This paper explores a group rule–utilitarian approach to understanding voter turnout, inspired by the theoretical work of John C. Harsanyi (1980) and Timothy J. Feddersen and Alvaro Sandroni (2002). It develops a model based on this approach and studies its performance in explaining turnout in Texas liquor referenda. The results are encouraging: the comparative static predictions of the model are broadly consistent with the data, and a structurally estimated version of the model yields reasonable coefficient estimates and fits the data well. The structurally estimated model also outperforms a simple expressive voting model.

Evidence suggests that people understand qualitatively how tastes change over time, but underestimate the magnitudes. This evidence is limited, however, to laboratory evidence or surveys of reported happiness. We test for such projection bias in field data. Using data on catalog orders of cold-weather items, we find evidence of projection bias over the weather—specifically, people's decisions are overinfluenced by the current weather. Our estimates suggest that if the order-date temperature declines by 30°F, the return probability increases by 3.95 percent. We also estimate a structural model to measure the magnitude of the bias.