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Implicit racial biases are lower in more populous more diverse and less segregated US cities

Author

Listed:
  • Andrew J. Stier

    (University of Chicago
    The Santa Fe Institute)

  • Sina Sajjadi

    (Complexity Science Hub
    Central European University)

  • Fariba Karimi

    (Complexity Science Hub
    Graz University of Technology)

  • Luís M. A. Bettencourt

    (University of Chicago
    University of Chicago)

  • Marc G. Berman

    (University of Chicago
    The University of Chicago Neuroscience Institute, University of Chicago)

Abstract

Implicit biases - differential attitudes towards members of distinct groups - are pervasive in human societies and create inequities across many aspects of life. Recent research has revealed that implicit biases are generally driven by social contexts, but not whether they are systematically influenced by the ways that humans self-organize in cities. We leverage complex system modeling in the framework of urban scaling theory to predict differences in these biases between cities. Our model links spatial scales from city-wide infrastructure to individual psychology to predict that cities that are more populous, more diverse, and less segregated are less biased. We find empirical support for these predictions in U.S. cities with Implicit Association Test data spanning a decade from 2.7 million individuals and U.S. Census demographic data. Additionally, we find that changes in cities’ social environments precede changes in implicit biases at short time-scales, but this relationship is bi-directional at longer time-scales. We conclude that the social organization of cities may influence the strength of these biases.

Suggested Citation

  • Andrew J. Stier & Sina Sajjadi & Fariba Karimi & Luís M. A. Bettencourt & Marc G. Berman, 2024. "Implicit racial biases are lower in more populous more diverse and less segregated US cities," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45013-8
    DOI: 10.1038/s41467-024-45013-8
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    References listed on IDEAS

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    1. Agustin Lage-Castellanos & Giancarlo Valente & Elia Formisano & Federico De Martino, 2019. "Methods for computing the maximum performance of computational models of fMRI responses," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-25, March.
    2. Jose Lobo & Luis MA Bettencourt & Michael E Smith & Scott Ortman, 2020. "Settlement scaling theory: Bridging the study of ancient and contemporary urban systems," Urban Studies, Urban Studies Journal Limited, vol. 57(4), pages 731-747, March.
    3. Jaap M J Murre & Joeri Dros, 2015. "Replication and Analysis of Ebbinghaus’ Forgetting Curve," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-23, July.
    4. Eun Lee & Fariba Karimi & Claudia Wagner & Hang-Hyun Jo & Markus Strohmaier & Mirta Galesic, 2019. "Homophily and minority-group size explain perception biases in social networks," Nature Human Behaviour, Nature, vol. 3(10), pages 1078-1087, October.
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