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The Role of Technological and Industrial Heterogeneity In Technology Diffusion: a Markovian Approach

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  • Adela Luque

Abstract

Recent empirical studies have established the importance of intra and inter-industry heterogeneity in investment in innovation and other outcomes. This paper examines the role of industry and technology heterogeneity in the diffusion of advanced manufacturing technologies from a simple Markovian approach. Using the Maximum Entropy estimator, I estimate transition probabilities and corresponding half-lives, look for outliers in technology and industry diffusion patterns, and try to find explanations of their unusual behavior in idiosyncratic technology and industry characteristics. A consistent industry-level pattern that emerged is one that relates consumer demand and production processes. It seems that in industries where hand-made products are a sign of quality to the customer, technology spreads very slowly. On the other hand, in industries where demand for sophisticated, high-precision goods is high or in industries where demand-driven product specifications vary quite rapidly over relatively short periods of time, advanced technologies diffuse much more rapidly.

Suggested Citation

  • Adela Luque, 2003. "The Role of Technological and Industrial Heterogeneity In Technology Diffusion: a Markovian Approach," Working Papers 03-07, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:03-07
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    References listed on IDEAS

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    1. Jennifer F. Reinganum, 1981. "Market Structure and the Diffusion of New Technology," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 618-624, Autumn.
    2. Rosenberg, Nathan, 1976. "On Technological Expectations," Economic Journal, Royal Economic Society, vol. 86(343), pages 523-535, September.
    3. Drew Fudenberg & Jean Tirole, 1985. "Preemption and Rent Equalization in the Adoption of New Technology," Review of Economic Studies, Oxford University Press, vol. 52(3), pages 383-401.
    4. Pindyck, Robert S, 1991. "Irreversibility, Uncertainty, and Investment," Journal of Economic Literature, American Economic Association, vol. 29(3), pages 1110-1148, September.
    5. Weiss, Allen M, 1994. "The Effects of Expectations on Technology Adoption: Some Empirical Evidence," Journal of Industrial Economics, Wiley Blackwell, vol. 42(4), pages 341-360, December.
    6. Mark Doms & Timothy Dunne & Kenneth R. Troske, 1997. "Workers, Wages, and Technology," The Quarterly Journal of Economics, Oxford University Press, vol. 112(1), pages 253-290.
    7. Stoneman, P, 1981. "Intra-Firm Diffusion, Bayesian Learning and Profitability," Economic Journal, Royal Economic Society, vol. 91(362), pages 375-388, June.
    8. Avinash Dixit, 1992. "Investment and Hysteresis," Journal of Economic Perspectives, American Economic Association, vol. 6(1), pages 107-132, Winter.
    9. Ernst R. Berndt & Catherine J. Morrison & Larry S. Rosenblum, 1992. "High-Tech Capital Formation and Labor Composition in U.S. Manufacturing Industries: An Exploratory Analysis," NBER Working Papers 4010, National Bureau of Economic Research, Inc.
    10. Cukierman, Alex, 1980. "The Effects of Uncertainty on Investment under Risk Neutrality with Endogenous Information," Journal of Political Economy, University of Chicago Press, vol. 88(3), pages 462-475, June.
    11. Diansheng Dong & Atanu Saha, 1998. "He came, he saw, (and) he waited: an empirical analysis of inertia in technology adoption," Applied Economics, Taylor & Francis Journals, vol. 30(7), pages 893-905.
    12. Steve J. Davis & John Haltiwanger, 1991. "Wage Dispersion Between and Within U.S. Manufacturing Plants, 1963-1986," NBER Working Papers 3722, National Bureau of Economic Research, Inc.
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    Keywords

    CES; economic; research; micro; data; microdata; chief; economist;

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