Managing Digital Piracy: Pricing, Protection and Welfare

16 Pages Posted: 13 Oct 2008

See all articles by Arun Sundararajan

Arun Sundararajan

NYU Stern School of Business; New York University (NYU) - Center for Data Science

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Date Written: May 2003


This paper analyzes the optimal choice of pricing schedules and technological deterrencelevels in a market with digital piracy, when legal sellers can sometimes control the extentof piracy by implementing digital rights management (DM) systems. It is shown that the seller'soptimal pricing schedule can be characterized as a simple combination of the zero-piracy pricingschedule, and a piracy-indifferent pricing schedule which makes all customers indifferent betweenlegal consumption and piracy. An increase in the level of piracy is shown to lower prices and profits,but may improve welfare by expanding the fraction of legal users and the volume of legal usage.In the absence of price-discrimination, the optimal level of technology-based protection againstpiracy is shown to be the technologically-maximal level, which maximizes the difference betweenthe quality of the legal and pirated goods. However, when a seller can price-discriminate, it isalways optimal for them to choose a strictly lower level of technology-based protection. Moreover,if a DRM system weakens over time, due to its technology being progressively hacked, the optimalstrategic response may involve either increasing or decreasing the level of technology-based protectionand the corresponding prices. This direction of change is related to whether the technologyimplementing each marginal reduction in piracy is increasingly less or more vulnerable to hacking.Pricing and technology choice guidelines based on these results are presented, and some socialwelfare issues are discussed.

Suggested Citation

Sundararajan, Arun, Managing Digital Piracy: Pricing, Protection and Welfare (May 2003). NYU Working Paper No. 2451/14154, Available at SSRN:

Arun Sundararajan (Contact Author)

NYU Stern School of Business ( email )

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New York University (NYU) - Center for Data Science ( email )

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