Essays on Dynamic Pricing and Choice in the Internet and Sharing Economy

dc.contributor.advisorTunca, Tunay Ien_US
dc.contributor.authorMing, Liuen_US
dc.contributor.departmentBusiness and Management: Decision & Information Technologiesen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.description.abstractThe widespread use of the Internet, social networking, mobile technology and big data has improved people's ability to obtain and use information to an unprecedented level. Influencing consumer behavior and changing concepts of consumption, the Internet and Sharing Economy has carved itself a significant and growing place in the daily life and the economy. The race to commercialize the value of this change has brought about numerous innovations and creative operational solutions in emerging industries worldwide. The first chapter of my dissertation theoretically and empirically studies consumer equilibrium, pricing, and efficiency of these events. Modeling a continuous time customer arrival and sign-up process, we start by deriving the stochastic dynamic consumer equilibrium. Based on this equilibrium and utilizing sign-up level data from a major Chinese retailer's group buying events, we then structurally estimate consumer arrival rates and utility distributions for 266 events, and empirically verify the fit and predictive power of the model. Utilizing the estimated arrival rates and consumer utility distributions, we then employ a doubly stochastic Generalized Linear Regression Model to provide empirical evidence for consumer network effects in group buying, and estimate 15.4% increase in consumer demand attributable to the employment of a group buying mechanism. Through counterfactual analysis, we further estimate that employing group buying increased retailer profits by 11.21% on average, corresponding to an annual monetary gain of approximately $4.32M for the 266 events in the data set. We further demonstrate that low deal discounts offered by the retailer for very low and very high consumer arrival rates boost profitability, suggesting that an inverse U-shaped deal discount pattern as a function of consumer arrival rate is recommendable when employing group buying events. Ride-sharing platforms, such as Uber and Lyft and their Chinese counterpart Didi, set prices dynamically to balance the demand and supply for their services. In the second chapter, we provide an empirical model and analysis of price formation and surplus generation of these services. We first develop a two-sided-market discrete choice model, capturing the formation of mutually dependent demand (consumer) and supply (driver) sides that jointly determine the pricing. Based on this model, we then use a comprehensive data set obtained from Didi to estimate consumer and driver price elasticities as well as other factors that affect market participation. Based on the estimation results and counterfactual analysis, we demonstrate that surge pricing has a significant role in improving the welfare of consumers and Kuaiche drivers, i.e., by 21.80% and 22.02%, respectively. In terms of government regulations, proposed regulation imposing price caps that match current Taxi rates can decrease consumer surplus by 39.84% while causing a relatively moderate 5.66% decrease in Kuaiche driver surplus. Further, we estimate that restricting driver capacity to equal local Taxi levels would have more severe consequences, resulting in 18.07% and 23.40% reductions in consumer and Kuaiche driver surpluses respectively.en_US
dc.titleEssays on Dynamic Pricing and Choice in the Internet and Sharing Economyen_US


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