Earlier this month, a memo written by (former) Google employee James Damore went viral. The controversial, ten-page letter suggested the company has fewer female engineers because men are better suited for the job. Damore argued that Google’s initiatives to increase diversity is actually a discriminatory policy, and that a liberal bias throughout management makes it difficult to discuss the issue internally. The debates surfaced by this event rage on.
Aside from personal beliefs, there is a core business case to be made for diversity, one that we have made more than once on this blog. Research by McKinsey and others shows that companies with more diverse workforces perform better financially. Numerous studies continue to show the value that gender diversity has proven in boosting productivity and the bottom line within all levels of a company, from entry level to the boardroom, as well as the critical role women play in enhancing the collective intelligence of groups (see research by MIT Sloan Professor Thomas Malone).
So why is it that the workforce of tech companies is so predominantly male? In their most recent annual diversity report, Google shared that 31% of its employees are women, and only 20% of its technology roles (vs administrative occupations) are filled by women.
One explanation for the gender imbalance in tech and engineering may have to do with negative group dynamics. A new study co-authored by MIT sociologist Susan Silbey finds that many women become disillusioned with their STEM career prospects after poor internship and summer work experiences during which gender dynamics appeared to generate more opportunities for men to work on the most challenging problems, sidelining women for routine or managerial tasks. A recent Wall Street Journal article on the topic reported, “The No. 1 reason women leave tech isn’t a life transition like starting a family, but the fact that they didn’t feel welcome or included at their companies, in their teams or within the industry as a whole.” The article cites various sources for this data, including the aforementioned MIT study.
Another reason may be what MIT Sloan Professor Emilio Castilla refers to as the “paradox of meritocracy.” Progressive companies that foster merit-based practices assume they are not biased in their decisions around hiring, retention, compensation, and promotion. But, unfortunately, true meritocracies don’t really exist. Castilla’s research studying workplace inequality and merit-based pay have shown that these approaches are no protection against demographic bias.
“When managers believe their company is a meritocracy because formal evaluative and distributive mechanisms are in place, they are in fact more likely to exhibit the very biases that those systems seek to prevent,” writes Castilla in an article for MIT Sloan Management Review. “Achieving meritocracy in the workplace can be more difficult than it first appears.” Castilla has also demonstrated that managers tend to more favorably review employees who are like them.
The good news is that establishing a fairer workplace doesn’t require an inordinate amount of time or resources. “Companies can develop into meritocracies by implementing merit-based evaluation and reward systems that have both accountability and transparency,” says Castilla. Castilla advocates for establishing clear processes and criteria for any employee career decisions; monitoring and evaluating the outcomes of such company processes; and bestowing an individual or group within the organization with the responsibility, ability, and authority to ensure that those formal processes are fair.
“The collection and analysis of data on people-related processes and outcomes—what is referred to as ‘people analytics’—are key here, enabling companies to identify and correct workplace biases.” If you’re interested in learning more about making better employee-related decisions, as well as the new science of people analytics, Castilla leads a two-day, MIT Sloan Executive Education program, Leading People at Work: Strategies for Talent Analytics.