Wells Zhou

I am a Ph.D. candidate in Economics at UC-Irvine. My primary research interests include Industrial Organization and Information Economics.

I am on the 2023-2024 academic year job market and will be available for interviews during the ASSA 2024 Annual Meeting.



Working Paper

"Strategic Data Acquisition and Price Competition" (Job Market Paper) Micro
Abstract: Leveraging the power of modern data analytics and the increasing access to consumer data, businesses can now infer consumer preferences, enabling them to personalize advertising and implement differential pricing strategies. However, the consequences of determining which consumer information to acquire become unclear when firms engage in competition. To explore the strategic implications of data acquisition choices on market competition, I present a two-stage duopoly model. In the first stage, firms decide which consumer characteristics they aim to learn, and in the second stage, both firms engage in costly advertising with the gathered information. In contrast to the monopoly benchmark, where the monopolistic firm never acquires partial information, I demonstrate that under competition, equilibria exist where both firms strategically acquire distinct consumer characteristics.
"Selling via Informational Intermediary" Micro
Avaliable upon request
Abstract: This paper studies the optimal information design for an informational intermediary that earns commission fees by retaining sales. The effects of the announced disclosure policy are two-fold. While a more informative policy will be more likely to attract consumers to visit, some amount of concealment generates high revenues. In the presence of such trade-offs, we characterize the optimal disclosure policy. In particular, the optimal policies take the form of binary signals.
"Demand Estimation with Image Data" IO
Avaliable upon request
Abstract: Visualization of products might be crucial for consumers making purchasing decisions. One challenge to including visual information about products in demand analysis is due to the high dimensionality of the image data. I propose a two-stage semi-nonparametric estimation strategy to estimate demand in differentiated markets based on aggregated data and image data. In particular, the proposed estimation strategy builds on the standard framework developed by Berry (1994) and Berry, Levinsohn, and Pakes (1995) by adding image data into the model. The first stage of estimation is to transform a demand system to a partial linear form, a technique proposed recently by Lu, Shi, and Tao (2019). In the second stage, a convolutional neural network (CNN) model from machine learning is applied to estimate the "visual utility" function. The estimation is considered under a semi-nonparametric framework and the results from sieve estimation are used to establish the consistency. A simulation study is included to demonstrate the proposed estimation strategy.

Work in Progress

"New Estimation Method for the Binary Choice Panel Data Model with Lagged Independent Variables" Metrics
Avaliable soon
Abstract: Avaliable soon.


University of California-Irvine
Ph.D. in Economics
University of Wisconsin-Madison
M.S. in Economics
University of Minnesota
B.A. in Mathematics
B.S. in Economics