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HBR: Research: A Method for Overcoming Implicit Bias When Considering Job Candidates

by Zhiyu Feng, Yukun Liu, Zhen Wang and Krishna Savani.

“We are committed to a balanced gender distribution and value a variety of backgrounds and experiences among our employees.”

“All applicants will receive consideration for employment without regard to race, color, religion, sex, or national origin.”

“Experience the difference. Make the difference!”

Statements like these are common in job postings these days. Hiring diverse employees has become a critical goal for organizations around the world. And yet, many companies are failing to bring in a diverse workforce.

One reason for continued lack of diversity is that even if similarly qualified candidates from diverse backgrounds apply for job openings, recruiters, because of implicit biases, gravitate toward candidates with identities that fit a stereotype (e.g., men in the technology industry).

Our research identified an economic, convenient, and effective intervention to nudge recruiters to select more diverse candidates: partitioning candidates into different categories.

Our research built on the partition dependence bias, which occurs when people have to choose multiple options out of many available options. When the options are grouped together based on a given dimension, people tend to think, “Let’s choose some from each category.” Therefore, people tend to choose some options from each group, ultimately choosing more diverse options.

Our paper recently published in Organizational Behavior and Human Decision Processes describes eight experiments in which we asked people to review job candidates’ profiles or resumes and choose a few candidates to interview. We either categorized the candidates along a diversity-related dimension (e.g., gender, ethnicity, nationality, or university) or just randomly interspersed them. In every experiment, we found that when candidates were categorized on a given dimension, people chose more diverse candidates on that dimension.

For instance, we conducted a study with 121 experienced HR professionals who had an average of eight years of HR-related experience. We asked them to download a zipped folder containing resumes of 16 job applicants who graduated from one of four top universities. In one version of the study, the order of the resumes was random and did not vary by university. In the other version, the resumes from each school were contiguous in the folder (i.e., the files in the folder were sorted alphabetically). All HR managers were asked to select four candidates to interview. We found that when the resumes were randomly interspersed, 14% of managers chose candidates from all four universities, but this number more than doubled to 35% when the resumes were grouped together by university. We found similar results when we grouped candidates by gender, either by listing them contiguously or by using a paper clip to hold their printed resumes together. We found similar results when grouping candidates by ethnicity and nationality.

In two of the experiments, we found that grouping candidates together increased the diversity of the selected candidates without reducing the quality of the selected candidates. For example, when male and female candidates differed in their mean college GPAs, grouping men and women candidates led managers to choose more gender-diverse candidates but did not affect the average GPA of those selected.

What about cases in which a manager can select only a single candidate rather than multiple candidates?

Here, we presented people with brief profiles of six job candidates, three of which were European American, one African American, one Latin American, and one Asian American. In one condition of the experiment, all candidates were listed on a separate line on the screen. In another condition, European American candidates were listed contiguously on a single line, but minority applicants were listed individually on separate lines. When majority candidates were grouped together but minority candidates were listed separately, 15% more people chose a minority candidate.

To implement this strategy in the workplace, when sending resumes of applicants to hiring managers, organizations can put all resumes of candidates from well-represented backgrounds in sub-folders, but put resumes of candidates from under-represented backgrounds in the main folder (without grouping them into sub-folders). The relevant group-related information (e.g., gender) can be collected in the application form, and the application website can be programmed to automatically put candidates from different groups into different folders. The folders can be named “Batch 1,” “Batch 2,” etc. to avoid explicitly drawing attention to the grouping.

One limitation of our nudging strategy is that if managers have strong biases against a particular group, then putting candidates from that group into a separate category has no effect on their hiring decisions — they’re still unlikely to select someone from that group. This nudge is only likely to work when managers don’t have strong biases against a particular group.

Importantly, this intervention does not restrict managers in any way — they are absolutely free to choose whichever candidates that they want. It only draws managers’ attention to qualified minority candidates who might otherwise not attract their attention.

Overall, if companies want to bring in more diverse talent, they need to change the way they hire new employees. Our research identifies a simple but effective tool — partitioning candidates into different categories — that can help organizations build more diverse workforces without restricting managers’ choices.

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