Fan Zhang

Research

Publication

A Method to Estimate Discrete Choice Models that is Robust to Consumer Search 

with Jason Abaluck and Giovanni Compiani, forthcoming at the Journal of Political Economy

Abstract: We state conditions under which choice data suffices to identify preferences when consumers may not be fully informed about the attributes of goods. Our approach can be used to test for full information, to forecast how consumers will respond to information, and to conduct welfare analysis when consumers are imperfectly informed. In a lab experiment, we successfully forecast the response to new information when consumers engage in costly search. In data from Expedia, our method identifies which attribute was not immediately visible to consumers in search results, and we then use the model to compute the value of additional information.

Work in Progress

Variety Seeking in High-Frequency Consumption: New Implications for Targeted Marketing

with Carol Hengheng Lu and Zhijie Lin

Abstract: We study consumers' variety-seeking preferences and explore their implications for targeted marketing using proprietary data from a food delivery platform. We document that a substantial fraction of consumers have variety-seeking preferences. Consumers, on average, are willing to pay 19.9%  more to switch to a different seller. In the counterfactual analysis, optimizing ranking by considering variety-seeking preferences increases platform revenue, consumer welfare, and purchase probability.  Furthermore, we find that consumers' variety-seeking preferences soften price competition. Optimal targeted pricing implies an increase in prices for rival sellers' consumers and a decrease in prices for the sellers' own consumers.

Learning Through Ratings Under Endogenous Product Quality

with Przemyslaw Jeziorski

Abstract: This paper examines the design of rating systems in markets where product quality is unobserved and dynamically managed by firms. We model this interaction as a dynamic Bayesian game, in which sellers can enhance quality through costly effort while buyers update their beliefs based on ratings. Ratings serve a dual purpose: they inform consumers, mitigating adverse selection, and incentivize seller effort, addressing moral hazard. A key feature of our dataset from Airbnb’s short-term rental marketplace is that seller effort is directly observable, allowing us to empirically separate moral hazard from adverse selection. Using structural estimates, we conduct counterfactual experiments to assess the impact of alternative rating systems and fake reviews on welfare, explicitly accounting for the endogenous provision of quality.