Stereotypes in Hiring
A Racial Comparison
While it has progressively become less tolerable to hold racist tendencies in the United States, people still harbor racial stereotypes. Studies show that people judge individuals of the same race differently based on their racial prototypicality, or how closely their appearances resemble racial stereotypes. For example, more prototypical Asian faces have flatter noses and smaller eyes. In this study, we tested the effect of racial prototypicality on people’s opinions in job hiring. We ran 120 participants who were employed in business and STEM and had them rank Asian and white applicants for business and STEM jobs. Each job listing had three equally qualified applicants: one was white, one looked stereotypically less Asian, and one looked more stereotypical Asian. Each listing also had one gender to control for gender bias. The results show no difference for all applicants in STEM jobs for the male and female conditions. For business, there was a significant bias against white females while the other Asian female applicants were ranked similarly. These results were not replicated for the male business applicants. While we did not find evidence of racial prototypicality prejudices, this bias against white women in business brings up questions about possible negative stereotypes surrounding white women about their abilities in business. Thus, while the data shows racial equality in STEM positions, there is still evidence of inequality in the hiring system in the business field which should be acknowledged and addressed for creating equality in the United States.
While the United States has long been called a melting pot, this country has struggled with racial prejudice and discrimination since its founding. Today, having racist tendencies is increasingly becoming less acceptable, but studies and experiences in everyday life have shown that people still harbor racial stereotypes. One area where this is apparent is in the hiring process for jobs. For example, a previous study revealed that if a job application is altered by simply changing the race of an applicant, people will discriminate against the black applicant if they score higher on a racism measure (McConahay, 1983). In another study, white names were 50% more likely to receive a callback than black names with resumes of similar quality (Bertrand & Mullainathan, 2004).
Furthermore, previous studies have shown that people will judge individuals of the same race differently based on how racially typical they look. This phenomenon is called racial prototypicality, which is how much of a race a person looks. A black person, for example, can look “more black” than another black person (Strom, Zebrowitz, Zhang, Bronstad, & Lee, 2012). If a person looks “more black,” this means their faces have more stereotypically black facial features, such as wider noses or fuller lips. Unfortunately, people are also more likely to associate the “more black” person with violence and delinquency (Kleider, Cavrak, & Knuycky, 2012). For other races, there are similar stereotypical facial features that can make someone look more or less of a race; for instance, a “more Asian” face would have traits such as small eyes and a flat nose (Kaw, 1991).
MATERIALS AND METHODS
In this study, I wanted to complete further research on the impacts of racial stereotyping and the effects of racial prototypicality on job hiring, with a specific focus on Asians, as there is a lack of research on hiring prospects for Asians in the United States. To do this, I created a survey on Qualtrics that would be distributed through Amazon Mechanical Turk, a website that allows adults to take questionnaires and participate in research. The survey asked participants to take the place of a hiring manager by ranking applicant resumes for a certain business or STEM (science, technology, engineering, or math) job. Each job listing had four applicants, and the participant rated the four of them in the order of most suitable to least suitable for the job. Three of the applicants had equally qualified resumes, while the fourth had a resume that was less qualified (lower GPA, lower-ranked university, and irrelevant major) to give the impression that there was indeed a correct overall ranking. The three equally qualified resumes went to a white applicant, an Asian applicant that looked less stereotypically Asian, and an Asian that looked more prototypically Asian. The applicant that had a worse resume than the others was either white or Asian; the number of conditions that had white applicants in this spot was equal to the number of situations that had Asian applicants in this position. Furthermore, the conditions only contained either males or females, to combat the possibility of gender bias.
The faces were taken from a study where they had already been rated on how racially prototypical they looked (Strom, et al.); thus, I was able to use these ratings to determine which faces would be used. Within the survey, we had eight job listings that included Asian applicants; there were four male, four female, four STEM, and four business conditions. The other job listings contained different occupations and applicants of different ethnicities in order to distract participants from the focus of the study, which were the Asian conditions.
In order to account for any unconscious differences between the similarly qualified resumes, the resumes were swapped between the “most Asian” applicants and the “least Asian” applicants for later repetitions of the study. In total, 290 participants were run on MTurk, but many results were excluded as we only wanted the participants who were employed in STEM and business fields since those were the positions that the survey was “hiring” for. Additionally, we also included a question in the survey that asked what they thought the study was about. If a participant noted anything about race or judging applicants based on their differences, their results were also excluded. Thus, we ended up with the results of 120 participants.
The results were very interesting, and were completely different to what we had expected. After all, we had predicted there to be evidence of a certain prevailing Asian stereotype, which is excelling at math and science but being shy and awkward. Thus, we expected that the more Asian an applicant looked, the better ranked they would be for STEM jobs, but the worse ranked they would be for business jobs. The opposite would apply for the white applicants, where they would be ranked highest for the business jobs and ranked the lowest for the STEM jobs. The less prototypically Asian would be ranked in between the white and the more stereotypical Asian applicants.
However, none of these predictions ended up becoming true. We found that there were no significant differences for STEM jobs across the board, as the participants ranked everyone similarly for STEM jobs for both the men and women conditions. The same also held true for men in business. The finding that was incredibly surprising for us was that there was a significant anti-white bias against females in business (F (2, 238) = 3.761, p = .025). We also found that the female participants ranked white males better than the male participants did, and that female participants also ranked white females worse than the male participants did for both the STEM and business conditions (F (2, 238) = 5.920, p = .003).
The results that we received were quite unexpected; after all, we believed that Asians would be discriminated against in business, rather than white women being discriminated against. A possible explanation could be a mixture of stereotypes and sexism. For example, while white and Asian male applicants were rated similarly for both jobs, only the female white women were discriminated against in business. Previous studies have shown that women who displayed more traditionally masculine traits (aggressiveness, outspokenness) were more likely to be successful in traditionally masculine roles (higher positions in business and management) but were also more likely to be disliked and not respected (Cooper, 2013). Thus, this would imply that there was a sexism bias against women in business roles because the stereotypical woman role does not fit the description of a successful business person. It is possible that the participants saw the white women as more feminine than the Asian women, and thus did not believe that they would do as well in a business situation. However, since we did not ask for ratings of femininity for the applicants, we do not truly know if this is the case here. It is also possible that the participants favored the Asian women more because they saw Asians as being more competent than white women. It is also interesting to see that the female participants preferred white women less than the male participants did, and since most of the participants were white, these women were discriminating against people of their same demographic. Thus, it is possible that the women participants felt threatened by the prospect of seeing successful women of their demographic and judged them more harshly. The same might have been true for the men too, as they ranked the white male applicants lower than the female participants did.
After receiving these results, I have many more questions about the hiring processes and how race and gender (of both applicant and hirer) can affect the results. I hope to be able to run several more iterations of the first survey itself, in order to get to a point where the results could potentially be generalized and to confirm whether or not racial prototypicality makes a difference for Asians in the job market. I also would like to include new studies by mixing gender and race; for example, how would the ratings compare for a white male vs. an Asian female? I hope to be able to answer these questions in the near future.
Deborah Wu ('16) studies Psychology and Music with a Concentration in Music Education. She is currently planning on pursuing a Ph.D. in social psychology and focusing her research on stereotyping and prejudice. Fun fact -- she has learned how to play 10 different musical instruments.ACKNOWLEDGEMENTS
The authors thank the Northwestern Office of Undergraduate Research for its financial support through the Academic Year Undergraduate Research Grant.
Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. The American Economic Review, 94(4), 991-1013.
Cooper, M. (2013). For women leaders: Likability and success hardly go hand-in-hand. Harvard Business Review.
Kaw, E. (1991). Medicalization of racial features: Asian American women and cosmetic surgery. Medical Anthropology Quarterly, 7(1), 74-89.
Kleider, H.M., Cavrak, S. E., & Knuycky, L. R. (2012). Looking like a criminal: Stereotypical Black facial features promote face categorization error. Memory & Cognition, 40(8), 1200-1213.
McConahay, J. B. (1983). Modern racism and modern discrimination: The effects of race, racial attitudes, and the context on simulated hiring decisions. Personality and Social Psychology Bulletin, 9, 551-558.
Strom, M.A., Zebrowitz, L.A., Zhang, S., Bronstad, P.M., & Lee, H.K. (2012). Skin and bones: The contribution of skin tone and facial structure to racial prototypicality ratings. PLoS ONE 7(7): e41193. doi:10.1371/journal.pone.0041193