2023 SPSSI Conference:
Anti-Muslim Policy as a Cue for Individual Implicit Anti-Muslim Bias

Aeleah M. Granger, Portland State University
Kimberly Barsamian Kahn, Portland State University

In 2016, the world watched as then-candidate Donald Trump espoused bold anti-Muslim stances. After his inauguration, these words transformed into Executive Order 13769, more commonly known as the “Muslim ban.” The impact was immediate: travelers were detained, families were separated, legal battles against the ban ensued, and airports across the U.S. became grounds for large-scale protests against the ban. During this time, the FBI was also tracking a surge in anti-Muslim and anti-Arab hate crimes – a spike eerily reminiscent of the aftermath of the 9/11 attacks – which has since been linked with upticks in Islamophobic rhetoric online (Müller & Schwarz, 2023).

Such conspicuous hate crimes, while deeply concerning, represent the tip of the iceberg. Beneath this visible surface, there exists another realm of bias: implicit biases. Unlike their explicit counterparts, implicit biases operate in the background, manifesting in our actions and attitudes without our conscious awareness. Shaped by both individual and contextual influences, studies have long documented how different contexts can modulate implicit biases (e.g., Payne et al., 2019). Public policy changes, like legalizing same-sex marriage, nudge these covert biases in significant ways (Ofosu et al., 2019). This suggests that different contexts have different levels of pre-existing bias which then reciprocally influence implicit attitudes. Still, more research is needed on the interplay between contextual- and individual-level anti-Muslim bias, specifically.

To examine how widespread anti-Muslim policy influences individual anti-Muslim implicit bias, our study utilized Implicit Association Test (IAT) data from Harvard’s Project Implicit (2012-2022; N = 331,583; Xu et al., 2022), state-level Presidential election data (2012-2020; MIT Election Lab, 2020), and Muslim ban activity data (2017-2021; ACLU-Washington, 2020), including major public discussions of the ban, ban implementation, and major court actions against the ban. Since people are influenced by both their personal, pre-existing beliefs and their surrounding environment, this study examined how the Muslim ban influenced individual implicit bias across different states and among conservative and liberal individuals. In simpler terms: Did the Muslim ban alter automatic anti-Muslim prejudices in the U.S. differently for conservatives and liberals?

Somewhat surprisingly, instead of amplifying anti-Muslim implicit bias, Muslim ban activity was associated with reduced anti-Muslim implicit bias compared to before and after the ban. This reduction, however, was nuanced and depended on participant political orientation. Those who were very conservative showed both more anti-Muslim implicit bias overall and increased implicit anti-Muslim bias during Muslim ban periods, perhaps as a result of differing group norms among conservatives and liberals.

Still, why might so many people have responded to the Muslim ban with less anti-Muslim bias? One possibility is the backlash effect. Oftentimes, overtly discriminatory policies push many to challenge their internalized biases, especially in a nation that proudly champions religious freedom (Collingwood et al., 2018). This may reflect a self-correcting mechanism for discriminatory policies. Although more research is needed to understand these differing responses to the same Islamophobic policy, this study illuminates a pivotal insight: The interplay between individual and contextual biases. If discriminatory policies like the Muslim ban can stir reactionary automatic shifts in bias, imagine the power of deliberate, positive policy change.

References

? American Civil Liberties Union Washington. (2020, February 10). Timeline of the Muslim Ban. American Civil Liberties Union of Washington. Retrieved October 21, 2022, from https://www.aclu-wa.org/pages/timeline-muslim-ban 

Collingwood, L., Lajevardi, N., & Oskooii, K. A. R. (2018). A change of heart? Why individual-level public opinion shifted against Trump’s “Muslim Ban.” Political Behavior, 40(4), 1035–1072. https://doi.org/https://doi.org/10.1007/s11109-017-9439-z

MIT Election Data and Science Lab, (2020). U.S. President 1976-2020 (Harvard Dataverse; Version V6) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/42MVDX

Müller, K., & Schwarz, C. (2023). From hashtag to hate crime: Twitter and antiminority sentiment. American Economic Journal: Applied Economics, 15(3), 270-312.

Ofosu, E. K., Chambers, M. K., Chen, J. M., & Hehman, E. (2019). Same-sex marriage legalization associated with reduced implicit and explicit antigay bias. Proceedings of the National Academy of Sciences, 116(18), 8846–8851. https://doi.org/https://doi.org/10.1073/pnas.1806000116

Payne, B. K., Vuletich, H. A., & Brown-Iannuzzi, J. L. (2019). Historical roots of implicit bias in slavery. Proceedings of the National Academy of Sciences, 116(24), 11693–11698. https://doi.org/https://doi.org/10.1073/pnas.1818816116

Xu, K., Nosek, B. A., Greenwald, A. G., Ratliff, K. A., Bar-Anan, Y., Umansky, E., … Frost, N. (2022, May 17). Project Implicit Demo Website Datasets. https://doi.org/10.17605/OSF.IO/Y9HIQ

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