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Dynamic Entry With Demand And Supply Side Spillovers

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  • Yong Tan

Abstract

Supply side spillovers have been used to explain firms' entry behavior in the pharmaceutical industry. In contrast, demand side spillovers have received less attention. This paper identifies supply and demand spillovers using a dynamic model of strategic interaction. The results indicate that demand side spillovers are more significant than the supply side spillovers to generic drug firms, and the demand side spillovers increase a firm's market share by 3%–4% on average in subsequent market entry. In addition, with supply side spillovers alone, lowering entry barriers can increase future entry rates, while in the case of demand side spillovers, lowering entry barriers will have the opposite effect. (JEL L110, L130, D220)

Suggested Citation

  • Yong Tan, 2019. "Dynamic Entry With Demand And Supply Side Spillovers," Contemporary Economic Policy, Western Economic Association International, vol. 37(1), pages 86-101, January.
    • Handle: RePEc:bla:coecpo:v:37:y:2019:i:1:p:86-101
    DOI: 10.1111/coep.12391
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    1. Rebecca Henderson & Iain Cockburn, 1996. "Scale, Scope, and Spillovers: The Determinants of Research Productivity in Drug Discovery," RAND Journal of Economics, The RAND Corporation, vol. 27(1), pages 32-59, Spring.
    2. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    3. Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
    4. Michael J. Mazzeo, 2002. "Product Choice and Oligopoly Market Structure," RAND Journal of Economics, The RAND Corporation, vol. 33(2), pages 221-242, Summer.
    5. Pierre Dubois & Olivier de Mouzon & Fiona Scott-Morton & Paul Seabright, 2015. "Market size and pharmaceutical innovation," RAND Journal of Economics, RAND Corporation, vol. 46(4), pages 844-871, October.
    6. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    7. Hai Che & Tülin Erdem & T. Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    8. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 497-529.
    9. Lucia Foster & John Haltiwanger & Chad Syverson, 2016. "The Slow Growth of New Plants: Learning about Demand?," Economica, London School of Economics and Political Science, vol. 83(329), pages 91-129, January.
    10. Gianni Amisano & Maria Letizia Giorgetti, 2013. "Entry Into Pharmaceutical Submarkets: A Bayesian Panel Probit Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 667-701, June.
    11. Berry, Steven T, 1992. "Estimation of a Model of Entry in the Airline Industry," Econometrica, Econometric Society, vol. 60(4), pages 889-917, July.
    12. Paul Klemperer & A. Jorge Padilla, 1997. "Do Firms' Product Lines Include Too Many Varieties?," RAND Journal of Economics, The RAND Corporation, vol. 28(3), pages 472-488, Autumn.
    13. Hai Che & Tülin Erdem & T. Sabri Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    14. Alessandro Gavazza, 2011. "Demand spillovers and market outcomes in the mutual fund industry," RAND Journal of Economics, RAND Corporation, vol. 42(4), pages 776-804, December.
    15. Victor Aguirregabiria & Pedro Mira, 2007. "Sequential Estimation of Dynamic Discrete Games," Econometrica, Econometric Society, vol. 75(1), pages 1-53, January.
    16. C. Lanier Benkard, 2004. "A Dynamic Analysis of the Market for Wide-Bodied Commercial Aircraft," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 71(3), pages 581-611.
    17. David Reiffen & Michael R. Ward, 2005. "Generic Drug Industry Dynamics," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 37-49, February.
    18. Silvia Appelt, 2015. "Authorized Generic Entry prior to Patent Expiry: Reassessing Incentives for Independent Generic Entry," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 654-666, July.
    19. Katja Seim, 2006. "An empirical model of firm entry with endogenous product‐type choices," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 619-640, September.
    20. A. M. Spence, 1981. "The Learning Curve and Competition," Bell Journal of Economics, The RAND Corporation, vol. 12(1), pages 49-70, Spring.
    21. Ken Hendricks & Alan Sorensen, 2009. "Information and the Skewness of Music Sales," Journal of Political Economy, University of Chicago Press, vol. 117(2), pages 324-369, April.
    22. Bresnahan, Timothy F. & Reiss, Peter C., 1991. "Empirical models of discrete games," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 57-81.
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