Gibrat’s law is a referent model of corporate growth dynamics. This paper employs Bayesian panel data methods to test Gibrat’s law and its implications. Using a Pharmaceutical Industry Database (1987–1998), we find evidence against Gibrat’s law on average, within or across industries. Estimated steady states differ across firms, and firm sizes and growth rates do not converge within the same industry to a common limiting distribution. There is only weak evidence of mean reversion: initial larger firms do not grow relatively slower than smaller firms. Differences in growth rates and in steady state size are persistent and firm-specific, rather than size-specific.
Testing Gibrat's Legacy: A Bayesian Approach to Study the Growth of Firms
CEFIS, Elena;
2007-01-01
Abstract
Gibrat’s law is a referent model of corporate growth dynamics. This paper employs Bayesian panel data methods to test Gibrat’s law and its implications. Using a Pharmaceutical Industry Database (1987–1998), we find evidence against Gibrat’s law on average, within or across industries. Estimated steady states differ across firms, and firm sizes and growth rates do not converge within the same industry to a common limiting distribution. There is only weak evidence of mean reversion: initial larger firms do not grow relatively slower than smaller firms. Differences in growth rates and in steady state size are persistent and firm-specific, rather than size-specific.Pubblicazioni consigliate
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