Collaborative Research: Empirical Analysis of the Practice of Price Discrimination in the Airline Industry

The practice of charging different prices to different consumers, i.e., price discrimination, is commonly used by firms in a variety of industries with the intention of increasing profits. Yet, little is known about the impact on consumers of modern sophisticated discriminatory pricing practices that leverage increasingly detailed information. This gap in understanding must be overcome to develop rules that balance positive aspects of these practices like efficiency against redistributive harm and privacy concerns. The researchers study these issues in the context of two different strategies used in the airline industry: personalized pricing via targeted discounts and auctions for upgraded services and amenities. The empirical analysis relies on unique data obtained from a North American airline to gain deeper insights into the practice of price discrimination by evaluating current and potential price-discrimination strategies. The results of the analysis provide insight into the effectiveness of different strategies and methods on profit and consumer welfare, while also offering guidance to decision makers on how to limit any negative impacts on consumers. The research contributes to knowledge of the welfare impacts of modern discriminatory pricing practices, as well as the challenges associated with implementation. The detailed proprietary data used in the empirical analysis provides a unique opportunity to study two different practices used in the airline industry: personalized pricing via targeted discounts and upgrade auctions. The results of the analysis regarding firms’ ability to leverage increasingly detailed data and implement sophisticated mechanisms to price discriminate is directly of interest to economists and important for development of rules to limit harm to consumers. The contribution of the research is in the field of econometrics and industrial organization, and specifically advances econometric methods for personalized pricing and integration of auctions into a dynamic-pricing environment. These advancements provide a framework for studying these topics in other industries, and guidance for important discussions around data privacy and price discrimination. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.