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From Gibrat'S Legacy To Gibrat'S Fallacy. A Bayesian Approach To Study The Growth Of Firms

Author

Listed:
  • Elena Cefis

    (Università di Bergamo)

  • Luigi Orsenigo

    (Università Bocconi)

  • Matteo Ciccarelli

    (Universidad de Alicante)

Abstract

We aim at testing Gibrat's Law, a building block of the corporate growth dynamics. Using a Bayesian statistical framework that nests previous approaches, we provide evidence against Gibrat's law on average, within or across industries. Notwithstanding, data show only weak evidence of mean reversion, i.e. initial larger firms do not grow relatively slower than smaller firms. Moreover, differences in growth rates and in size steady state are persistent and firm-specific. Previous results confirming Gibrat's argument are likely to be incorrect being based on models that do not exploit appropriately all information contained in a panel data set.

Suggested Citation

  • Elena Cefis & Luigi Orsenigo & Matteo Ciccarelli, 2002. "From Gibrat'S Legacy To Gibrat'S Fallacy. A Bayesian Approach To Study The Growth Of Firms," Working Papers. Serie AD 2002-19, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    • Handle: RePEc:ivi:wpasad:2002-19
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    References listed on IDEAS

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    Cited by:

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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