Working Papers

Moved to Vote: The Long-Run Effects of Neighborhoods on Political Participation (November 2019)
Eric Chyn, Kareem Haggag

Abstract: How does one’s childhood neighborhood shape political engagement later in life? We leverage a natural experiment that moved children out of disadvantaged neighborhoods to study effects on their voting behavior more than a decade later. Using linked administrative data, we find that children who were displaced by public housing demolitions and moved using housing vouchers are 12 percent (3.3 percentage points) more likely to vote in adulthood, relative to their non-displaced peers. We argue that this result is unlikely to be driven by changes in incarceration or in their parents’ outcomes, but rather by improvements in education and labor market outcomes, and perhaps by socialization. These results suggest that, in addition to reducing economic inequality, housing assistance programs that improve one’s childhood neighborhood may be a useful tool in reducing inequality in political participation.

Racial Disparities in Voting Wait Times: Evidence from Smartphone Data (November 2019)
M. Keith Chen, Kareem Haggag, Devin G. Pope, Ryne Rohla

Abstract: Equal access to voting is a core feature of democratic government. Using data from millions of smartphone users, we quantify a racial disparity in voting wait times across a nationwide sample of polling places during the 2016 U.S. presidential election. Relative to entirely-white neighborhoods, residents of entirely-black neighborhoods waited 29% longer to vote and were 74% more likely to spend more than 30 minutes at their polling place. This disparity holds when comparing predominantly white and black polling places within the same states and counties, and survives numerous robustness and placebo tests. We shed light on the mechanism for these results and discuss how geospatial data can be an effective tool to both measure and monitor these disparities going forward.

Press: Washington Post Op-Ed, Scientific American, The Root, The Hill

Attribution Bias in Major Decisions: Evidence from the United States Military Academy (June 2019)
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.

Press: NPR Morning Edition (Hidden Brain)

Inaccurate Statistical Discrimination (May 2019)
J. Aislinn Bohren, Kareem Haggag, Alex Imas, Devin G. Pope

Abstract: Discrimination has been widely studied in economics and other disciplines. In addition to identifying evidence of discrimination, economists often categorize the source of discrimination as either taste-based or statistical. Categorizing discrimination in this way can be valuable for policy design and welfare analysis. We argue that a further categorization is important and needed. Specifically, in many situations economic agents may have inaccurate beliefs about the expected productivity or performance of a social group. This motivates our proposed distinction between accurate (based on correct beliefs) and inaccurate (based on incorrect beliefs) statistical discrimination. We do a thorough review of the discrimination literature and argue that this distinction is rarely discussed. Using an online experiment, we illustrate how to identify accurate versus inaccurate statistical discrimination. We show that ignoring this distinction – as is often the case in the discrimination literature – can lead to erroneous interpretations of the motives and implications of discriminatory behavior. In particular, when not explicitly accounted for, inaccurate statistical discrimination can be mistaken for taste-based discrimination, accurate statistical discrimination, or a combination of the two.

Press: Freakonomics

Publications

Attribution Bias in Consumer Choice
Kareem Haggag, Devin G. Pope, Kinsey B. Bryant-Lees, Maarten W. Bos
Review of Economic Studies, 86(5), 2136-2183. 2019

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
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
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.

Editors’ Choice: Science Magazine, Vol 345(6203)
Press: Bloomberg, NPR Morning Edition, Yahoo! Finance, Pacific Standard Magazine, Guardian, New York Times, Boston Globe
[Online Appendix]