Abstract
Motivated reasoning posits that people distort how they process new information in the direction of beliefs they find more attractive. This paper introduces a novel experimental paradigm that is able to portably identify motivated reasoning from Bayesian updating across a variety of factual questions; the paradigm analyzes how subjects assess the veracity of information sources that tell them the median of their belief distribution is too high or too low. A Bayesian would infer nothing about the source veracity from this message, but motivated reasoners would infer that the source were more truthful if it reported the direction that they find more attractive. I find novel evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, gun laws, and the performance of other subjects. Motivated reasoning from messages on these topics leads people’s beliefs to become more polarized, even though the messages are uninformative.
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