Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/305687 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 17245
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
We audit the job recommender algorithms used by four Chinese job boards by creating fictitious applicant profiles that differ only in their gender. Jobs recommended uniquely to the male and female profiles in a pair differ modestly in their observed characteristics, with female jobs advertising lower wages, requesting less experience, and coming from smaller firms. Much larger differences are observed in these ads' language, however, with women's jobs containing 0.58 standard deviations more stereotypically female content than men's. Using our experimental design, we can conclude that these gender gaps are generated primarily by content-based matching algorithms that use the worker's declared gender as a direct input. Action-based processes like item-based collaborative filtering and recruiters' reactions to workers' resumes contribute little to these gaps.
Schlagwörter: 
recommender system
algorithm
gender
job platform
JEL: 
C93
J71
J16
O33
M50
Dokumentart: 
Working Paper

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