If you’re an Asian jobseeker with all the right credentials, you might still find yourself at a disadvantage because of your name.
A Canadian study concluded that job applicants who bear Asian names – including Pakistani, Indian, and Chinese names – receive fewer interview invites compared to those with Anglo-sounding names, as reported by The Star.
The study that was conducted by University of Toronto and Ryerson University separated the applicants in three categories: Anglo-sounding names with Canadian qualifications, Asian-sounding names with Canadian qualifications and Asian-sounding names with foreign qualifications.
The study found that even for those in the Asian-sounding category who have similar or higher qualifications, they still get 20-40% less call backs compared to those in the Anglo-sounding category.
Even if some Asians come with a master’s degree, it is still overlooked, while their Anglo-sounding counterparts were better received even if they only had a bachelor’s degree.
Dr. Rupa Banerjee, co-author of the study, called this phenomenon an “employer’s implicit bias.”
“They have seven seconds or less to look at that resume and make a decision if it should be in the look over again pile, or don’t look over again pile,” Banerjee said.
Similar to an employer-job applicant setup, the hiring manager might make “very quick unconscious” decisions because of this said “implicit bias.” But Banerjee argued that “implicit bias” is not racist, but something that is innate in everyone.
Using her name as an example, Banerjee said that a manager might assume that she is not very good with English once her foreign name is revealed.
Fortunately, jobseekers with Asian-sounding names have higher chances to get a call back if they apply for a position in larger companies, thanks to their more sophisticated Human Resource departments.
In order to solve the problem, it seems like “blind hiring” would be more preferable. In this way, managers can focus more on the credentials and the skills of the applicant instead of other unnecessary biases.