, Volume 20, Supplement 3, pp 11–29 | Cite as

Returns on Research and Development for 1990s New Drug Introductions

  • Henry Grabowski
  • John Vernon
  • Joseph A. DiMasi
Original Research Article


Background: Previously published research by the authors found that returns on research and development (R&D) for drugs introduced into the US market in the 1970s and 1980s were highly skewed and that the top decile of new drugs accounted for close to half the overall market value. In the 1990s, however, the R&D environment for new medicines underwent a number of changes including the following: the rapid growth of managed-care organisations; indications that R&D costs were rising at a rate faster than that of overall inflation; new market strategies of major firms aimed at simultaneous launches across world markets; and the increased attention focused on the pharmaceutical industry in the political arena.

Objective: The aim of this study was to examine the worldwide returns on R&D for drugs introduced into the US market in the first half of the 1990s, given that there have been significant changes to the R&D environment for new medicines over the past decade or so.

Results: Analysis of new drugs entering the market from 1990 to 1994 resulted in findings similar to those of the earlier research — pharmaceutical R&D is characterised by a highly skewed distribution of returns and a mean industry internal rate of return modestly in excess of the cost of capital.

Conclusions: Although the distribution of returns on R&D for new drugs continues to be highly skewed, the analysis reveals that a number of dynamic forces are currently at work in the industry. In particular, R&D costs as well as new drug introductions, sales and contribution margins increased significantly compared with their 1980s values.


Contribution Margin Capital Asset Price Model Generic Competition Patent Expiry Hurdle Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This paper was supported by an unrestricted grant from the Program in Pharmaceuticals and Health Economics at Duke University. We are indebted to a number of individuals who commented on prior versions of this paper, including Mike Scherer, Dennis Mueller, Bill Comanor, Patricia Danzon, Vivian Ho, Mike Morissey, David Grabowski, Robert Helms, Adrian Towse, Paul Meyer, and Steve Propper. Any errors that remain in the manuscript are the responsibility of the authors.

The Program receives support from various foundations, health sector entities and pharmaceuticals firms.


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Copyright information

© Adis International Limited 2002

Authors and Affiliations

  • Henry Grabowski
    • 1
  • John Vernon
    • 1
  • Joseph A. DiMasi
    • 2
  1. 1.Department of Economics, 314 Social SciencesDuke UniversityDurhamUSA
  2. 2.Tufts Center for the Study of Drug DevelopmentTufts UniversityBostonUSA

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