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Testing Dynamic Oligopolistic Interaction: Evidence from the Semiconductor Industry


  • Christine Zulehner


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.

Suggested Citation

  • Christine Zulehner, 1999. "Testing Dynamic Oligopolistic Interaction: Evidence from the Semiconductor Industry," CIG Working Papers FS IV 99-17, Wissenschaftszentrum Berlin (WZB), Research Unit: Competition and Innovation (CIG).
  • Handle: RePEc:wzb:wzebiv:fsiv99-17

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    References listed on IDEAS

    1. 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.
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    6. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    7. 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 445-476, Fall.
    8. 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.
    9. A. M. Spence, 1981. "The Learning Curve and Competition," Bell Journal of Economics, The RAND Corporation, vol. 12(1), pages 49-70, Spring.
    10. 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.
    11. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    12. 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.
    13. 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.
    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. 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 133-159, April.
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    Cited by:

    1. Jeremiah Harris & Ralph Siebert, 2015. "Driven by the Discount Factor: Impact of Mergers on Market Performance in the Semiconductor Industry," CESifo Working Paper Series 5199, CESifo Group Munich.
    2. Lambertini, Luca & Mantovani, Andrea, 2006. "Identifying reaction functions in differential oligopoly games," Mathematical Social Sciences, Elsevier, vol. 52(3), pages 252-271, December.
    3. Andrew Clarke, 2008. "Learning-by-Doing and Productivity Dynamics in Manufacturing Industries," Department of Economics - Working Papers Series 1032, The University of Melbourne.
    4. Ralph Siebert, 2016. "The Impact of Horizontal Mergers on Market Structure: Evidence from the Semiconductor Industry," CESifo Working Paper Series 5911, CESifo Group Munich.
    5. Siebert Ralph B, 2010. "Learning-by-Doing and Cannibalization Effects at Multi-Vintage Firms: Evidence from the Semiconductor Industry," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-32, May.
    6. repec:eee:indorg:v:53:y:2017:i:c:p:32-62 is not listed on IDEAS
    7. repec:eee:indorg:v:54:y:2017:i:c:p:89-124 is not listed on IDEAS
    8. 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.

    More about this item


    Oligopoly; dynamic games; semiconductor industry;

    JEL classification:

    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L63 - Industrial Organization - - Industry Studies: Manufacturing - - - Microelectronics; Computers; Communications Equipment
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games


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