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Testing Dynamic Oligopolistic Interaction: Evidence from the Semiconductor Industry

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  • Christine Zulehner

Abstract

This paper analyzes the impact of a dynamic specification on the estimation of the conduct parameter in an oligopolistic market. Various empirical studies have shown that in the semiconductor industry, in particular in the Dynamic Random Access Memory (DRAM) market, one has to account for dynamic elements as learning-by-doing within firms and learning spillovers among them. Therefore this market seems to be appropriate to investigate whether firms behave strategically in a dynamic sense and how open-loop or closed-loop as equilibrium concepts alter the size of the estimated parameters. I apply a structural oligopolistic model of dynamic nonprice competition that incorporates learning-by-doing and spillovers. Theory shows that learning-by-doing and learning spillovers have important consequences for firm behavior. Whether firms in the DRAM industry take the strategic effects of learning-by-doing and learning spillovers actually into account when choosing their output strategies, is answered with empirical evidence. Using quarterly data from 1974-1996 at the firm level, I estimate demand and pricing relations for three different generations of DRAM chips. The empirical results show that the game theoretic specification has an important impact and that firms behave strategically. The assumption of an open-loop specification would underestimate the conduct parameter on average about 50%. ZUSAMMENFASSUNG - (Testen dynamischer oligopolistischer Interaktion: Empirische Evidenz aus der Halbleiterindustrie) In diesem Arbeitspapier wird der Einfluß einer dynamischen Spezifikation auf die Schätzung des Verhaltensparameters in einem oligopolistischen Marktes untersucht. Verschiedene empirische Studien haben gezeigt, daß die Halbleiterindustrie, im speziellen der Dynamic Random Access Memory (DRAM) Markt, von dynamischen Elementen wie Learning-by-doing in Unternehmen und Learning spillovers zwischen Unternehmen geprägt ist. Das wirft die Frage auf, ob sich Unternehmen in einem dynamischen Sinne strategisch verhalten und wie open-loop beziehungsweise closed-loop als Gleichgewichtskonzepte die Größe der geschätzten Parameter verändern. In diesem Papier wird ein strukturelles oligopolistisches Modell in einem dynamischen Kontext betrachtet, indem Unternehmen Mengen setzten und Learning-by-doing und Learning spillovers relevant sind. Die Theorie zeigt, daß Learning-by-doing und Learning spillovers wichtige Konsequenzen für das Verhalten von Unternehmen haben. Ob die Unternehmen in der DRAM Industrie tatsächlich die strategischen Effekte aus Learning-by-doing und Learning spillovers in Betracht ziehen, wird auf empirische Weise versucht zu beantworten. Unter der Verwendung vierteljährlicher firmenspezifischer Daten der Jahre 1974-1996 werden die Nachfrage- und die Angebotsgleichung für drei Generationen von DRAMs geschätzt. Die Schätzergebnisse zeigen, daß die spieltheoretische Spezifikation einen wichtigen Einfluß hat und daß sich Unternehmen strategisch in einem dynamischen Sinne verhalten. So unterschätzt die Annahme einer open-loop Gleichgewichtslösung den Verhaltensparameter im Durchschnitt um 50% unterschätzen.

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Bibliographic Info

Paper provided by Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG) in its series CIG Working Papers with number FS IV 99-17.

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Length: 42 pages
Date of creation: Sep 1999
Date of revision:
Publication status: Published in the International Journal of Industrial Organization , Vol. 21(10), December 2003, pp. 1527-1556.
Handle: RePEc:wzb:wzebiv:fsiv99-17

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Keywords: Oligopoly; dynamic games; semiconductor industry;

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  1. Bresnahan, Timothy F., 1989. "Empirical studies of industries with market power," Handbook of Industrial Organization, in: R. Schmalensee & R. Willig (ed.), Handbook of Industrial Organization, edition 1, volume 2, chapter 17, pages 1011-1057 Elsevier.
  2. Dick, Andrew R, 1991. "Learning by Doing and Dumping in the Semiconductor Industry," Journal of Law and Economics, University of Chicago Press, vol. 34(1), pages 133-59, April.
  3. Mark J. Roberts & Larry Samuelson, 1988. "An Empirical Analysis of Dynamic, Nonprice Competition in an Oligopolistic Industry," RAND Journal of Economics, The RAND Corporation, vol. 19(2), pages 200-220, Summer.
  4. Steen, Frode & Salvanes, Kjell G., 1999. "Testing for market power using a dynamic oligopoly model," International Journal of Industrial Organization, Elsevier, vol. 17(2), pages 147-177, February.
  5. Ronald S. Jarmin, 1994. "Learning by Doing and Competition in the Early Rayon Industry," RAND Journal of Economics, The RAND Corporation, vol. 25(3), pages 441-454, Autumn.
  6. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
  7. Feenstra, Robert C & Levinsohn, James A, 1995. "Estimating Markups and Market Conduct with Multidimensional Product Attributes," Review of Economic Studies, Wiley Blackwell, vol. 62(1), pages 19-52, January.
  8. Neven, Damien & Roller, Lars-Hendrik, 1999. "An aggregate structural model of competition in the European banking industry," International Journal of Industrial Organization, Elsevier, vol. 17(7), pages 1059-1074, October.
  9. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
  10. Slade, Margaret E, 1995. "Product Rivalry with Multiple Strategic Weapons: An Analysis of Price and Advertising Competition," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 4(3), pages 445-76, Fall.
  11. C. Lanier Benkard, 2000. "Learning and Forgetting: The Dynamics of Aircraft Production," American Economic Review, American Economic Association, vol. 90(4), pages 1034-1054, September.
  12. Karp, Larry S & Perloff, Jeffrey M, 1989. "Dynamic Oligopoly in the Rice Export Market," The Review of Economics and Statistics, MIT Press, vol. 71(3), pages 462-70, August.
  13. Irwin, Douglas A & Klenow, Peter J, 1994. "Learning-by-Doing Spillovers in the Semiconductor Industry," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1200-1227, December.
  14. Ralph Siebert, 1999. "Multiproduct Competition, Learning by Doing and Price-Cost Margins over the Product Life Cycle: Evidence from the DRAM Industry," CIG Working Papers FS IV 99-21, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
  15. A. M. Spence, 1981. "The Learning Curve and Competition," Bell Journal of Economics, The RAND Corporation, vol. 12(1), pages 49-70, Spring.
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Cited by:
  1. Luca Colombo & Paola Labrecciosa, 2012. "Inter-firm knowledge diffusion, market power, and welfare," Journal of Evolutionary Economics, Springer, vol. 22(5), pages 1009-1027, November.
  2. Andrew Clarke, 2008. "Learning-by-Doing and Productivity Dynamics in Manufacturing Industries," Department of Economics - Working Papers Series 1032, The University of Melbourne.
  3. Lambertini, Luca & Mantovani, Andrea, 2006. "Identifying reaction functions in differential oligopoly games," Mathematical Social Sciences, Elsevier, vol. 52(3), pages 252-271, December.
  4. Siebert, Ralph Bernd, 2010. "Learning-by-Doing and Cannibalization Effects at Multi-Vintage Firms: Evidence from the Semiconductor Industry," MPRA Paper 24008, University Library of Munich, Germany.

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