Attribution Bias in Major Decisions: Evidence from the United States Military Academy (May 2018)
Kareem Haggag, Richard W. Patterson, Nolan G. Pope, Aaron Feudo
Abstract: 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 to that course. To do so, we exploit the conditional random assignment of student course schedules at the United States Military Academy. We find that students who are assigned to an early morning (7:30 AM) section of a general education course are roughly 10% less likely to major in that subject, relative to students assigned to a later time slot for the course. We find similar effects for fatigue generated by having one or more back-to-back courses immediately prior to a general education course that starts later in the day. Finally, we demonstrate that the pattern of results is consistent with attribution bias and difficult to reconcile with competing explanations.
Attribution Bias in Consumer Choice (May 2018)
Kareem Haggag, Devin G. Pope, Kinsey B. Bryant-Lees, Maarten W. Bos
Forthcoming at Review of Economic Studies
Abstract: When judging the value of a good, people may be overly influenced by the state in which they previously consumed it. For example, someone who tries out a new restaurant while very hungry may subsequently rate it as high quality, even if the food is mediocre. We produce a simple framework for this form of attribution bias that embeds a standard model of decision making as a special case. We test for attribution bias across two consumer decisions. First, we conduct an experiment in which we randomly manipulate the thirst of participants prior to consuming a new drink. Second, using data from thousands of amusement park visitors, we explore how pleasant weather during their most recent trip affects their stated and actual likelihood of returning. In both of these domains, we find evidence that people misattribute the influence of a temporary state to a stable quality of the consumption good. We provide evidence against several alternative accounts for our findings and discuss the broader implications of attribution bias in economic decision making.
Learning by Driving: Productivity Improvements by New York City Taxi Drivers (January 2017)
Kareem Haggag, Brian McManus, Giovanni Paci
American Economic Journal: Applied Economics, 9(1), 70-95. 2017
Abstract: 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 our data’s breadth to investigate the factors that contribute to driver improvement across a variety of situations. New drivers lag farther behind experienced drivers when in difficult situations. Drivers benefit from accumulating neighborhood-specific experience, which affects how they search for their next customers.
Default Tips (April 2014)
Kareem Haggag, Giovanni Paci
American Economic Journal: Applied Economics, 6(3), 1-19. 2014
Abstract: We examine the role of defaults in high-frequency, small-scale choices using unique data on over 13 million NYC taxi rides. We exploit a shift in the set of default tip suggestions presented to customers prior to payment, as the base fare changes from below $15 to above $15. Using a regression discontinuity design, we show that default suggestions have a large impact on tip amounts. These results are supported by a secondary analysis that uses the quasi-random assignment of customers to different cars to examine default effects on all fares above $15. Finally, we highlight a potential cost of setting defaults too high, as a higher proportion of customers opt to leave no credit card tip when presented with the higher suggested amounts.
Selected Work in Progress
Inaccurate Statistical Discrimination
J. Aislinn Bohren, Kareem Haggag, Alex Imas, Devin Pope