12:00 horas vía Zoom.
Invitado: Daniel Schwartz, académico de Ingeniería Industrial, U. de Chile.
Tema: “The Rise of a Nudge: Field Experiment and Machine Learning on Minimum and Full Credit Card Payments”
The minimum payment warning, a notice that informs credit cardholders of the downside of making the minimum payment, has been described as a perverse nudge because it negatively affects those who would pay more than the minimum, presumably due to the anchoring bias. This issue is tackled in a massive field experiment by introducing a new “statement balance warning” that informs the interest charges for paying less than the statement balance. The experiment used email payment reminders that randomly added a minimum payment and statement balance warnings. Results indicate that the messages shifted actual payment distribution depending on the warning, and adding a statement balance warning significantly increased payments. The analysis is combined with causal random forests, a novel machine learning technique, to find heterogeneous treatment effects, underlying mechanisms, and the optimum policy in different scenarios, and with an online experiment to further examine conditions in which the warnings affect payment behavior. The statement balance warning significantly increased payments by cardholders who usually partially repay their balance, are more likely to make deliberate decisions every month, and understand less the consequences of paying less than the statement balance. The optimum policy to decrease interest charges or debt delinquency indicates that cardholders should receive a combination of warning messages depending on their payment history.
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