Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/260816 
Year of Publication: 
2022
Series/Report no.: 
CESifo Working Paper No. 9686
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
We propose a new method for measuring gender and ethnic stereotypes in news reports. By combining computer vision and natural language processing tools, the method allows us to analyze both images and text as well as the interaction between the two. We apply this approach to over 2 million web articles published in the New York Times and Fox News between 2000 and 2020. We find that in both outlets, men and whites are generally over-represented relative to their population share, while women and Hispanics are under-represented. We also document that news content perpetuates common stereotypes such as associating Blacks and Hispanics with low-skill jobs, crime, and poverty, and Asians with high-skill jobs and science. For jobs, we show that the relationship between visual representation and racial stereotypes holds even after controlling for the actual share of a group in a given occupation. Finally, we find that group representation in the news is influenced by the gender and ethnic identity of authors and editors.
Subjects: 
stereotypes
gender
race
media
computer vision
text analysis
JEL: 
L82
J15
J16
Z10
C45
Document Type: 
Working Paper
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