Not Learning from Others. With John Conlon, Gautam Rao, Matthew Ridley, Frank Schilbach.
Conditionally Accepted, The Journal of Political Economy
We study social learning using experiments where two people independently learn relevant information and can share it to make accurate private decisions. Across three experiments, people are substantially less sensitive to information others discover than to equally-relevant information they discovered themselves. This holds when they must learn information from others through discussion; when the experimenter perfectly communicates the information; and even when participants observe others' information with their own eyes. Our results therefore stem not from a failure to elicit information from others but a systematic tendency to underweight it relative to one's own information. Our findings illustrate a powerful barrier to social learning that might underlie many documented cases of failure to learn from others.
Learning in the Household. With John Conlon, Gautam Rao, Matthew Ridley, Frank Schilbach.
Do spouses pool useful information and learn from each other when they have incentives to do so? In an experiment with married couples in India, we vary whether individuals discover information themselves or must instead learn via a discussion what their spouse discovered. Women treat their own and their husband's information the same. In contrast, men respond half as much to information discovered by their wife, even when it is perfectly communicated. When paired with strangers, both men and women heavily discount their partner's information relative to their own. We thus provide evidence of a gender difference in social learning (only) in the household.
Wading Through Sludge: Evidence from Title IX Reporting Systems. With Michael J. Challis. [Draft available upon request.]
We study how administrative sludge in Title IX reporting systems affects both the number of reports and the composition of reports. Using a new panel for all college campuses in New York (2018 - 2024) and Texas (2019 - 2024), we collect and scrape all elements of the sexual violence reporting process, extract textual and structural features with natural language processing, and build interpretable design indices via factor analysis. We find in our cross-sectional specification that procedurally dense, legalistic landing pages are associated with fewer incident reports but higher formalization conditional on reporting, consistent with entry-margin screening; these associations largely vanish with institution fixed effects. By contrast, within institutions, more narrative questions in reporting forms sharply reduces conversion from reports to formal complaints and processed cases.
Sludge and Sexual Violence Reporting: Evidence from New York and Texas. With Michael J. Challis and George Ferridge.
Dating: A Markov Process? With Michael J. Challis.
Evidence and Policymaking: A Behavioral Perspective. With Pieter Serneels and Mattie Toma.