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23 Colum. Sci. & Tech. L. Rev. 1 (2021)

handle is hein.journals/cstlr23 and id is 1 raw text is: THE PRICE TAG OF PAY-FOR-DELAY


VOLUME XXIII                   STLR.ORG                         FALL 2021
Robin Feldman*
In a landmark decision nearly a decade ago, the U.S. Supreme Court opened
the door for antitrust suits against brand and generic pharmaceutical companies
who engage in collusive settlements to delay the time for the generic to come to
market. With these pay -for-delay agreements, brand-name companies offer
prospective generics some form of compensation in exchange for the generic's
promise not to enter the market until an agreed-upon date. Laying the groundwork
for the lawsuit that would eventually lead to the Actavis decision, the Federal Trade
Commission (FTC') published a study estimating that pay-for-delay agreements
cost American consumers $3.5 billion annually, a figure that has been cited
repeatedly by scholars and policymakers alike.
To understand the state of pay-for-delay agreements, this Article presents an
in-depth examination of the burden that pay-for-delay imposes, both on society at
large and on individual patients, and explores the modern legal landscape that has
emerged since the Supreme Court's historic pronouncement. Part I describes pay-
for-delay agreements, exploring the literature on the potential harm of such
agreements among pharmaceutical competitors. Part II presents a new analysis
demonstrating that the cost of pay-for-delay to American consumers is far greater
than anyone has recognized, and well beyond the $3.5 billion figure cited by the
FTC in 2010. We applied six different methodologies to provide as fair and broad
a view as possible. The range of methodologies show that at a minimum, the cost
ofpay-for-delay settlements on the U.S. population between 2006 and 2017 is $6.2
* Arthur J. Goldberg Distinguished Professor of Law, Albert Abramson '54 Distinguished
Professor of Law Chair, Director, Center for Innovation (C4i), University of California Hastings
Law. Research for this piece was funded in part by a generous grant from the Laura and John Arnold
Foundation. I wish to express my thanks to Rainy Alsaffar, Chief Data Scientist at C4i, for his work
in conducting the empirical analysis. I am grateful to Matthew Avery for helpful comments and to
Christopher Kim, Brittany Ng, Oriana Tang, Nathan Brown, Joseph Clateman and Nicholas Massoni
for research assistance. I am particularly indebted to Gideon Schor for leading the research team.



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