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A tale of two tails: Do Power Law and Lognormal models fit firm-size distributions in the mid-Victorian era?

Piero Montebruno, Robert J. Bennett, Carry van Lieshout and Harry Smith

Physica A: Statistical Mechanics and its Applications, 2019, vol. 523, issue C, 858-875

Abstract: The paper explores the frequency and size distributions of firm-size in a novel dataset for the mid-Victorian era from a recent extraction of the England and Wales population censuses of 1851, 1861, 1871, and 1881. The paper contrasts the hypothesis of the Power Laws against the Lognormal model for the tails of the distributions using maximum likelihood estimation, log likelihood ratio, clipped sample coefficient of variation UMPU-Wilks test, Kolmogorov–Smirnov statistic, among other state-of-the-art statistical methods. Our results show that the Power Law hypothesis is accepted for the size distribution for the years 1851 and 1861, while 1871 is marginally non-significant, but for 1881 the test is inconclusive. The paper discusses the process that generates these distributions citing recent literature that shows how after adding an i.i.d. noise to the Gibrat’s multiplicative model one can recreate a Power Law behaviour. Overall, the paper provides, describes and statistically tests for the very first time a unique historical dataset confirming that the tails of the distributions at least for 1851 and 1861 follow a Pareto model and that the Lognormal model is firmly rejected.

Keywords: Power Law; Pareto distribution; Zipf’s law; Lognormal distribution; Gibrat’s law; Firm size; Victorian census (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.physa.2019.02.054

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