The Racialized and Gendered Workplace: Applying an Intersectional Lens to a Field Experiment on Hiring Discrimination in Five European Labor Markets
Employment chances of minority applicants depend on how well they are perceived to align with the feminine or masculine traits of the job. While white women are strongly preferred for female-type jobs, women of color are not given a similar advantage.
Gender, racial and ethnic stereotypes continue to be obstacles for racial minority groups when applying to a job position. Hiring discrimination fosters economic inequality for both women and racial and ethnic minorities since these groups have to overcome the additional obstacle of bias in the labor market in order to secure an income.
Intersectionality theory states that social identities are embedded in culturally specific systems of privilege and power. Rather than seeing misogyny, homophobia, racism and other systems of oppression as independent from one another, intersectionality claims these systems are interlocked and the interactions and overlaps should be closely examined. When analyzing hiring discrimination, an intersectional lens turns into a critical tool to understand how different and overlapping identities might affect a person’s prospects to be hired, and what type of jobs they are most likely to be called for.
In this large-scale simultaneous field experiment in five European countries, the authors analyzed how employers evaluate applicants with intersecting group membership when making hiring decisions, focusing on job positions that are frequently gendered, namely: store assistants, receptionists, payroll clerks, cooks, software developers and marketing/sales representatives. The authors hypothesized that the gendered nature of racial discrimination would differ across minority groups and labor market segment, depending on the existing stereotypes of specific gender-race subgroups.
Employment chances of applicants of different gender and racial backgrounds depend on how well they are perceived to align with the feminine or masculine traits of the job they apply to.
- Racial minorities experienced significant discrimination in the labor market. Black and MENAP minorities (people originating from regions such as the Middle East, North Africa, and Pakistan) were 11.8 – 11.9% less likely to be called back, as compared to their white counterparts. Asians and white minorities were 3.7 and 6.6% less likely to be called back, respectively.
- The discrimination that each racial group faced was consistent across genders.
- White candidates, regardless of immigrant background, are rewarded when applying to gender-congruent jobs—that is, jobs whose stereotyped gender matched their own—especially in female-dominated occupations.
- For instance, white women applying to a female-stereotyped job were more likely to receive callbacks.
- In gendered-balanced or male-dominated occupations, Black and Middle Eastern candidates of both genders faced discrimination.
- Black and MENAP men faced the strongest discrimination when applying to male-type jobs - likely due to the male-stereotyped occupational context making their stereotyped masculinity be perceived as threatening.
The study consisted of a set of large-scale simultaneous field experiments in Great Britain, Germany, the Netherlands, Norway and Spain. The research team sent more than 19,000 job applications in six core occupations: store assistants, receptionists, payroll clerks, cooks, software developers and marketing/sales representatives. All applicants were qualified for the jobs they applied to and had four years of uninterrupted work experience, and their ages ranged from 22 to 26.
Proxied by the applicant´s countries of origin, ethnicity was communicated through names and reinforced through a statement in the cover letter and the language skills mentioned in the resume. The design was factorial, as several characteristics were simultaneously varied across applications, including the gender and race of the applicant.
After the compilation of data, the authors used both linear and logistic regression models predicting callbacks based on ethnicity and gender.