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Gibrat’s law and quantile regressions: An application to firm growth

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  • Distante, Roberta
  • Petrella, Ivan
  • Santoro, Emiliano

Abstract

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 (Cordoba, 2008).

Suggested Citation

  • Distante, Roberta & Petrella, Ivan & Santoro, Emiliano, 2018. "Gibrat’s law and quantile regressions: An application to firm growth," Economics Letters, Elsevier, vol. 164(C), pages 5-9.
    • Handle: RePEc:eee:ecolet:v:164:y:2018:i:c:p:5-9
    DOI: 10.1016/j.econlet.2017.12.028
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    Cited by:

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    5. Christos Axioglou & Nicos Christodoulakis, 2019. "Which firms survive in a crisis? Corporate dynamics in Greece 2001-2014," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 133, Hellenic Observatory, LSE.
    6. Marco Guerzoni & Luigi Riso & Marco Vivarelli, 2023. "Was Robert Gibrat right? A test based on the graphical model methodology," DISCE - Quaderni del Dipartimento di Politica Economica dipe0031, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    7. Marco Guerzoni & Luigi Riso & Marco Vivarelli, 2023. "The Law of Proportionate Effect: A test based on the graphical model methodology," Working Papers 514, University of Milano-Bicocca, Department of Economics.
    8. George E. Halkos & Michael L. Polemis, 2019. "The impact of market structure on environmental efficiency in the United States: A quantile approach," Business Strategy and the Environment, Wiley Blackwell, vol. 28(1), pages 127-142, January.
    9. Besstremyannaya, Galina & Dasher, Richard & Golovan, Sergei, 2022. "Quantifying heterogeneity in the relationship between R&D intensity and growth at innovative Japanese firms: A quantile regression approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 27-45.
    10. Polemis, Michael, 2018. "Personality traits as an engine of knowledge: A quantile regression approach," MPRA Paper 88614, University Library of Munich, Germany.
    11. Lee Mihye, 2023. "Determinants of Firm-Level Growth: Lessons from the Czech Republic, Hungary, and Poland," South East European Journal of Economics and Business, Sciendo, vol. 18(1), pages 46-57, June.
    12. Anh Tuan Bui & Susan Lambert & Tung Duc Phung & Giao Reynolds, 2021. "The Impact of Business Obstacles on Firm Growth and Job Stability in East Asia and Pacific Nations," Sustainability, MDPI, vol. 13(19), pages 1-19, October.
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    14. Polemis, Michael, 2024. "Are Cartels Forever? Global Evidence Using Quantile Regression Analysis," MPRA Paper 120534, University Library of Munich, Germany.
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    More about this item

    Keywords

    Firm growth; Size; Age; Conditional quantiles;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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