Network Effects, Nonlinear Pricing and Entry Deterrence

Download or read book Network Effects, Nonlinear Pricing and Entry Deterrence written by Arun Sundararajan and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A number of technology products display positive network effects, and are used in variable quantities by heterogeneous customers. Examples include operating systems, infrastructure and back-end software, web services and networking equipment. This paper studies optimal nonlinear pricing for such products, under incomplete information, and with the threat of competitive entry. Both homogeneous and heterogeneous network effects are modeled. Conditions under which a fulfilled-expectations contract exists and is unique are established. While network effects generally raise prices, it is shown that accompanying changes in consumption depend on the nature of the network effects - in some cases, it is optimal for the monopolist to induce no changes in usage across customers, while in others cases, network effects raise the usage of all market participants. Optimal pricing is shown to include quantity discounts that increase with usage, and may also involve a nonlinear two-part tariff. These results highlight the impact of network effects on trade-offs between price discrimination and value creation, and have important managerial implications for pricing policy in technology markets. The need to deter competitive entry generally lowers profits for the monopolist, and increases customer surplus. When network effects are homogeneous across customers, the resulting entry-deterring monopoly contract is a fixed fee and results in the socially optimal outcome. However, when the magnitude of heterogeneous network effects is relatively high, there are no changes in total surplus induced by the entry threat, and the price changes merely cause a transfer of value from the seller to its customers. The presence of network effects, and of a credible entry threat, are also shown to increase distributional efficiency by reducing the disparity in relative value captured by different customer types. Regulatory and policy implications of these results are discussed.