A Generalized Preferential Attachment Model for Business Firms Growth Rates: I. Empirical Evidence

Fabio Pammolli (), Dongfeng Fu, Sergey V. Buldyrev, Massimo Riccaboni (), Kaushik Matia, Kazuko Yamasaki and H. Eugene Stanley

MPRA Paper from University Library of Munich, Germany

Abstract: We introduce a model of proportional growth to explain the distribution P(g) of business firm growth rates. The model predicts that P(g) is Laplace in the central part and depicts an asymptotic power-law behavior in the tails with an exponent ζ = 3. Because of data limitations, previous studies in this field have been focusing exclusively on the Laplace shape of the body of the distribution. We test the model at different levels of aggregation in the economy, from products, to firms, to countries, and we find that the its predictions are in good agreement with empirical evidence on both growth distributions and size-variance relationships.

Keywords: Gibrat Law; Firm Growth; Size Distribution (search for similar items in EconPapers)
JEL-codes: D21 E01 E17 L00 L16 L25 L60 L65 (search for similar items in EconPapers)
Date: 2006-08-31
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in The European Physical Journal B 2.57(2007): pp. 127-130

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/15983/1/MPRA_paper_15983.pdf original version (application/pdf)

Related works:
Working Paper: A Generalized Preferential Attachment Model for Business Firms Growth Rates: I. Empirical Evidence (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:15983

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().