  Motivated reasoning posits that people distort how they process information
in the direction of beliefs they find attractive. This paper creates a novel
experimental design to identify motivated reasoning from Bayesian updating when
people have preconceived beliefs. It analyzes how subjects assess the veracity
of information sources that tell them the median of their belief distribution
is too high or too low. Bayesians infer nothing about the source veracity, but
motivated beliefs are evoked. Evidence supports politically-motivated reasoning
about immigration, income mobility, crime, racial discrimination, gender,
climate change, and gun laws. Motivated reasoning helps explain belief biases,
polarization, and overconfidence.
