Gibrat's Law and Quantile Regressions: an Application to Firm Growth
Roberta Distante (),
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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
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)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:wrk:wrkemf:16
Access Statistics for this paper
More papers in EMF Research Papers from Economic Modelling and Forecasting Group Contact information at EDIRC.
Bibliographic data for series maintained by Ana Galvão ().