Welfare Effects Of Dynamic Pricing

Airfares fluctuate due to demand shocks and intertemporal variation in willingness to pay. I estimate a model of dynamic airline pricing accounting for both sources of price adjustments using novel flight-level data. I use the model estimates to evaluate the welfare effects of dynamic airline pricing. Relative to uniform pricing, dynamic pricing benefits early-arriving, leisure consumers at the expense of late-arriving, business travelers. Although dynamic pricing ensures seat availability for business travelers, these consumers are then charged higher prices. When aggregated over markets, welfare is higher under dynamic pricing than under uniform pricing. The directionality of the welfare effect at the market level depends on whether dynamic price adjustments are mainly driven by demand shocks or by changes in the overall demand elasticity.

Dynamic pricing is designed to increase revenues or profits of the firm by adjusting prices in response to changes in the marginal value of capacity as described in Gallego and van Ryzin (1994). While thousands of papers have been written about dynamic pricing, this is to our knowledge the first paper to examine the impact of dynamic pricing in Gallego and van Ryzin (1994) on social welfare and consumers' surplus. We present a dynamic pricing formulation designed to maximize welfare and show that the optimal policy has the same structural property as the revenue-maximizing dynamic pricing policy. For sufficiently constrained systems, we show that the revenue maximizing dynamic pricing policy and the market clearing price are both asymptotically optimal for welfare. We also find that in most cases dynamic pricing improves consumers' surplus compared to the optimal static price for a revenue-maximizing firm. Our findings can potentially change the public image of dynamic pricing, and lead to rich managerial insights as well as policy implications: (1) for large-scale systems with scarce capacity, the central planner and a monopoly firm would essentially implement the same pricing policy; (2) a revenue-maximizing dynamic pricing policy can benefit customers when the demand elasticity is in a small bounded interval as is the case for several important demand functions.

We argue that dynamic pricing motivated by the management of inventory holding and ordering costs leads to increased operational efficiencies which could benefit firms without hurting consumers. To demonstrate this point, we equip the traditional economic order quantity (EOQ) setting with a rich set of demand models and compare social outcomes under two alternatives, dynamic and static pricing. We show that dynamic pricing generates higher retailer profits, a lower average price per unit sold, and higher sales volumes than static pricing. The mechanism behind the result is that with dynamic pricing the retailer ties the price of each unit to its holding costs, which allows him to increase the order quantity compared to static pricing and thus save on fixed ordering costs. Some of these cost savings are passed to consumers. Moreover, we demonstrate that this mechanism is robust to the presence of price-anticipating (strategic) consumer behavior.

Digital technologies are driving the application of dynamic pricing. Today, this pricing strategy is used not only for perishable products such as flights or hotel rooms, but for almost any product or service category. With dynamic pricing, retailers frequently adjust their prices over time to respond to factors such as demand, their supply and that of competitors, or the time of sale. Additionally, dynamic pricing allows retailers to take advantage of a large share of consumers' willingness to pay while avoiding losses from unsold products. Ultimately, this can lead to an increase in revenue and profit. However, the application of dynamic pricing comes with great challenges. In addition to the technological implementation, companies have to take into account that dynamic pricing can cause complex and unintended behavioral consequences on the consumer side. The key objective of this dissertation is to provide a deeper understanding of the impact of dynamic pricing on consumer behavior. To this end, this dissertation presents insights from four perspectives. First, how reference prices as a critical component in purchase decisions are operationalized. Second, how customers search for products priced dynamically, differentiated by business and private customers, as well as by different devices used for the search. Third, whether and how dynamic pricing influences the impact of internal reference prices on purchase decisions. Finally, this dissertation demonstrates that consumers perceive price changes as personalized in different purchase contexts, leading to reduced perceptions of fairness and undesirable behavioral consequences.

This paper assesses the welfare e⁄ects of rms ability to use data for group and personalized pricing in markets with unit ( q = 1) and multi-unit demand consumers ( q > 1). The disutility cost of not consuming the ideal good is a function of units purchased and can increase at a decreasing rate 2 [0 ; 1] as consumption increases (is the elasticity of the disutility cost with respect to q ): Group pricing (GP) and personalized pricing (PP) are compared to uniform pricing (UP). GP always boosts pro ts at the expense of consumers. When = 0 ; PP reduces industry pro ts and boosts consumer welfare. The same happens when q is low and/or is su¢ ciently high. In contrast, if heterogeneity in demand is su¢ ciently high and is su¢ ciently low, PP can enhance pro ts at the expense of consumer welfare.

Dynamic pricing is a common tool to maximize sales in markets for perishable goods with limited capacity and stochastic demand. We look at the relationship between dynamic pricing and oligopolistic competition in the airline industry. For this purpose, we estimate the dynamic oligopoly model of dynamic pricing competition in a market with carrier exit using flight-level data. We discover that dynamic pricing results in a Pareto improvement, increasing firm profits and consumer welfare. We break down the impact into two categories: price discrimination and pricing on residual capacity (revenue management). We find that price discrimination softens competition, whereas revenue management intensifies it.

We study the welfare implications of personalized pricing, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. Using data from a unique randomized controlled pricing field experiment we train a demand model and conduct inference about the effects of personalized pricing on firm and consumer surplus. In a second experiment, we validate our predictions in the field. The initial experiment reveals unexercised market power that allows the firm to raise its price optimally, generating a 55% increase in profits. Personalized pricing improves the firm's expected posterior profits by an additional 19%, relative to the optimized uniform price, and by 86%, relative to the firm's unoptimized status quo price. Turning to welfare effects on the demand side, total consumer surplus declines 23% under personalized pricing relative to uniform pricing, and 47% relative to the firm's unoptimized status quo price. However, over 60% of consumers benefit from lower prices under personalization and total welfare can increase under standard inequity-averse welfare functions. Simulations with our demand estimates reveal a non-monotonic relationship between the granularity of the segmentation data and the total consumer surplus under personalization. These findings indicate a need for caution in the current public policy debate regarding data privacy and personalized pricing insofar as some data restrictions may not per se improve consumer welfare.

The paper discusses the economic aspects of the most important questions (such as demand response or capacity allocation) related to differential pricing. First, we consider a revenue-neutral introduction of peak-load pricing. We examine under what circumstances does peak-load pricing lead to a Pareto improvement compared to uniform pricing. Second, we analyze what properties of customers make it profitable for a firm to introduce peak-load pricing. We find that on the supply side, incentives to introduce differential pricing may be technology-driven (i. e. high on-peak marginal costs) or demand-driven (i.e. low elasticity of substitution). Consumers benefit more if they can adopt to prices more flexibly. Innovative technology, such as smart meters, may help consumers benefit from real-time pricing. Such technology is expensive to install. This makes it necessary that consumers cover part of the costs. If they are myopic, or other effects of bounded rationality hinder their commitment, regulatory intervention might be needed to increase welfare. The more accessible enabling technology (price comparison websites, cheap smart meters etc.) will be, the more everyone will benefit from time-varying pricing.