Understanding the structure of time preferences allows for accurate predictions of the effects of changing intertemporal incentives. Behavioral models of present bias are used to rationalize field data seemingly at odds with exponential discounting, leveraging additional degrees of freedom to improve in-sample fit. Largely lacking to date are the critical out-of-sample tests necessary to ensure predictive accuracy. This paper contrasts exponential discounting with present-biased procrastination for around 22,000 tax filers, advancing the literature in this domain by providing novel out-of-sample tests for both theories. Present bias provides qualitatively better in-sample fit, matching substantial increases in filing probability as the tax deadline approaches. Present bias also has improved out-of-sample predictive power for responsiveness to the 2008 Stimulus Act, and experimental data demonstrate a link between present bias and filing times. Without present bias, predicted responses to changed incentives are inaccurate, demonstrating its necessity in research and policy applications.
Martinez, S., Meier, S., & Sprenger, C. (2022). Procrastination in the Field: Evidence from Tax Filing. Journal of the European Economic Association, https://doi.org/10.1093/jeea/jvac067