Jeanne Brett Chair; Professor of Management & Organizations; Professor of Psychology, Weinberg College of Arts & Sciences (Courtesy)
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Many organizations genuinely aim to create inclusive work environments that provide all employees with a sense that they belong and will be given an equal opportunity to succeed.
Conscious or unconscious stereotypes—based on employees’ identities or social-group memberships—can lead to biased decisions, which can prevent organizations from hiring, developing, and promoting the best talent. For example, the widely shared stereotype that women are more relational and family-oriented than men may lead to the often-misguided assumption that women are not committed to their jobs after they have children, which can result in them being offered fewer opportunities for advancement.
These types of disparities in employees’ treatment and outcomes can emerge at different points: when people apply for jobs, whether they are interviewed and hired, how they are trained, what kinds of assignments they receive, whether and how they are mentored, and how their performance is evaluated.
If you are interested in creating a more level playing field for all employees, your organization can use data to understand how your practices and policies might be contributing to disparities, rely on research-based solutions to address these disparities, and test the effectiveness of these solutions to ensure that they are having the desired impact in your organization.
The first step in creating a more equitable workplace is understanding where and how your company’s disparities begin to emerge. This understanding requires putting aside assumptions about what you think is causing those disparities and instead focusing on the data.
For example, if your company hires and promotes mostly white men, you might assume that your company has a pipeline problem, where you are simply not getting enough qualified applicants from underrepresented groups. Or, you may assume that your company has a problem at the performance-review stage, such that performance reviews do not fully recognize the contributions of underrepresented groups. Without data, it is impossible to know which of these assumptions is accurate.
To test your assumptions, it is critical to gather data at every level of the process through which people join the organization and then ascend—or stall—throughout their career trajectories. You might ask: Are initial resume screens or candidate interviews tipping the balance? Do your performance evaluations and subsequent promotion decisions exacerbate these disparities?
For example, if half of the resumes that you receive are from women, but only 30 percent of the candidates deemed “qualified” are women, then you may need to evaluate the criteria that you use to assess quality and consider whether these criteria may be biased against women. Or, if men and women receive similar performance reviews during their first three years, but men are disproportionately receiving promotions, you may need to further review your criteria for offering promotions.
Beyond measuring the impact of specific policies and practices, there may also be larger cultural problems that produce disparities. Many organizational cultures inadvertently prioritize the norms, perspectives, and practices of the groups that are most represented in them. If one group is allowed to determine the “rules of the game”—from how people are rewarded to how they are welcomed and socialized—that group can inadvertently undermine others’ opportunities to succeed.
In many technology companies, for example, work spaces are often designed with stereotypes of “geeky” men in mind. These kinds of practices can lead women to be less interested in joining these firms, can undermine women’s sense of belonging, and can ultimately reduce their interest in staying in the organization.
For leaders, it is critical to recognize the ways in which the standard policies and practices of an organization are not culturally neutral. For example, while your organization’s diversity statement may be designed to emphasize your organization’s interest in the common humanity of all employees, this focus on similarity is most likely to resonate with white employees and may invalidate the experiences of employees from underrepresented racial or ethnic groups. Many employees of color regularly feel different and are treated differently on a regular basis. In stark contrast to these realities of difference and exclusion, making norms of similarity the default may lead racial minorities to feel a lack of belonging in the organization.
Remember that, even if you identify one key source of disparities, it’s important to understand that these disparities can emerge and build on each other through various policies and practices. There may be an initial problem at the resume-review phase, which widens in the hiring phase, which widens again in performance evaluations. Knowing the sources of disparities will inform the intervention—or set of interventions—that will be necessary to level the playing field.
Once you have identified the sources of your disparities, your second step is to design and implement research-based solutions to make your organization more equitable. These solutions should target the source of the disparities and create greater consistency, transparency, and accountability in the organization’s policies and practices.
To promote consistency, leaders need to develop clear and standardized policies and procedures for making decisions about hiring, promotion, and work assignments. After all, without clear and consistent guidelines before initiating the hiring or promotion process, bias is especially likely to creep in and shape people’s judgment. For example, without establishing the criteria for hiring beforehand, people tend to adjust their rationale for hiring to fit or match with the person they want to hire.
Introducing a mentoring program, bias training, and a new promotion system all at once may seem like a good strategy, but it makes it impossible to discern which change or changes may be responsible for producing the outcomes you observe.
Consistency is also essential for defending against bias in managers’ decisions about how to assign employees different types of tasks. Leaders might have the best intentions to give all employees similar types of opportunities to learn and grow with challenging “stretch” assignments that are likely to advance their careers. However, managers may unintentionally offer men more stretch assignments, while asking their female counterparts to complete “office housework” tasks, such as taking notes or planning social events, that do not advance their careers. Relying on a consistent system for making these assignments—one that ensures both stretch assignments and office housework are distributed equitably for all employees—can help managers avoid falling into this trap.
To promote transparency, organizations need to design and implement policies to help all employees—even those who may be less comfortable and familiar with the culture of your organization—understand and take full advantage of the opportunities available to them.
In the case of promotions, for example, Google has found that men are more likely than women to nominate themselves for a promotion. To address this issue, Google simply made it clear to all employees what the promotion process was and then encouraged women who were ready for a promotion to apply. By making these expectations for self-advocacy much more transparent, the gender gap in promotions was reduced.
To promote accountability, companies should take steps to make people across the organization aware of their commitments to their fellow employees as well as the people the organization serves. Airbnb, for example, encountered a problem with racial discrimination on their platform, such that hosts were less likely to accept the booking requests of African-American guests compared to their white counterparts. For example, some African-American users found their requests were rejected and told that the booking was not available—only to later find that the space was still listed as available to others. Other African-American users found hosts to be more receptive to their booking requests when they changed their profile names and pictures to pose as white users.
To reduce the chances that hosts will rely on racial stereotypes to decide which guests to accommodate, Airbnb instituted systemwide reforms. The company now asks hosts to sign a community commitment pledge holding them accountable to not discriminate on the basis of social-group membership and has partnered with the NAACP to recruit more hosts from communities of color.
American companies currently spend billions annually on efforts to increase the diversity of their workforces. Yet most do so with little assurance that the money spent is having the desired impact.
Take Yelp’s attempt to encourage managers to be consistent in how they evaluated resumes and to therefore avoid bias. It employed a strategy of “resume blinding”—where names and other likely sources of demographic information are stripped from a candidate’s resume. When they tested their efforts, however, this research-based strategy did not work as well as Yelp had hoped. One reason may have been because resume readers can often glean information about a candidate’s gender, race, or social class based on other information on the resume.
So once you have a research-based strategy in your organization, your third step is to test that strategy to see if it actually produces the desired impact.
For example, Google used to recruit heavily at elite colleges—assuming that attending a top school signaled that a job candidate would be a top performer in their company. But focusing recruiting on such a small group of universities was limiting the company’s diversity—and an internal study suggested that attending an elite college had no bearing on employees’ performance outcomes at Google. These findings helped the company change its candidate recruiting strategy to include a wider range of schools.
How should you test whether the introduction of a new policy or practice actually improves diversity or inclusion in your organization? The gold standard is to design and run a controlled experiment to measure the effects of a particular solution in your company. For example, if you want to test the impact of a new mentoring program for underrepresented racial minorities in your organization, you would ideally want two groups of employees in your experiment, half of whom are randomly assigned to receive the new mentoring program and half of whom are not. These two comparison groups will allow you to assess whether the group of employees who received the new mentoring program fared better than the control group in areas such as promotion and retention.
Ideally, your experiment should test the impact of one initiative at a time. Introducing a mentoring program, bias training, and a new promotion system all at once may seem like a good strategy, but it makes it impossible to discern which change or changes may be responsible for producing the outcomes you observe. Without knowing what is working, you may not be able to replicate your success moving forward.
The drawback of experiments, however, is that they tend to be time-consuming and expensive. The next best strategy is to gather data before and after you change one policy or practice to see whether you observe better diversity-related outcomes after you make the change compared with beforehand.
This data-based approach requires understanding the sources of your disparities, developing research-based solutions, and testing their efficacy. This is the surest way to ensure that your diversity-related efforts are creating a level playing field for all employees.