Hello! I’m Si ZUO 左思

How to say my name?   See Zoo-oh

I am a fourth-year Ph.D. in Strategy and Business Economics, SC Johnson Graduate School of Management & Economics Department, Cornell University. 

My research interests are industrial organization and quantitative marketing. Specifically, I am interested in

  • Dynamic on Platforms: how small firms build reputations and set dynamic pricing on large online platforms;

  • Online/Offline Interaction: what will happen to physical stores if the chain opens an online store;

I use casual inference, structural estimation, machine learning, and game theory model for the research questions. 

 

I am passionate about teaching and am the course designer/lead instructor of Cornell Johnson NBA 6955 Industrial Organization, Consulting, and Business Strategy in Fall 2022.

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Phd in Economics, Cornell University

September 2019 - present

MS in Economics, Hong Kong University of Science and Technology, Hong Kong

September 2018 - June 2019

BS in Economics, Sun Yat-sen University, Guangzhou, China

October 2014 - June 2018

(Exchange Student, October 2016 - February 2017, Nagoya University, Japan)

Research

Working Paper & Work in Progress

Covered by South China Morning Post. Presented in International Industrial Organization Conference (2022, Boston), North America SummerMeeting (2022, Miami), Asia-Pacific Industrial Organization Conference (2021, NUS)

Abstract:

To build a reputation on online platforms, new firms need to accumulate reviews through sales and consider the corresponding pricing strategy. We construct a dynamic model with both price signaling and a review-based reputation system. A high-quality firm can signal its unobserved quality by setting a lower introductory price than that of a low-quality firm because the high-quality firm benefits more from accumulating reviews in early periods. Using data from Zaihang, a service platform, we find empirical evidence that experts with high unobserved ability indeed adopt low introductory prices. We use an expert's performance on another platform as an instrument for the expert's ability to provide evidence for the causal relationship. The price and sales dynamics in the data are also consistent with the model predictions. The platform can accelerate quality revelation by facilitating price signaling. To do so, platforms could make price comparison easier and provide training to new firms about signaling.

Stores Going Online: Market Expansion or Cannibalization? , with Yangguang Huang and Chenyang Li

Abstract:
With the rise of e-commerce, more and more chain stores have opened online sales channels. For one chain, there are usually one online store and many offline stores. Online stores may cannibalize the sales of the existing physical stores because of their advantage in lower shopping costs. On the other hand, the online sales channel is usually a tool for advertisement, which may expand the offline store's market. From our novel daily revenue data of 380 offline stores from 2016 to 2020, we identify the countervailing cannibalization effect and the informative effect of opening up online branches on offline stores. We first use exogenous demand shocks (weather, Covid-19, and online shopping festivals) to provide solid evidence of these two effects. We then separately estimate these two effects by a structural model. We find that the cannibalization effect dominates the informative effect in most cases. The electronics category has the largest cannibalization effect, while the cosmetics and jewelry category has the smallest.

Externality Within the Shopping Mall, with Tianli Xia

Abstract:
Many papers show there exists the externality among shops within a mall or shopping street, but there is little study about how the externality changes across space and categories. Using the novel daily data of 380 stores in a large mall from 2016 to 2020, we identify the externalities from anchor stores using the anchor stores' promotional events. We adopt a new IV for a store's promotion: the promotional events of the other stores under the same brand in the same city. Then we show how the externalities vary across floors, distance, and store categories, which is unique to the existing literature. Finally, we use simulations to illustrate how rent contracts and store allocations could internalize the externalities among shops and provide managerial suggestions.

Teaching

Instructor

NBA 6955 Industrial Organization, Consulting and Business Strategy                                                               Fall 2022
Course Page     Syllabus

MBA Elective Course (also open for Graduates), Course Designer, and Lead Instructor.
32 students enrolled, Evaluation 4.4/5. 
SC Johnson Graduate School of Management, Cornell University.

Industrial Organization Research Workshop  Syllabus                                                                                       Winter 2022

CICER Winter Camp for Undergraduates, Cornell Institute for China Economic Research, Cornell University.

Teaching Assistant

MBA Courses

Data Analysis and Modeling (with Sessions), MBA Core Course, for Omid Rafieian, SC Johnson, Cornell University,           Summer 2022              

Microeconomics for Management, MBA Core Course, for Yi Chen & Michael Waldman, SC Johnson, Cornell University,         

             Summer 2021 & Fall 2020

Strategy, Cornell-Tsinghua Finance MBA Core Course,  for Thomas Jungbauer, SC Johnson, Cornell University,                       

Winter 2021 & Spring 2021

Ph.D. Courses

Applied Microeconomics II: Game Theory, Ph.D. Core Course, for Michael Waldman, Dyson School of Applied Economics and Management, Cornell University,                                                                                                                                                                Spring 2022

Microeconomics Theory I (with Sessions), Ph.D. Core Course, for David Easley, Economics Department, Cornell University,      Fall 2021

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NBA 6955 Industrial Organization, Consulting, and Business Strategy 

Open to MBA and all Graduates.

M/W 1:25-2:40pm, Breazzano Center 123, August 22nd - Oct. 7th

Course Description and Syllabus

This course aims to enable students to apply IO models to study real-world problems. We will learn

(i.) the fundamentals of game theory through numerous examples;

(ii.) the application of game theory models in various IO topics: pricing and firm competition, product differentiation, merger analysis (antitrust), platform and entry analysis.

(iii.) market analysis and case studies for different industries/marketplaces (oil, smartphone, music streaming, social media apps, food delivery platforms, ride-sharing platforms, and EV industry).

(iv.) empirical methods used in research and in the consulting industry (regression analysis, structural models, and causal inferences).

This course is especially helpful for students interested in consulting or related industry jobs. 

Guest Speakers and Guest Lectures (40mins)

There are excellent guest speakers for this course in 2022 Fall :

Prof. Michale Waldman (SBE, Johnson, Cornell) will give a guest lecture about product line design on Sept 7th.

Prof. Thomas Jungbauer (SBE, Johnson, Cornell) will give a guest lecture about online ad targeting and searching on Sept. 21st.

Prof. Omid Rafieian (Marketing, Johnson, Cornell) will give a guest lecture about machine learning, AI, and personalization on Sept. 28th.

Prof. Justin Johnson (SBE, Johnson, Cornell) will give a guest lecture about antitrust law and practice on Oct. 3rd. 

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