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Why Men Still Get An Unfair Advantage Finding New Jobs

A large scale research of 10 million candidate profiles conducted by Talenya reveals why men still get an unfair advantage finding new job opportunities.  

It’s 2020, and women are still being paid 79 cents to the dollar as compared to men, with women making up only 40% of managers. Deep structural problems exist, starting with the hiring process. The problem is twofold - arising both from the way candidates are recruited as well as the presence of implicit bias when viewing candidates.

It isn’t that there aren’t qualified women in the pipeline, it’s that recruiters find and accept candidates who write resumes and professional networking profiles in a certain way -- the male way. Discrimination based on how people write their resumes is apparent in both human recruitment and improperly trained AI-powered recruitment systems. Amazon scrapped their recruiting AI after finding that there was gender bias, specifically related to certain language used by male candidates as opposed to female candidates.

Recruiters often use skills as keywords to search on sites like LinkedIn to find candidates. According to a study by Linkedin, profiles listing 5 or more skills are viewed 17X more than profiles with fewer skills. But, according to LinkedIn, women listed 11% fewer skills than men, meaning fewer opportunities to be found by recruiters.

However, the number of skills on a candidate profile is just one side of the story. Women also tend to write less text on their profiles, describing their career and achievements.

A recent study conducted by Talenya, analyzed over 10 million profiles and found that women write, on average, 34.2% less text on their public profiles on social sites like LinkedIn. 

In that study, Talenya looked at over 10 million profiles and divided males and females into 3 groups, depending on the amount of words on the profiles: short, medium and long. 

In the group with the shortest profiles, women outnumbered males by 12.3%. In the group with medium length profiles, that difference was 18.8% but in this case, in favor of males. In the group with the longest profiles, males dominated again, with 71.4% more representation. 

This study showed clearly that women tend to write less text on their profiles.

Recruiters are likely to consider sparse text on a profile as a reason to overlook or simply ignore candidates. With so many profiles to review, recruiters prefer to look at profiles that are rich and full of content to help them make an educated decision on whether to contact such candidates and invite them to an interview.

The first section recruiters review, the Talenya study finds, is the candidate self description. Then they are likely to look at the required skills and then the description of each of the roles they performed in their career. If recruiters look for a particular skill, they prefer to see it mentioned in either the candidate’s self-description or within the text describing the most recent role.

Aside from the recruiting process, women often face implicit bias in the hiring process. Implicit bias can be poignantly shown by one Skidmore College study relating to STEM hiring. In this study, Identical resumes were given to hiring managers, with the only difference being the gender of the name. The resume with the female gendered name was perceived as less competent and was less likely to be hired. Even when a hiring manager would hire the fictional candidate, her salary would be on average 13% than her identical male counterpart.

By understanding how bias against women manifests in the recruiting process, and being proactive toward changing recruiting practices accordingly, it is possible to find and hire more talented women into positions they deserve.

By: Robin Burkeman, CMO, Talenya. Talenya is an AI-powered talent sourcing solution, powering talent acquisition teams to uncover and engage with 3X more and diverse talent than any other tool. To learn more about Talenya's Talent AI™ click here. To learn more about Talenya's Diversity AI™ click here.

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