2014
DOI: 10.1016/j.physa.2013.11.027
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A model for scaling in firms’ size and growth rate distribution

Abstract: We introduce a simple agent-based model which allows us to analyze three stylized facts: a fat-tailed size distribution of companies, a 'tentshaped' growth rate distribution, the scaling relation of the growth rate variance with firm size, and the causality between them. This is achieved under the simple hypothesis that firms compete for a scarce quantity (either aggregate demand or workforce) which is allocated probabilistically. The model allows us to relate size and growth rate distributions. We compare the… Show more

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Cited by 10 publications
(3 citation statements)
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“…Other studies further find that a positive or negative relationship between the two variables does not necessarily affect the applicability of Gibrat's law ( 55 , 56 ). Recent studies have proposed that the validity of Gibrat's law varies with different sample sizes ( 57 ), years of observation ( 58 ). Based on previous studies, we consider Gibrat's law to investigate the economic growth rate of emerging countries.…”
Section: Methodsmentioning
confidence: 99%
“…Other studies further find that a positive or negative relationship between the two variables does not necessarily affect the applicability of Gibrat's law ( 55 , 56 ). Recent studies have proposed that the validity of Gibrat's law varies with different sample sizes ( 57 ), years of observation ( 58 ). Based on previous studies, we consider Gibrat's law to investigate the economic growth rate of emerging countries.…”
Section: Methodsmentioning
confidence: 99%
“…This can be evaluated using (8) and for ρ(n) the expression (5). For α = 0.5 and β = 0, this yields a upper incomplete Gamma function shown in Figure 4 and References [38][39][40]: Such 'tent-shaped' aggregate growth rate distributions are often observed for quantities that themselves follow a power-law [22,25,27,29,41]. The tent shape is the sample average, but not the expectation for a given urn, as other models for it presume [28,42].…”
Section: Aggregate Growth Rate Distribution Of the Stable Size Processmentioning
confidence: 99%
“…This can be evaluated using (8) and for ρ(n) the expression (5). For α = 0.5 and β = 0, yields a upper incomplete Gamma function shown in figure 2 and [19,20]: G(g) ∝ Γ 0, 1 2 n 0 (g − 1) 2 . Such 'tent-shaped' growth rate distributions are often observed for quantities that themselves follow a power-law [2,10,15,23,26,28,30].…”
Section: Growth Rate Probability Densitymentioning
confidence: 99%