Gibrat's Law and Quantile Regressions: an Application to Firm Growth
Ivan Petrella () and
Emiliano Santoro ()
EMF Research Papers from Economic Modelling and Forecasting Group
The nexus between firm growth, size and age in U.S. manufacturing is examined through the lens of quantile regression models. This methodology allows us to overcome serious shortcomings entailed by linear regression models employed by much of the existing literature, unveiling a number of important properties. Size pushes both low and high performing firms towards the median rate of growth, while age is never advantageous, and more so as firms are relatively small and grow faster. These findings support theoretical generalizations of Gibrat's law that allow size to affect the variance of the growth process, but not its mean.
Keywords: firm growth; size; age; conditional quantiles JEL Classification Numbers: D22; L11; C21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eff, nep-ent and nep-tid
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https://warwick.ac.uk/fac/soc/wbs/subjects/emf/res ... ript_20122017_el.pdf
Journal Article: Gibrat’s law and quantile regressions: An application to firm growth (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:wrkemf:16
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