Data and Code: The Fake News Effect: Experimentally Identifying Motivated Reasoning Using Trust in News
Principal Investigator(s): View help for Principal Investigator(s) Michael Thaler, University College London
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
|---|---|---|---|
| fake-news-effect_code | 05/28/2023 09:02:PM |
Project Citation:
Thaler, Michael. Data and Code: The Fake News Effect: Experimentally Identifying Motivated Reasoning Using Trust in News. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-03-29. https://doi.org/10.3886/E183845V1
Project Description
Summary:
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These files provide the data and code used for the analysis in The Fake News Effect: Experimentally Identifying Motivated Reasoning Using Trust in News. The abstract is below:
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.
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.
Funding Sources:
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Eric M. Mindich Research Fund for the Foundations of Human Behavior;
Harvard University. Harvard Business School. Division of Research
Scope of Project
Subject Terms:
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Motivated reasoning;
Biased beliefs;
Polarization;
Fake news
JEL Classification:
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C91 Design of Experiments: Laboratory, Individual
D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
D84 Expectations; Speculations
D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
L82 Entertainment; Media
C91 Design of Experiments: Laboratory, Individual
D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
D84 Expectations; Speculations
D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
L82 Entertainment; Media
Geographic Coverage:
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United States
Time Period(s):
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2018 – 2019
Collection Date(s):
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2018 – 2019
Data Type(s):
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experimental data
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