I'm an assistant professor of economics at the University of Arkansas specializing in behavioral and experimental economics. I use laboratory, field, and online experiments to study questions about education and public policy.
I'm originally from Kansas and received my bachelor's degree from Kansas State University. Before joining the faculty at the University of Arkansas, I studied economics at UC San Diego.
( 01 )
Grants, Awards & Fellowships
Walton College Excellence in Research Award
PEDL Major Research Grant
(Co-PI with Sarojini Hirshleifer and Arman Rezaee)
Employer beliefs, employee training, and labor market outcomes: A field experiment in Uganda ($452,000)
J-PAL Post-Primary Education Initiative
(Co-PI with Sarojini Hirshleifer and Arman Rezaee)
Employer beliefs, employee training, and labor market outcomes: A field experiment in Uganda ($16,660)
Robert Wood Johnson Foundation
(Co-PI with Alex Imas and Michael Kuhn)
Examining the impact of waiting periods on improving the use of food subsidies for healthier consumption while maintaining choice ($198,940)
Laura and John Arnold Foundation
(Co-PI with Sally Sadoff)
Improving Community College Outcomes through Performance Incentives ($312,000)
(Co-PI with Sally Sadoff)
Improving Community College Outcomes through Performance Incentives ($25,000)
Russell Sage Foundation Small Grant in Behavioral Economics
(Co-PI with Tristan Gagnon-Bartsch and Shengwu Li)
On the Elicitation of Willingness to Pay for Stigmatized Goods ($4,700)
( 02 )
University of California, San Diego
PhD in Economics (2015)
Advisor: James Andreoni
Kansas State University
BA in Economics and Mathematics (2010)
Minors in Spanish and Statistics
( 03 )
University of Arkansas
Industrial Organization I: Graduate course in applied microeconomic theory and industrial organization.
Economics of Organizations: Undergraduate course in managerial economics, game theory, and industrial organization.
University of California, San Diego
Teaching assistant for Game Theory, MBA Strategy, and Intermediate Microeconomics.
( 04 )
Improving College Instruction through Incentives (with Sally Sadoff)
Journal of Political Economy (Forthcoming) [Link]
Prior work demonstrates the importance of college instructor quality, but little is known about whether college instruction can be improved. In a field experiment, we examine the impact of performance-based incentives for community college instructors. We estimate that instructor incentives improve student exam scores by 0.16-0.2 standard deviations (SD), increase course grades by 0.1 SD, reduce course dropout by 17 percent, and increase credit accumulation by 18 percent. The effects are largest among part-time adjunct instructors. During the program, instructor incentives have large positive spillovers to students' unincentivized courses, significantly increasing completion rates and grades in courses outside our study. One year after the program ends, instructor incentives increase transfer rates to four-year colleges by an estimated 22-28 percent, with no impact on two-year college degrees. To test for potential complementarities, we examine the impact of instructor incentives in conjunction with student incentives and find no evidence that the incentives are more effective in combination. Finally, we elicit contract preferences for the loss-framed incentives we offer. At baseline, instructors prefer gain-framed incentives. However, after experiencing loss-framed incentives, instructors significantly increase their preferences for them.
Understanding Outcome Bias (with Michael Kuhn)
Games and Economic Behavior (2019) [Link]
Disentangling effort and luck is critical when judging performance. In a principal-agent experiment, we demonstrate that principals' judgments of agent effort are biased by luck, despite perfectly observing the agent's effort. We find that two potential solutions to this "outcome bias"—the opportunity to avoid irrelevant information about luck, and outsourcing judgment to independent third parties—are ineffective. When we give control over information about luck to principals and agents in separate treatments, we find asymmetric sophistication: agents strategically manipulate principals' outcome bias, but principals fail to recognize their own bias. Independent third parties are just as biased as principals. These findings indicate that the scope of outcome bias may be larger than previously understood and that outcome bias cannot be driven solely by emotional responses nor distributional preferences. Instead, we hypothesize that luck directly affects beliefs, and we test this hypothesis by eliciting the beliefs of third parties and principals. Lucky agents are believed to exert more effort than identical, unlucky agents. We propose a model of biased belief updating explaining these results.
Social Desirability Bias and Polling Errors in the 2016 Presidential Election (with Aaron Novotny)
Journal of Behavioral and Experimental Economics (2018) [Link]
Social scientists have observed that socially desirable responding (SDR) often biases unincentivized surveys. Nonetheless, media, campaigns, and markets all employ unincentivized polls to make predictions about electoral outcomes. During the 2016 presidential campaign, we conducted three list experiments to test the effect SDR has on polls of agreement with presidential candidates. We elicit a subject's agreement with either Hillary Clinton or Donald Trump using explicit questioning or an implicit elicitation that allows subjects to conceal their individual responses. We find evidence that explicit polling overstates agreement with Clinton relative to Trump. Subgroup analysis by party identification shows that SDR significantly diminishes explicit statements of agreement with the opposing party's candidate, driven largely by Democrats who are significantly less likely to explicitly state agreement with Trump. We measure economic policy preferences and find no evidence that ideological agreement drives SDR. We find suggestive evidence that local voting patterns predict SDR.
Press: [Press Release] [KUAF (NPR)] [KNWA 24] [KARK 4]
A Classroom Experiment on Effort Allocation under Relative Grading
Economics of Education Review (2018) [Link]
Grading on the curve is a form of relative evaluation similar to an all-pay auction or rank-order tournament. The distribution of students drawn into the class from the population is predictably linked to the size of the class. Increasing the class size draws students' percentile ranks closer to their population percentiles. Since grades are awarded based on percentile ranks in the class, this reallocates incentives for effort between students with different abilities. The predicted aggregate effort and the predicted effort from high-ability students increases while the predicted effort from low-ability students decreases. Andreoni and Brownback (2017) find that the size of a contest has a causal impact on the aggregate effort from participants and the distribution of effort among heterogeneous agents. In this paper, I randomly assign "class sizes" to quizzes in an economics course to test these predictions in a real-stakes environment. My within-subjects design controls for student, classroom, and time confounds and finds that the lower variance of larger classes elicits greater effort from all but the lowest-ability students, significantly increasing aggregate effort.
All-Pay Auctions and Group Size: Grading on the Curve and Other Applications (with James Andreoni)
Journal of Economic Behavior and Organization (2017) [Link]
We model contests with a fixed proportion of prizes, such as a grading curve, as all-pay auctions where higher effort weakly increases the likelihood of a prize. We find theoretical predictions for the heterogeneous effect auction size has on effort from high- and low-types. We test our predictions in a laboratory experiment that compares behavior in two-bidder, one-prize auctions with behavior in 20-bidder, 10-prize auctions. We find a statistically significant 11.8% increase in aggregate bidding when moving from the small to large auction. The impact is heterogeneous: as the auction size increases, low-types decrease effort but high-types increase effort. Additionally, the larger auction provides a stronger rank-correlation between effort and ability, awarding more prizes to the higher-skilled and improving the efficiency of prize allocation.
Works In Progress
Behavioral Food Subsidies (with Alex Imas & Michael Kuhn)
We examine the potential of healthy food subsidies for reducing nutritional inequality through demand-side interventions. Using a pre-registered field experiment with low-income grocery shoppers, we show that low-cost, scalable behavioral interventions make subsidies substantially more effective. Our unique design allows us to elicit choices and deliver subsidies both before and during a shopping trip. We examine two novel interventions: giving shoppers greater agency through a choice between subsidies and introducing waiting periods designed to prompt deliberation about food purchases. The interventions increase healthy purchases by 61% relative to choiceless healthy subsidies, and 199% relative to a control group.
The Impact of Summer School on Community College Student Success (with Sally Sadoff)
We examine whether summer school is a missed opportunity for colleges to accelerate completion. We randomly assign summer scholarships to community college students and link their educational outcomes to their preferences for the scholarships. The scholarships have a large impact on degree acceleration, increasing graduation within one year of the intervention by 32% and transfers to four-year colleges by over 50%. Treatment effects are concentrated among students with a preference against summer school. Our results suggest that educational impacts do not drive enrollment preferences. And, that many more students could benefit from summer school than the small minority who currently enroll.
Predicting Biased Polls (with Nathaniel Burke and Tristan Gagnon-Bartsch)
Socially desirable responding (SDR) is a well-documented phenomenon in which poll respondents strategically conceal stigmatized behaviors or preferences in order to present a positive image. This paper examines whether observers of polling information anticipate this bias and are sophisticated enough to correct for it. We elicit information from unincentivized polls about behaviors with varying degrees of social desirability. We simultaneously elicit incentivized, revealed preferences for the same behaviors. Comparing these elicitations, we identify bias from SDR that is well-predicted by independent surveys about the social desirability of each action. We then recruit predictors to guess the incentivized choice behavior. Predictors are shown a sample of responses from either (i) the incentivized choice group itself or (ii) the unincentivized group. Predictors correctly discount unincentivized information, and their prediction accuracy is higher than a fully-naive approach that takes unincentivized information at face value. However, they show no sophistication in predicting the direction or magnitude of the bias from SDR and, thus, fail to “de-bias” their signals.
Increasing Access to Training, Capital, and Networks: Two Planned Field Experiments with Small Firms in Uganda (with Sarojini Hirshleifer, Arman Rezaee, & Benjamin Kachero)
Time-Preferences and Grocery Purchases (with Alex Imas & Michael Kuhn)
On the Elicitation of Willingness to Pay for Stigmatized Goods
(with Tristan Gagnon-Bartsch & Shengwu Li)