Yingzhi Liang (梁应之)


I am a doctoral candidate at the University of Michigan, School of InformationMy research interests are behavioral and experimental economics, market and mechanism design. I design theoretically-based mechanisms to solve social problems and evaluate them using controlled lab and field experiments.

I am fortunate to work with field partners in designing and conducting large scale randomized field experiments, including the largest ride-sharing company in China: DiDi, the non-profit micro-lending platform: Kiva.org, and the personalized online education platform: ECoachand the interactive decision-making learning tool: MobLab Inc.

I expect to graduate this Summer. I will join The Chinese University of Hong Kong (CUHK) Business School as an Assistant Professor this Fall.

Fields: 
Behavioral and Experimental Economics, Market and Mechanism Design, Data Science, Causal Inference, Gig Economy, Social Network


References:
yanchen@umich.edu
trosenbl@umich.edu
econdm@umich.edu
leider@umich.edu
zhixiwan@hku.hk
blgong@dbm.ecnu.edu.cn

 

Publications:

Yan Chen, Yingzhi Liang, Tayfun Sönmez. Journal of Mechanism and Institution Design 1.1 (2016): 45-82.

We present an experimental study of three school choice mechanisms under complete information, using the designed environment in 
Chen & Sönmez (2006). We find that the top trading cycles (TTC) mechanism outperforms both the Gale-Shapley deferred acceptance (DA) 
and the Boston immediate acceptance (BOS) mechanism in terms of truth-telling and efficiency, whereas DA is more stable than either TTC or BOS. Compared to the incomplete information setting in Chen & Sönmez (2006), the performance of both TTC and BOS improves with more information, whereas that of DA does not.


Working Papers:

Yingzhi Liang, Binglin Gong. Resubmitted to Management Science (2020)

Market design has provided many managerial insights into why certain market institutions fail while others succeed in allocating scarce resources in both the for- and non-profit sectors. In this paper, we analyze a new form of dynamic matching mechanism enabled by innovations in information technology. We provide a theoretical and experimental examination of this mechanism in the context of college admissions in Inner Mongolia, China, where students are given real-time allocation feedback and are allowed to revise their choices. Theoretically, we show that efficient and stable outcomes arise in every rationalizable strategy profile if there is a sufficient number of revision opportunities. Experimentally, we find that in an environment with high strategic complexity, the Inner Mongolia Dynamic mechanism performs better than theoretical predictions: It is as stable as the Deferred Acceptance mechanism and as efficient as the Boston mechanism, with higher truth-telling rate than both of them. These results suggest that the Inner Mongolia Dynamic mechanism can be a good substitute for static mechanisms in complex environments. The Inner Mongolia Dynamic mechanism may also be useful in matching potential employers and employees in the labor market.

Incorporating Private Information Into Centralized Algorithms: A Field Experiment at a Ride-Sharing Platform.
Yingzhi Liang. (2020) 

Many have seen the gig economy as the "future of work". Despite having more flexible working hours than workers at traditional workplaces, ride-sharing drivers have little power over the algorithm that assigns them tasks. As a result, there can exist a misalignment between driver location preference and assigned trips. We examine the effect of allowing drivers to self define working regions through a natural experiment on the largest ride-sharing platform in China, DiDi. We find that treatment drivers increase working hours and income by more than 4% while maintaining productivity, measured by hourly earnings. We also find no evidence that the treatment lowers matching efficiency, measured by passengers and drivers' waiting time. 

Group Size and Efficient Contribution Level in Voluntary Contribution Mechanism.
Yan Chen, Yingzhi Liang. Working Paper (2020)

We investigate the optimal group size for public goods provision when group members have complementary efforts. We model the complementarity of efforts by adding the constant elasticity of substitution (CES) function into the canonical linear public goods provision model. We find that the optimal team size depends on the level of complementarity. When the complementarity is high, there is an upper bound for the optimal team size, but when the complementarity is low, there is a lower bound for the optimal team size. These theoretical predictions are validated via a lab experiment. 

Work in Progress:

Revitalizing Dormant Teams in Online Communities: A Field Experiment on Kiva.org.
Wei Ai, Roy Chen, Yan Chen, Yingzhi Liang, Qiaozhu Mei.

We investigate the effect of repeated interventions in charitable giving on microfinance platform Kiva.org. After sending forum messages to inactive teams every month for six months, we find that the treatment lenders lend significantly more in the first month, but the effect gradually decreases over the course of the experiment.

Team Composition: Friends or Strangers?
Yingzhi Liang, Tanya Rosenblat.

We study peer effect by randomly assigning group members in an undergraduate introductory programming course. Students are paired with a friend or a stranger in the same class to complete a group assignment. We find that students paired with friends have a 6% higher completion rate than students paired with strangers. A follow-up survey reveals that the effect comes from students being more accountable towards their friends than unfamiliar classmates. Our findings confirm the effectiveness of using strong ties as a commitment device in the education setting.

Predicting Students Academic Success Using Simple Economics Games.
Yan Chen, Yingzhi Liang, Qiaozhu Mei, Dongwu Wang, Stephanie Wang.

We predict undergraduate student GPA using their choices and behavioral patterns from a set of simple economics games, including the trust game, beauty contest, competitiveness game, risk lottery, and knapsack problem. Among machine learning models used, LassoLars performs the best, but the overall predicting power of these economic games is small. A self-reported procrastination measure is more predictive than any game choices.