Information Inequality in College Major Choice (job market paper)

I study disparities in college major choices across students from different socioeconomic backgrounds and analyze their implications for intergenerational income mobility. One potential explanation for these disparities is differential access to information about majors' academic content and personal fit. To explore the role of information frictions on major choices, I use administrative data from the centralized college application system in China. Consistent with the information inequality hypothesis, I document that students of low socioeconomic status (SES) are 21.6% (3.16 percentage points) more likely than their high-SES peers to choose majors that are familiar to them from their high school curricula. Further support for the information inequality hypothesis comes from a survey experiment in which high school students report their expectations about college majors and from information spillovers among high school classmates. To discuss the economic consequences, I calibrate a model of major choice and find that, because of information inequality, low-SES students face higher mismatch rates and lower future incomes than their high-SES peers. Counterfactual analyses indicate that information interventions and affirmative action policies can effectively narrow the income gap across socioeconomic backgrounds.

Heuristics in Self-Evaluation: Evidence from the Centralized College Admission System in China (joint with Hongbin Li)

Forthcoming, the Review of Economics and Statistics

Using administrative data on the Chinese National College Entrance Examination, we study how left-digit bias affects college applications. We find strong discontinuities in students' admission outcomes at 10-point thresholds. Students with scores just below multiples of 10 make more conservative college application choices that place them into less selective colleges and majors. In contrast, students who score at or just above multiples of 10 aim and achieve higher but are at greater risk of overshooting. The discontinuity reveals that, despite the educational and labor market consequences, students' self-evaluation based on exam scores is subject to information processing heuristics.

Family Spillover Effects of Marginal Diagnoses: The Case of ADHD (joint with Petra Persson and Maya Rossin-Slater)

Conditionally Accepted, the American Economic Journal: Applied Economics

The health care system uses patient family medical history in many settings, and this practice is widely believed to improve the efficiency of health care allocation. This paper provides a counterpoint by documenting that reliance on hereditary information can amplify the misallocation of low-value care. We study Attention Deficit Hyperactivity Disorder, and show that reliance on family medical history generates a "snowball effect"—the propagation of an original marginal diagnosis to a patient's relatives. This snowball effect raises the private and social costs of low-value care.


The Gender Gap in Small Business Performance: Evidence from a Food Ordering Platform (joint with Carol Lu)

Presented at the ASSA/AEA 2022 Annual Meeting

In this paper, we examine the gender gap in business performance among freelancers who sell home-cooked meals—a field traditionally perceived as female-typed and requiring complex business strategies. Using high-quality proprietary data from an online food ordering platform, we document a 12% gender gap in sellers' daily revenues. Factors highlighted in the existing literature such as working hours and experience narrow the gap by 63%. The remaining gap can be explained by male and female freelancers adopting different business strategies in pricing, positioning, and market expansion.

Firm Pricing Strategy and Consumer Dishonesty: Evidence from the Bike-Sharing Industry (joint with Guangyu Cao, Wei Dai, and Juanjuan Meng)

This paper investigates the impact of firm pricing strategy on consumer dishonesty in the context of bike-sharing. Using more than one million trip records from a bike-sharing company and shock from a free-riding campaign, we create a novel measure of cheating and construct a Regression Discontinuity model to find that bike-sharing users would submit more false reports of defective bikes to escape payments when facing a price hike. Heterogeneity analysis reveals that the increases in cheating behaviors after a price surge are more conspicuous for male and non-student users and concerns about their social images during the daytime hours could mitigate motives to cheat. We also find suggestive evidence that consumers exhibit a reciprocal relationship with the bike-sharing company.