Gender bias still exists despite several global attempts to make sure females and males are offered equal opportunity in the corporate world. Take a look at some of the job advertisements and it won’t take you long to spot there is an unconscious bias towards male applicants in almost half of the job adverts.
Global recruitment experts Michael Page and the University of Manitoba have revealed that female applicants are subtly discouraged from applying for jobs in a variety of lucrative sectors. The findings demonstrated how powerful words can be used to recruit: men can be supportive while women can be analytical. The study also identified gendered words that are most commonly used across several sectors.
Social psychologists who have been trained to analyze gendered language in job ads and Michael Page consultants were part of the expert team that investigated the gendered language bias. Tech, media and finance had the largest disparities in the use of masculine words. However, there was a higher ratio between masculine phrases used for ads and the ones used for media. The phrase "lead" was found to be the most used masculine phrase in all sectors except the financial and retail sectors. Every sector listed different versions of the feminine word “support" and only 16% contained a balance of masculine gendered words.
Recent research has shown that COVID-19 has made women 1.8 times more likely than men to lose their jobs. This is why subtle gender biases in the workplace such as gendered language are likely to slow down the rate at which women get back on the job ladder. These findings are especially worrying for women who may already have suffered a significant financial impact from the recession and pandemic.
Unfortunately, most recruiters are not aware of the potential consequences of gendered language, especially in deterring women from applying for jobs, and how it can hinder gender equality at work. The study has, however, provided tools to identify problematic job ads and assist with this equity issue.
Sheri Hughes, Director for Diversity and Inclusion at Michael Page commented on the results. "Being more inclusive requires listening as well as responding to important developments. It is now accepted that the pandemic has disproportionately affected women, and many of them are struggling to return to work after being laid off. We believe everyone should have an equal chance to recover from the effects of COVID-19.”
Findings show that there continue to be many institutional and conscious barriers to equality for women in the workplace. We are asking all companies to consider these findings and join us in committing towards raising awareness about gender bias in job advertisements and encouraging other recruitment firms to join our fight for a fair job market for everyone.”
In fact, employers will be more likely to attract candidates from all under-represented groups if they use inclusive language. Businesses will see a long-term benefit from this approach, as it increases their chances of finding the right talent to fill the positions they offer.
While reviewing the findings, Danielle Gaucher (Associate Professor of Psychology and Director of Intergroup Relations and Social Justice Laboratory at the University of Manitoba) said: “Although there are some people who believe these subtle biases no longer exist in 2021, they still pervade all aspects of society, including recruitment and hiring.
These findings show how powerful words can be applied to the recruitment environment. It is important to note that gendered words do not mean someone of the opposite gender cannot demonstrate a certain trait. For example, men can be supportive while women can be analytical. But many don't realize the potential consequences that gendered language can have, particularly in deterring women from applying for jobs and how it can be a barrier to gender equality at work. Now we have the tools to spot problematic job ads and help with this equity issue.
Linguistic Inquiry (LIWC), which codes the ads for gendered wording, was used to examine them. This software had been identified from gender stereotyping literature review. A specialist software program was also developed by the expert team. It could generate the most unique words from all job ads and categorize them by sector. All statistical analysis was done using IBM SPSS software.
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