The Growth of Firms
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The Growth of Firms

A Survey of Theories and Empirical Evidence

Alex Coad

Much progress has been made in empirical research into firm growth in recent decades due to factors such as the availability of detailed longitudinal datasets, more powerful computers and new econometric techniques. This book provides an up-to-date catalogue of empirical work, as well as a coherent theoretical structure within which these new results can be interpreted and understood. It brings together a large body of recent research on firm growth from a multidisciplinary perspective, providing an up-to-date synthesis of stylized facts and empirical regularities. Numerous empirical findings and theories of firm growth are also surveyed and compared in order to evaluate their validity.
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Chapter 4: Gibrat’s Law

Alex Coad


Gibrat’s law continues to receive a huge amount of attention in the empirical industrial organization literature, more than 75 years after the seminal publication of Gibrat (1931). We begin by presenting the ‘Law’, and then review some of the related empirical literature. We do not attempt to provide an exhaustive survey of the literature on Gibrat’s law, because the number of relevant studies is indeed very large. (For other reviews of empirical tests of Gibrat’s law, the reader is referred to the survey by Lotti et al., 2003); for a survey of how Gibrat’s law holds for the services sector see Audretsch et al., 2004.) Instead, we try to provide an overview of the essential results. We investigate how expected growth rates and growth rate variance are influenced by firm size, and also investigate the possible existence of patterns of serial correlation in firm growth. 4.1 GIBRAT’S MODEL Let us briefly return to Gibrat’s model of firm growth presented earlier in section 2.2. As before, we define xt to be the size of a firm at time t, and let et be random variable representing an idiosyncratic, multiplicative growth shock over the period t – 1 to t. We have xt – xt−1 5 etxt−1 which can be developed to obtain xt 5 (1 1 et)xt−1 5 x0(1 1 e1)(1 1 e2). . .(1 1 et) (4.2) (4.1) It is then possible to take logarithms in order to approximate log(1 1 et) by et to obtain1...

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