DP16624 Visual Representation and Stereotypes in News Media

Author(s): Elliott Ash, Ruben Durante, Mariia Grebenshchikova, Carlo Schwarz
Publication Date: October 2021
Date Revised: May 2022
Keyword(s): Computer Vision, gender, media, race, Stereotypes, Text Analysis
JEL(s): C45, J15, J16, L82, Z1
Programme Areas: Public Economics, Political Economy
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=16624

We propose and validate a new method to measure gender and ethnic stereotypes in news reports, using computer vision tools to assess the gender, race and ethnicity of individuals depicted in article images. Applying this approach to 700,000 web articles published in the New York Times and Fox News between 2000 and 2020, we find that males and whites are overrepresented relative to their population share, while women and Hispanics are underrepresented. Relating images to text, we find 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. Analyzing news coverage of specific jobs, we show that racial stereotypes hold even after controlling for the actual share of a group in a given occupation. Finally, we document that group representation in the news is influenced by the gender and ethnic identity of authors and editors.