Gender Bias in the Job Market: a Longitudinal Analysis

Shiliang Tang
Xinyi Zhang
Jenna Cryan
Miriam Metzger
Haitao Zheng
Ben Y. Zhao

Proceedings of the 21st ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2018)

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Paper Abstract

For millions of workers, online job listings provide the first point of contact to potential employers. As a result, job listings and their word choices can significantly affect the makeup of the responding applicant pool. Here, we study the effects of potentially gender-biased terminology in job listings, and their impact on job applicants, using a large historical corpus of 17 million listings on LinkedIn spanning 10 years. We develop algorithms to detect and quantify gender bias, validate them using external tools, and use them to quantify job listing bias over time. We then perform a user survey over two user populations (N1=469, N2=273) to validate our findings and to quantify the end-to-end impact of such bias on applicant decisions. Our findings show gender-bias has decreased significantly over the last 10 years. More surprisingly, we find that impact of gender bias in listings is dwarfed by our respondents' inherent bias towards specific job types.