Working Papers

1. "How Parents' Beliefs About Their Children's Academic Ability Affect Educational Investments" [Job Market Paper]

Abstract: Using a randomized field experiment with parents of high school students in China, I examine the causal effects of parents' belief in their children's ability on educational investment and children's academic performance. I document two types of information frictions that result in systematic biases in parents' beliefs about children's ability: overconfidence in future performance and underestimating college admission requirements. I then introduce two interventions to correct parents' belief biases. In the first intervention, I use machine-learning techniques to generate predictions on children's future academic performance and distribute them to randomly selected parents. In the second intervention, I give randomly selected parents a report that lists the feasible colleges corresponding to their children's current academic performance. I find that both interventions lead to dramatic reductions in belief biases. In addition, parents report higher levels of monetary investments in children's education, which significantly improved children's academic performance. I also find significant non-linearity for the impacts of ability belief on parental educational investments around their aspirations.

Presentation: 2022 AEA (scheduled); The Economic Science Association’s Job-Market Candidates Seminar (scheduled); Graduate Students in Economics of Education Zoom Seminar; Beijing Normal University Workshop; Camphor Economist Circle Workshop; 2021 AAEA; Washington Area Development Economics Symposium; Urban and Regional Planning and Design Seminar; 2018 AEA; 2017 AAEA.

2. "Understanding Mechanisms Underlying Peer Effects on Educational Investment Among Parents: Evidence from China"

Abstract: Using a randomized experiment with 3379 parents of high-school students in China, I identify two channels of social influence in parents’ decisions on children’s educational investments: parents adjust their decisions based on other parents’ behaviors because they learn from other parents’ decisions (“social learning”) or because their children are facing competition from peers (“competition externality”). I find that both channels have statistically significant effects on parents’ investment decisions and increase their willingness to buy an educational service by over 20%. Although the average effects of the two channels are not statistically different, the main channels of peer effects are heterogeneous by parents’ educational background: parents with higher education are more likely to learn from peers’ decisions whereas those with lower education are mainly incentivized by the competition externality. These results also shed light on the mechanisms underlying the herding behavior and involution in other settings with competition, such as competitive sports.

Presentation: 2022 AEA (scheduled); Development and Applied Micro Tea.

3. "Invest in Talented or Invest in Disadvantaged: How Aspirations Affect Parents’ Investment Strategy"

Abstract: This paper introduces the reference-dependent utility theory into the parental education investment decisions by cooperating parents’ aspirations into their utility. The model assumes parents' utility function is discontinuous or kinked at the thresholds of achieving aspirations. The modification generates an interesting non-monotonic correlation between ability and optimal educational investments around the aspirations. When children haven't achieved the aspirations, parental educational investment is substitutive to children's ability – the lower the ability, the higher the investment. In contrast, when the aspirations are already reached, parents' investment becomes complementary to children's ability – the higher the ability, the higher the investment. This model can rationalize the remedial investments behaviors.

Presentation: Urban and Regional Planning and Design Seminar; 2018 AEA; 2017 AAEA.

Work in Progress

1. "Worker Screening: Applications of Machine Learning Methods to Firm Decision-Making", with Jing Cai and Shing-Yi Wang.

2. "How Machine Learning Techniques Help Students’ Optimize Their Subject Choices and College Admissions".