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Distribution and Fluctuation of Firm Size in the Long-Run


  • W. Souma
  • H. Aoyama
  • L. Gruene


The paper studies empirically and analytically growth and fluctuation of firm size distribution. An empirical analysis is carried out on several data sets on firm size, with emphasis on one-time distribution as well as growth-rate probability distribution. Two well-known scaling laws, Pareto's law and Gibrat's law, are discussed. Some theoretical discussion on their relationship is presented. We also discuss to what extent there may exist economic mechanisms that produce an unequal firm size distribution in the long run. The mechanisms we study have been known in the economic literature since long. Yet, they have not been studied in the context of a dynamic decision problem of the firm. We allow for heterogeneity of firms with respect to certain characteristics. We then show that there are mechanisms at work which may generate a twin-peaked distribution of firm size in the long-run, which will then be tested empirically

Suggested Citation

  • W. Souma & H. Aoyama & L. Gruene, 2004. "Distribution and Fluctuation of Firm Size in the Long-Run," Computing in Economics and Finance 2004 92, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:92

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    References listed on IDEAS

    1. Finn E. Kydland & Edward C. Prescott, 1996. "The Computational Experiment: An Econometric Tool," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 69-85, Winter.
    2. Johann Peter Murmann & Thomas Brenner, 2003. "The Use of Simulations in Developing Robust Knowledge about Causal Processes: Methodological Considerations and an Application to Industrial Evolution," Computing in Economics and Finance 2003 66, Society for Computational Economics.
    3. Machlup, Fritz, 1978. "Methodology of Economics and Other Social Sciences," Elsevier Monographs, Elsevier, edition 1, number 9780124645509 edited by Shell, Karl.
    4. Franco Malerba & Luigi Orsenigo, 2002. "Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model," Industrial and Corporate Change, Oxford University Press, vol. 11(4), pages 667-703, August.
    5. Schwerin, Joachim & Werker, Claudia, 2003. "Learning innovation policy based on historical experience," Structural Change and Economic Dynamics, Elsevier, vol. 14(4), pages 385-404, December.
    6. Dominique Foray & Robin Cowan, 2002. "Evolutionary economics and the counterfactual threat: on the nature and role of counterfactual history as an empirical tool in economics," Journal of Evolutionary Economics, Springer, vol. 12(5), pages 539-562.
    7. Malerba, Franco, et al, 1999. "'History-Friendly' Models of Industry Evolution: The Computer Industry," Industrial and Corporate Change, Oxford University Press, vol. 8(1), pages 3-40, March.
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    More about this item


    Firm size; Pareto's law; Gibrat's law;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms

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