What is effective in promoting gender equality?
Certain things do work if they are appropriately designed. If we take talent management as an example, we can trace a person's life once they enter an organization. This begins with talent acquisition.
Most organizations would argue that they would like to take advantage of the entire talent pool. One way to begin is to de-bias our job advertisements and the language we use in them. We must scrutinize the job descriptions we use in our job advertisements. Let us cast a wide net and use inclusive language. Not every word has a gender-neutral equivalent. However, the research suggests that if you use a highly gendered term such as assertive, which may be an essential characteristic to look for, you should pair it with a non-gendered term such as collaborative.
As we begin to evaluate people, we enter the more challenging territory. It is not easy because most of us believe we're pretty good at it when the evidence suggests otherwise. We are very likely to be swayed by someone's appearance, even when appearance is not a good predictor of performance. We are influenced by various factors, including whether we share a common hobby, have a similar accent, or are from the same country—all of which should be irrelevant in theory.
What are your options there? I would recommend that companies blind themselves to the demographic characteristics of job applicants during the initial stage of the evaluation process. This entails the erasure of names. Age is still included [on applications] in some countries, such as Germany and Switzerland. In several countries, you are still required to have a photograph with your job application. All of that should be eliminated.
Here, we can genuinely learn from the orchestras and attempt to train our minds to focus on the candidate's quality rather than how someone looks the part. One example is Applied, a start-up that investigated the effect of blinding. It partnered with a technology company and required all applicants to undergo the traditional hiring process. Simultaneously, each application went through the Applied process, which included blind evaluations. This technology company discovered in the end was disciplinary bias, not gender, racial, or other biases. It believed it was recruiting only computer scientists and engineers—a small percentage of the general population. Once the company became blind to some of those characteristics and relied on job sample tests in which candidates were exposed to some of the tasks they would perform, it began hiring neuroscientists and psychologists, individuals who were capable of performing the work but did not naturally fit into the category from which it would hire.
Changing the default setting to effect change
One of the earliest insights in behavioral science was the importance of defaults. Therefore, it is critical to begin our assumptions in the right place. For example, we recently heard from a company that changed the default for part-time work in their job advertisements, stating that while part-time work is the default, candidates can opt-out and work full-time if they wish.
Telstra is Australia's largest telecommunications company. It altered the default setting to one of flexibility. Every job advertisement now states that flexible work is an expectation. And the norm in its firm culture is to inquire, "Why are you in the office today?" Isn't it possible for you to work from home?" I already know from Telstra's data that it significantly increased the likelihood of women applying.
The horizon beyond: People analytics
I am not suggesting we leave this to machines. However, I argue that we should use machines, algorithms, and data to make those decisions much more intelligently in conjunction with humans.
Dismantling the gender barrier
This is a critical point of view. We've thrown money at the problem via diversity- and leadership-development programs, attempting to assist historically marginalized groups such as women, people of color, and people with disabilities. Unfortunately, that is not the best way to proceed.
We need to understand what is broken and intervene where problems exist—to dissect what is broken and then attempt to repair it using data on what works. I am quite optimistic that big data analytics and experimentation will significantly alter the landscape over the next decade. However, I bring up experimentation to emphasize that we do not yet possess all the answers.
About Jim Woods
Jim has more than two decades of experience driving change around diversity, equity, inclusion, performance, growth, and innovation. He's designed and led complex transformation initiatives in companies linked to globalization, demographic changes, sustainability, shifting business models, and new technologies.
Earlier in his career, Jim served in the United States Navy and taught fifth-grade math and science, including university human resources and leadership. Also, Jim has taught at Villanova University. He has authored six business books on DE&I, and leadership.
Education
Capella University, MS in Organizational Development and Human Resources