We examine the role of defaults in high-frequency, small-scale choices using unique data on over 13 million New York City taxi rides. Using a regression discontinuity design, we show that default tip suggestions have a large impact on tip amounts. These results are supported by a secondary...

We study learning by doing (LBD) by New York City taxi drivers, who have substantial discretion over their driving strategies and receive compensation closely tied to their success in finding customers. In addition to documenting significant learning by these entrepreneurial agents, we exploit...

Using administrative data, we study the role of attribution bias in a high-stakes, consequential decision: the choice of a college major. Specifically, we examine the influence of fatigue experienced during exposure to a general education course on whether students choose the major corresponding...

Using administrative data, we study the role of attribution bias in a high-stakes, consequential decision: the choice of a college major. Specifically, we examine the influence of fatigue experienced during exposure to a general education course on whether students choose the major corresponding...

Using administrative data, we study the role of attribution bias in a high-stakes, consequential decision: the choice of a college major. Specifically, we examine the influence of fatigue experienced during exposure to a general education course on whether students choose the major corresponding...