Selected Publications
When Does Regulation Distort Costs? Lessons from Fuel Procurement in US Electricity Generation: Comment
( Han, Jin Soo | Houde, Jean-Francois | Van Benthem, Arthur A. | Abito, Jose Miguel )AMERICAN ECONOMIC REVIEW2021-04
Abstract
We revisit one of the results in Cicala (2015) and show that the previously estimated large and significant effects of US electricity restructuring on fuel procurement are not robust to the presence of outliers. Using methodologies from the robust statistics literature, we estimate the effect to be less than one-half of the previous estimate and not statistically different from zero. The robust methodology also identifies as outliers the plants owned by a single company whose coal contracts were renegotiated before discussions about restructuring even started.
Positive feedback in coordination games: Stochastic evolutionary dynamics and the logit choice rule
( Hwang, Sung-Ha | Rey-Bellet, Luc )GAMES AND ECONOMIC BEHAVIOR2021-03
Abstract
We study the problem of stochastic stability for evolutionary dynamics under the logit choice rule. We consider general classes of coordination games, symmetric or asymmetric, with an arbitrary number of strategies, which satisfies the marginal bandwagon property (i.e., there is positive feedback to coordinate). Our main result is that the most likely evolutionary escape paths from a status quo convention consist of a series of identical mistakes. As an application of our result, we show that the Nash bargaining solution arises as the long run convention for the evolutionary Nash demand game under the usual logit choice rule. We also obtain a new bargaining solution if the logit choice rule is combined with intentional idiosyncratic plays. The new bargaining solution is more egalitarian than the Nash bargaining solution, demonstrating that intentionality implies equality under the logit choice model. ⓒ 2021 Elsevier Inc.
The Effect of Monetary Policy on Bank Wholesale Funding
( Choi, Dong Beom | Choi, Hyun-Soo )MANAGEMENT SCIENCE2021-01
Abstract
We study how monetary policy affects the funding composition of the banking sector. When monetary tightening reduces the supply of retail deposits, banks attempt to substitute wholesale funding for deposit outflows to smooth their lending. Because of financial frictions, banks have varying degrees of access to wholesale funding. Therefore, large banks, or those with greater reliance on wholesale funding, increase their wholesale funding more. Consequently, monetary tightening increases both the reliance on and the concentration of wholesale funding within the banking sector. Our findings also suggest that liquidity requirements could bolster monetary policy transmission through the bank lending channel. Copyright: ⓒ 2020 INFORMS.
A Model of Brand Architecture Choice: A House of Brands vs. A Branded House
( Yu, Jungju )MARKETING SCIENCE2021-01
Abstract
Some firms that operate in multiple product markets use the same brand in different markets, whereas others use different brands in different markets. This research investigates in which product markets a firm should use the same or different brands and how this decision depends on the relatedness of product markets. To answer this question, I propose a framework of market relatedness that characterizes the relationships among distinct product markets from the supply side (e.g., shared production technology) and demand side (e.g., correlated customer preferences). This framework is applied to a model of reputation in which a multiproduct firm's product quality is jointly determined by its hidden capability type (i.e., adverse selection) and hidden choice of effort level (i.e., moral hazard) in each product market. Consumers obtain noisy information about the firm by observing its track record, that is, product quality produced in the past. Umbrella branding allows consumers to pool the firm's track record across different product markets and form expectations about the product quality based on market relatedness. The analysis shows that umbrella branding is optimal if supply-side relatedness is high and demand-side relatedness is not too high. However, if the product markets are closely related in both dimensions, then independent branding may be optimal because, as an umbrella brand, the firm faces a temptation to exploit positive information spillover across product markets through its shared brand name. By using different brand names, a firm can credibly commit to investing in all product markets and thereby earn higher profits. Finally, this paper provides implications for an umbrella brand's customer relationship management strategy whether to serve the same or distinct customer segments with its products.
Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach
( Shin, Donghyuk | He, Shu | Lee, Gene Moo | Whinston, Andrew B. | Cetintas, Suleyman | Lee, Kuang-Chih )MIS Quarterly2020-12
Abstract
This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model's power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.
