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

  • Elena Cefis

    (Università di Bergamo)

  • Luigi Orsenigo

    (Università Bocconi)

  • Matteo Ciccarelli

    (Universidad de Alicante)

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.

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File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2002-19.pdf
File Function: Fisrt version / Primera version, 2002
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Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2002-19.

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Length: 52 pages
Date of creation: Oct 2002
Date of revision:
Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2002-19
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