Summary: 

This paper addresses the role of "price trackers," particularly in the context of periodic sales. We show that when some consumers can obtain historical price information through price trackers (which implies that they become informed about how many low-valuation consumers have accumulated since the last sale and can thus predict how soon the firm will hold the next sale), the firm adjusts pricing by lowering the regular price and increasing the frequency of sales. This pricing adjustment can result in a positive information spillover to non-price tracker users.  In addition, our model provides an alternative explanation for the well-documented "Hi-Lo" pricing pattern: the rigidity of regular prices comes from consumers' incomplete information about historical prices.

Summary: This paper proposes a novel role of "limited-time offers" in search markets. We demonstrate that these offers are the most effective pricing tool for firms to manipulate consumers' search order: to induce "search prominence" or "search discrimination." In the existing literature, such marketing tactics have been associated with hold-up and anticompetitive effects (Armstrong and Zhou, 2015, "Search Deterrence"). Our findings complement this welfare view by showing that when prices are observable (after all, limited-time offers are often advertised), using limited-time offers may increase total welfare by inducing a more socially efficient search order.

Abstract: Many studies have shown that consumers, before deciding whether to purchase a new product or service, draw inferences from the choices of other consumers and actively acquire information from other sources. We propose a novel model that integrates two learning processes: observational learning and active learning. Building upon the classic observational learning framework, this model allows each consumer to make dynamic choices of information acquisition. We first analyze consumers' learning behavior in the presence of a given price, and then we endogenize the seller's dynamic pricing strategy in response to the two learning processes of consumers. We show that a forward-looking seller may find it optimal to sacrifice short-term profits by setting a higher price to induce active learning, thereby improving the information transmitted through observational learning and ultimately gaining higher expected future profits. We also investigate consumers' learning behavior, the seller's dynamic pricing strategy, and long-run market learning outcomes when the speed of information acquisition increases with sales.

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.