The Supply of Motivated Beliefs (New Draft: September 2023)
Summary: I run two experiments to study the role that motivated reasoning plays in the supply of false information. My main experiment, conducted using a large online sample of social media users, shows that incentivizing senders to be perceived as truthful causes them to send less truthful messages. Instead, these incentives lead senders to strategically misreport in order to appeal to receivers' current beliefs and their politically-motivated beliefs. However, receivers do not anticipate the adverse effects of senders' incentives. An additional experiment breaks the interaction between senders and receivers, showing more directly that beliefs about motivated reasoning are a driver of these effects.
(Study materials here)
Summary: We use experimental and empirical evidence to show that reaction to information depends on how strong the information is. Experimentally, we find novel evidence that people systematically overinfer from weak signals despite underinferring from strong signals. Greater task experience and cognitive sophistication are each associated with less overweighting of weak signals and less underweighting of strong signals. We also develop an empirical method to test over- vs. underreaction in observed prices across differing signal-strength environments, using time to resolution as an observable correlate of signal strength. Across betting and financial markets we find that when signals are weak, prices exhibit excess movement, indicating overreaction; when signals are strong, prices exhibit too little movement, indicating underreaction.
Summary: I experimentally test whether people engage in motivated reasoning towards believing "good news" or "bad news" about the world around them, and find a precisely-estimated and homogeneous null effect. Via a complementary survey, I show that respondents anticipate that good news makes people happier but are not necessarily beliefs that they are motivated to hold, suggesting that motivated reasoning is not primarily driven by belief-based happiness.
The Fake News Effect: Experimentally Identifying Motivated Reasoning Using Trust in News. American Economic Journal: Microeconomics, forthcoming.
Summary: I design a new experimental tool for portably identifying motivated reasoning from Bayesian updating, and use this tool to show that politics affects inference about factual questions on a broad range of topics: immigration, income mobility, crime, racial discrimination, gender, climate change, and gun laws. Politically-motivated reasoning is more severe for partisans and leads people to over-trust news more when it reinforces the biases in their beliefs.
(Online appendix and study materials here)
Polarization and Public Health: Partisan Differences in Social Distancing during the Coronavirus Pandemic (with Hunt Allcott, Levi Boxell, Jacob Conway, Matthew Gentzkow, and David Yang). Journal of Public Economics, 2020.
Summary: Using GPS data, we show that areas with more Republicans have engaged in less social distancing during COVID-19 than areas with more Democrats, and using a new survey, we show that Republican individuals self-report less social distancing and believe that the risk of COVID-19 is lower.
Gender Differences in Motivated Reasoning. Journal of Economic Behavior and Organization, 2021.
Summary: I show experimentally that there are statistically significant gender gaps in motivated reasoning about outperforming others, but that motivated reasoning about political issues is similar across genders. Motivated reasoning can help explain why there are gender gaps in overconfidence.
And Now for Something Completely Different
Tribone Tilings of Triangular Regions that Cover All but Three Holes. Discrete & Computational Geometry, 2015.
Summary: In my undergrad math thesis, I showed that there are a lot of ways to tile triangular regions with tribones when you leave three spaces uncovered.