Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/266005 
Year of Publication: 
2022
Series/Report no.: 
CESifo Working Paper No. 9970
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
Women are significantly underrepresented in the technology sector. We design a field experiment to identify statistical discrimination in job applicant assessments and test treatments to help improve hiring of the best applicants. In our experiment, we measure the programming skills of job applicants for a programming job. Then, we recruit a sample of employers consisting of human resource and tech professionals and incentivize them to assess the performance of these applicants based on their resumes. We find evidence consistent with inaccurate statistical discrimination: while there are no significant gender differences in performance, employers believe that female programmers perform worse than male programmers. This belief is strongest among female employers, who are more prone to selection neglect than male employers. We also find experimental evidence that statistical discrimination can be mitigated. In two treatments, in which we provide assessors with additional information on the applicants' aptitude or personality, we find no gender differences in the perceived applicant performance. Together, these findings show the malleability of statistical discrimination and provide levers to improve hiring and reduce gender imbalance.
Subjects: 
field experiment
discrimination
beliefs
gender
JEL: 
C93
J23
J71
J78
Document Type: 
Working Paper
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