previous patterns to help us make decisions about future patterns. Our pattern recognition system was not developed to be perfectly accurate or objective, and it is deliberately biased to our own experiences. If a person has only ever seen white swans, they might assume that all swans are white (even though this is a misuse of inductive reasoning). A black swan would have a more difficult time convincing someone who has only seen white swans that he, too, is a swan. Our imperfect but hugely powerful brains pick up on patterns and, over time, can create unconscious biases about expected behavior.
Historically, Silicon Valley has been a hub of success for white men. These early pioneers are the metaphorical 'white swans' of our story, and for a long time Silicon Valley only saw white swans in positions like founder, CEO, venture capitalist, and engineer. This established a pattern of successful white swans, which people's brains picked up on, creating social norms for success that were largely white and male. While white swans aren't necessarily the only kinds of swans—or even birds—who can be successful, our brains nonetheless have ingrained this pattern. As John Doerr, a famous venture capitalist, said in 2008:
Because of this long-standing pattern of successful white men in Silicon Valley, many of the qualities, communication styles, and behaviors of white men are also associated with success—behavior like assertiveness, dominance, or even interrupting . And our bias doesn't just tend towards male-centric qualities. Age-ism is also prevalent in the industry, with investors and news media outlets stereotyping successful founders as being young based on a few high-profile examples, like Mark Zuckerburg or Ben Silbermann .
When looking for new hotshot engineers, founders, or leaders, people default to these stereotypes about past patterns and often look for people who are young, white, or male. Even when we encourage people who are different from past examples to take on these roles, we have a hard time letting go of the patterns we've seen and expect them to exhibit behavior in line with our idea of success.
What happens when people violate social norms?
Given that we have these expectations about how people behave, what happens when someone violates our expectations? Violating social norms elicits varying degrees of responses. If someone steps into your personal space you might step backward to correct the violation. If someone older acts too "young," they might be mocked for their behavior in an attempt to let them know it is unacceptable. And if someone were to crowd you in an elevator or stand facing you directly, you might give them an angry look or make a remark about their behavior.
In Professor Nass's study on voices and learning, when the gender of the voice matched our gendered expectations about the material, the voice became more credible. Conversely, when the gender of the voice and the gender stereotypes about the material did not match, the voice lost credibility. And social norm violations can affect more than just the perception of credibility. Norm violations can actually elicit punitive behavior from the people around you. When faced with a violation of a social or gender norm, people will exert something called 'social control' in an attempt to realign the situation with their own expectations of behavior. Trying to re-align someone's behavior can involve giving them an angry look, negative comments, less money or promotions, ignoring them, and even excluding them from the group .
Damned if you do, damned if you don’t.
Workplace environments that require masculine behavior for advancement force women to choose between behavior that's in line with a promotion or behavior that's in line with social expectations. The first could earn them negative remarks and ostracism, the second could prevent them from being promoted. The court documents released on the Ellen Pao trial are an interesting example of how these competing, subconscious biases can play out in employee feedback.
In some performance reviews and emails shown during the trial, Kleiner partners complained that Ms. Pao did not speak up during board meetings and was “passive, reticent, waiting for orders in her relationships with C.E.O's” . In others, she was criticized for speaking up, demanding credit and “always positioning,” as one male partner wrote . She was even given coaching and training on how to better interrupt people in meetings . This section is not a commentary on the guilt of either Kleiner-Perkins or Ellen Pao. The feedback she received simply illustrates the competing biases between masculine and feminine behavior in the workplace.
People are almost entirely unaware that they have these subconscious biases, regardless of their own gender. In the introductory study of gendered voices and topics, all of the participants denied harboring gender biases while the data unequivocally showed that they did. The same thing can happen in the workplace, where people honestly don't realize that they harbor unconscious expectations about how women or other groups should behave. Because of the way that work environments are structured to reward male-centric behavior, these subconscious biases can systematically punish women more than men.
How does this manifest in the workplace?
Subconscious biases affect a lot of different parts of the work environment. They affect funding, promotions, investment, feedback, and the types of jobs people are encouraged to take. The promotion and executive gap between men and women is often attributed to unconscious biases, and countless articles over the years have tried to get to the bottom of why. An article in the Harvard Business Review attributes the promotion gap to the fact that women have mentors and men have sponsors , a McKinsey report found that men are promoted based on potential and women are promoted based on accomplishment , and many more articles talk about the role of gender bias in creating the promotion gap between men and women .
If we go back to the swan analogy, anyone who doesn't fit the past pattern of successful, white swans has to spend more time proving themselves. Black swans and other types of birds have to prove that they can do the job before they're promoted, whereas someone who exudes more 'white swan-ness' will be promoted based on pattern matching. When this happens on an engineering team the differences start small. It can look like men advancing to mid-level engineer several months sooner than women who were hired at the same time and skill-level. Those same men then become tech leads over their female peers because they have a steeper career trajectory. This effect cascades with each promotion until you see the gender and race gap that we have in companies today, with women and minorities clustered at the bottom of company hierarchies.
Women will leave these environments because it's demoralizing to work someplace where there is limited upward trajectory. Fixing this issue is crucial for retaining female engineers and curbing the cycle of attrition in software engineering. If this isn't solved you end up with a real-life version of the movie “A Bug’s life”. They come, they don't advance, they leave. They come, they don't advance, they leave.
How can we change it?
Changing deep-seated, subconscious biases is hard. I mean, they're deep-seated and subconscious, after all. Good intentions aren't enough to change our brain's hyper-active yet not entirely accurate pattern recognition. So the biggest and first piece of advice I can give is don't trust yourself.
Tracking data is one of the best ways you can remove reliance on your own faulty judgement and biases. Data can reveal trends and behavior that go against our expectations, things like pay gaps, promotion gaps, and mentorship gaps. Track employees' progress in their roles and progression through the company. How long do they spend in each role? How long does it take for them to get a salary bump? How much is that salary bump? Make sure that women and minorities are not falling behind men. A small startup might have too little data to see trends, but even tracking it will help you think about biases you might have and notice discrepancies early. Data is imperfect, but it is still less biased than the average human. Making it a part of your companies efforts to reduce bias and discrimination is a big step toward something resembling 'objectivity'.
Changing the Pattern
The next thing that can be done is to try to change the pattern. Challenge the traditional ideas of how successful engineers and leaders look and behave. A successful leader can be soft-spoken and introverted. A talented engineer can be girly and socially adept. You can do something like what Ben Horowitz has done on his blog—use the opposite pronoun from the one you would stereotypically imagine . Ben Horowitz refers to founders, CEOs, and engineers he writes about as 'she' instead of 'he'. This helps us envision characters who are different from the stereotypical expectation. Our brains are flexible, and pattern recognition can be expanded to include new ideas of what a successful leader or engineer looks like. People are, after all, able to recognize black swans with a little effort.
Finally, be cognizant of how you give feedback. A desire to give someone personal feedback about their behavior might indicate a bias on your part. You are, after all, trying to get them to fit your expectations of how they should behave. There are likely times that kind of feedback should be given, but think long and hard before you do. You don't want to end up giving feedback that sounds like the McSweeney article Reasons you were not promoted that are totally unrelated to gender.
The most important thing you can do is to be mindful that these biases exist. Try to notice when your own biases affect decision-making and work to correct the behavior. Take chances on women and minorities, and promote them into roles outside your comfort zone. Track promotions and salary for your employees so you have visibility into what's going on and aren't relying entirely on your own unconscious stereotypes and expectations. Remember, instincts and judgement are the flawed part of the system, not the performance of groups that don't match classic patterns for success.
- Nass, Clifford; Yen, Corina (2010-09-02). The Man Who Lied to His Laptop: What We Can Learn About Ourselves from Our Machines (p. 7). Penguin Group US. Kindle Edition.
- Wikipedia. Gender Role
- Bowles, Nellie. VCs in Bathrobes, Women Asked to Play Secretary, Angry Texts: Day One of Pao v. Kleiner Perkins. Recode.net, 2015.
- March, Evita. There's A New Catch-22 For Women In The Workplace. BusinessInsider.com, 2014.
- Scheiber, Noam. The Brutal Ageism of Tech. NewRepublic.com, 2014.
- Brauer, Markus; Chekroun, Peggy. The Relationship Between Perceived Violation of Social Norms and Social Control: Situational Factors Influencing the Reaction to Deviance. Journal of Applied Social Psychology, 2005.
- Miller, Claire Cain. A Racy Silicon Valley Lawsuit, and More Subtle Questions About Sex Discrimination. NYTimes.com, 2015.
- Bowles, Nellie. Performance Review Rewrites and Pao’s ‘Genetic Makeup’: Pao vs. Kleiner Perkins Trial Day 4. Recode.net, 2015.
- Miller, Claire Cain. The Kleiner Perkins Lawsuit, and Rethinking the Confidence-Driven Workplace. NYTimes.com, 2015.
- Carter, Nancy M.; Ibarra, Herminia; Silva, Christine. Why Men Still Get More Promotions Than Women. Harvard Business Review, 2010.
- Barsh, Joanna. Unlocking the full potential of women in the US economy. McKinsey.com, 2011.
- Clancy, Susan. The Hidden Reason Women Aren't Making It To The Top. Forbes.com, 2014.
- Horowitz, Ben. Blog
How our Engineering Environments are Killing Diversity Blog Post Series
- How Our Engineering Environments are Killing Diversity: Introduction
- Argument Cultures and Unregulated Aggression
- Criticism and Ineffective Feedback
- Onboarding and the Cost of Team Debt
- (this post) Social Norms and Gendered Expectations