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The Dynamics Of Productivity In The Telecommunications Equipment Industry

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  • George S Olley
  • Ariel Pakes

Abstract

Technological change and deregulation have caused a major restructuring of the telecommunications equipment industry over the last two decades. We estimate the parameters of a production function for the equipment industry and then use those estimates to analyze the evolution of plant-level productivity over this period. The restructuring involved significant entry and exit and large changes in the sizes of incumbents. Since firms choices on whether to liquidate and the on the quantities of inputs demanded should they continue depend on their productivity, we develop an estimation algorithm that takes into account the relationship between productivity on the one hand, and both input demand and survival on the other. The algorithm is guided by a dynamic equilibrium model that generates the exit and input demand equations needed to correct for the simultaneity and selection problems. A fully parametric estimation algorithm based on these decision rules would be both computationally burdensome and require a host of auxiliary assumptions. So we develop a semiparametric technique which is both consistent with a quite general version of the theoretical framework and easy to use. The algorithm produces markedly different estimates of both production function parameters and of productivity movements than traditional estimation procedures. We find an increase in the rate of industry productivity growth after deregulation. This in spite of the fact that there was no increase in the average of the plants' rates of productivity growth, and there was actually a fall in our index of the efficiency of the allocation of variable factors conditional on the existing distribution of fixed factors. Deregulation was, however, followed by a reallocation of capital towards more productive establishments (by a down sizing, often shutdown, of unproductive plants and by a disproportionate growth of productive establishments) which more than offset the other factors' negative impacts on aggregate productivity.

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Bibliographic Info

Paper provided by Center for Economic Studies, U.S. Census Bureau in its series Working Papers with number 92-2.

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Date of creation: Feb 1992
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Handle: RePEc:cen:wpaper:92-2

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

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References

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  1. Dunne, T. & Roberts, M.J. & Samuelson, L., 1988. "Pattenrs Of Firm Entry And Exit In U.S. Manufacturing Industries," Papers 1-88-2, Pennsylvania State - Department of Economics.
  2. Donald W.K. Andrews, 1989. "Asymptotics for Semiparametric Econometric Models: II. Stochastic Equicontinuity and Nonparametric Kernel Estimation," Cowles Foundation Discussion Papers 909R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1990.
  3. Newey, Whitney K., 1994. "Series Estimation of Regression Functionals," Econometric Theory, Cambridge University Press, vol. 10(01), pages 1-28, March.
  4. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  5. Ahn, H. & Powell, J.L., 1990. "Semiparametric Estimation Of Censored Selection Models With A Nonparametric Selection Mechanism," Working papers 90-33, Wisconsin Madison - Social Systems.
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  8. Newey, Whitney K, 1994. "The Asymptotic Variance of Semiparametric Estimators," Econometrica, Econometric Society, vol. 62(6), pages 1349-82, November.
  9. Steven J. Davis & John Haltiwanger, 1990. "Gross Job Creation and Destruction: Microeconomic Evidence and Macroeconomic Implications," NBER Chapters, in: NBER Macroeconomics Annual 1990, Volume 5, pages 123-186 National Bureau of Economic Research, Inc.
  10. Jovanovic, Boyan, 1982. "Selection and the Evolution of Industry," Econometrica, Econometric Society, vol. 50(3), pages 649-70, May.
  11. Robert H Mcguckin & George A Pascoe, 1988. "The Longitudinal Research Database (LRD): Status And Research Possibilities," Working Papers 88-2, Center for Economic Studies, U.S. Census Bureau.
  12. Ariel Pakes & Steven Olley, 1994. "A Limit Theorem for a Smooth Class of Semiparametric Estimators," Cowles Foundation Discussion Papers 1066, Cowles Foundation for Research in Economics, Yale University.
  13. Zvi Griliches, 1967. "Production Functions in Manufacturing: Some Preliminary Results," NBER Chapters, in: The Theory and Empirical Analysis of Production, pages 275-340 National Bureau of Economic Research, Inc.
  14. Andrews, Donald W K, 1991. "Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models," Econometrica, Econometric Society, vol. 59(2), pages 307-45, March.
  15. Richard Ericson & Ariel Pakes, 1992. "An Alternative Theory of Firm and Industry Dynamics," Cowles Foundation Discussion Papers 1041, Cowles Foundation for Research in Economics, Yale University.
  16. Newey, W.K., 1991. "Consistency and Asymptotic Normality of Nonparametric Projection Estimators," Working papers 584, Massachusetts Institute of Technology (MIT), Department of Economics.
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