Limited Time Offer as an Invitation to Search, with Zheng Gong, submitted
Abstract: A limited time offer is a special deal that lasts a limited period of time. Observing that firms usually actively advertise limited time offers in the real world, we present a price-directed search model to underline that firms use this sales tactic for gaining search prominence. We find that limited time offers maximize total welfare through inducing the socially optimal search order with and without competition. In a monopoly market, the monopolist finds it optimal to use a limited time offer if and only if searching the firm before the outside option is socially optimal. In a competitive market, all firms employ limited time offers and are visited in the first-best search order in equilibrium. By contrast, if limited time offers are not allowed, competing firms may all make higher profits despite strictly lower total welfare.
What the Past Tells about the Future: Historical Price in the Durable Good Market, with Zheng Gong and Yuxin Chen, submitted
Abstract: We investigate the dynamic pricing strategy of a durable good monopolist in a new setting that assumes away perfect consumer information on historical prices. We first show that when all consumers with heterogeneous tastes are not informed of historical prices, the monopolist charges a high regular price for most of the time and periodically holds low-price sales. Then we consider the case in which a small proportion of consumers (such as price-tracker users) become informed of historical prices. At the new equilibrium, the monopolist lowers the regular price and advances sales, implying shorter price cycles, more frequent sales, and a positive spillover effect of price-tracker users' informational advantage on the rest of uninformed consumers. By analyzing how the presence of price trackers affects market outcomes, this paper also provides managerial implications for sellers and platforms on price history disclosure policies.
Of Bestsellers and Customer Reviews: A Model of Two Learning Processes, Revise and resubmit, Marketing Science
Abstract: This paper proposes a novel model that integrates two distinct learning processes to illustrate typical shopping behavior: observational learning and active learning. In this model, consumers who are uncertain about a product's quality first update their beliefs in a Bayesian fashion upon observing others' choices (observational learning), and then decide how much time to spend on acquiring more information (active learning). Among other things, the model captures a novel information-loss effect: a consumer's purchase decision would be less informative about a product's quality if her active learning is reduced, which induces more active learning by the next consumer. The model can be extended to other shopping scenarios, for example, learning from product ratings with heterogeneous consumers. I provide some suggestive evidence that is consistent with the model's predictions using unique transaction-level data on air purifier purchase.
Should Google Profit like a Taxi Driver?
Abstract: In recent years, numerous European countries have taken or have considered taking regulatory actions against Google News with the aim of improving news quality. This paper explains how news aggregators affect newspapers' incentives in quality investment from two novel perspectives: (1) a positive market-expansion effect of news aggregators by eliminating information asymmetry between newspapers and news readers, and (2) a negative business-stealing effect by displaying excerpts of newspaper articles (snippets) on news aggregators' own sites, which are substitutes of original news. The model illustrates both effects and can be used to evaluate taxation policies on snippets. A tax proportional to how much information extracted from the original news, or a click-through subsidy paid by newspapers to aggregators can discourage news aggregators from showing free previews to appropriate traffic. Moreover, I extend the benchmark setting from one single newspaper to multiple newspapers, capturing an additional competition-in-traffic effect among newspapers. Finally, I also show that the model is robust to many other generalizations.
Abstract: This paper studies learning in the stock market. Our contribution is to propose a model to illustrate the endogenous timing decision on trading, taking into account the incentive of learning from others about the fundamental value. The model is similar to Easley and O'Hara (1992), except that we introduce less-informed traders whose private information is inferior to fully-informed traders, but superior to that of random noise traders and a zero-profit market maker. We also allow both types of informed traders to optimize timing of trading. We show that fully-informed traders act as early birds because it is optimal for them to buy or sell at the earliest possible time; meanwhile, less-informed traders could be better off as second mice by delaying transactions to learn from previous trades. The greater information asymmetry between the less-informed traders and the market maker, the larger profits the former could make even though the latter is learning from all trades.
Work in Progress
Over-investment Due to Demand Uncertainty and Relocation Cost, with Yuxin Chen