Sincere or motivated? Partisan bias in advice-taking
Author(s)
Zhang, Yunhao; Rand, David G.
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Political divisions have become a central feature of modern life. Here, we ask whether these divisions affect advice-taking from co- and counter-partisans in a nonpolitical context. In an incentivized task assessing the accuracy of nonpolitical news headlines, we find partisan bias in advice-taking: Democratic participants are less swayed by (accurate) information that comes from Republicans compared to the same information from Democrats (Republican participants display no such bias). We then adjudicate between two possible mechanisms for this biased advice-taking: a preference-based account, where participants are motivated to take less advice from counter-partisans because doing so is unpleasant; versus a belief-based account, where participants sincerely believe co-partisans are more competent at the task (even though this belief is incorrect). To do so, we examine the impact of a substantial increase in the stakes, which should increase accuracy motivations (and thereby reduce the relative impact of partisan motivations). We find that increasing the stakes does not reduce biased advice-taking, hence no evidence to support the bias is driven by preference. Consistent with the belief-based account, we find that Democratic participants (incorrectly) believe their co-partisans are better at the task, and this incorrect belief is much less severe among Republican participants. Further supporting the notion that the stated beliefs are sincere, raising the stakes of the belief elicitation of relative partisan competence does not affect the stated beliefs. Finally, participants—instead of ignoring the feedback—actually substantially update in favor of their counter-partisans given feedback that suggests counter-partisans are competent.
Date issued
2023Department
Sloan School of ManagementPublisher
Cambridge University Press
Citation
Zhang Y, Rand DG. Sincere or motivated? Partisan bias in advice-taking. Judgment and Decision Making. 2023;18:e29.
Version: Final published version
ISSN
1930-2975
Keywords
Economics and Econometrics, Applied Psychology, General Decision Sciences
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