Deviations from Gibrat’s Law and Implications for Generalized Zipf’s Laws

2009 ◽  
pp. 73-95
Author(s):  
Alexander Saichev ◽  
Yannick Malevergne ◽  
Didier Sornette
Keyword(s):  
Gibrat’S Law ◽  
Gibrat's Law

Profitability and Firm Size–Growth Relationship in Construction Companies in Malaysia From 2003 to 2010

2014 ◽  
Vol 17 (03) ◽  
pp. 1450014 ◽  
Author(s):  
Devinaga Rasiah ◽  
David Yoon Kin Tong ◽  
Peong Kwee Kim
Keyword(s):  
Growth Rate ◽  
Firm Size ◽  
Growth Process ◽  
Secondary Data ◽  
Annual Reports ◽  
Basic Study ◽  
Gibrat’S Law ◽  
Construction Companies ◽  
Gibrat's Law ◽  
Size Growth

In this study, we intended to examine empirically how a firm's profitability performance would impact its growth process and the inference for Gibrat's Law. The basic study looks at small, medium and large firms' tendency to grow when their internally generated profits are high. The sample is 124 construction companies listed from years 2003 to 2010 at BURSA Malaysia. Data used is secondary data collected from BURSA Malaysia and annual reports. The result indicated that "growth" contributed significantly to profitability in both small and medium-sized construction companies, but was not significant in large companies. Thus, hypothesis two was supported. This study supports Gibrat's Law, showing that size and growth rate are independent.

scholarly journals Defending Gibrat’s Law as a long-run regularity

2007 ◽  
Vol 32 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Francesca Lotti ◽  
Enrico Santarelli ◽  
Marco Vivarelli
Keyword(s):  
Gibrat’S Law ◽  
Gibrat's Law ◽  
Long Run

Growth and Firm Size Distribution

2016 ◽  
Vol 14 (2) ◽  
pp. 61-73
Author(s):  
Wei Zhang ◽  
Yan-Chun Zhu ◽  
Jian-Bo Wen ◽  
Yi-Jie Zhuang
Keyword(s):  
Size Distribution ◽  
Firm Size ◽  
Zipf’S Law ◽  
Growth Path ◽  
Firm Size Distribution ◽  
Zipf's Law ◽  
Gibrat’S Law ◽  
Gibrat's Law ◽  
Quantile Regression Method ◽  
The Relationship

Studies on the firm's size distribution (FSD) can set a good foundation to know about the growth path and mechanism of e-commerce firms. The purpose of this paper is to understand features of the China's listed e-commerce firms by testing Gibrat's law and Zipf's law within the Internet sectors. From a macroscopic perspective, with the approach of OLS estimation, Zipf's coefficient of the FSD is calculated to test whether Zipf's law holds. From a microscopic perspective, the relationship between e-commerce firm size and growth is explored by quantile regression method. The results indicate that from 2005 to 2014, Zipf's law cannot be rejected, with the relationship changing over time, Gibrat's law holds partly. It implies that competition status among China's e-commerce firms becomes more stable.

Human capital divergence and the size distribution of cities: Is Gibrat’s law obsolete?

Urban Studies ◽  
2020 ◽  
pp. 004209802095309
Author(s):  
Daniel Broxterman ◽  
Anthony Yezer
Keyword(s):  
Human Capital ◽  
Population Growth ◽  
Size Distribution ◽  
Growth Models ◽  
Theoretical Models ◽  
Empirical Literature ◽  
Skilled Workers ◽  
Gibrat’S Law ◽  
Gibrat's Law ◽  
Growth Hypothesis

This article studies how the changing geographic distribution of skilled workers in the US affects theoretical models that use Gibrat’s law to explain the size distribution of cities. In the empirical literature, a divergence hypothesis holds that college share increases faster in cities where college share is larger, and a growth hypothesis maintains that the rate of city population growth is also directly related to initial college share. Examining the divergence hypothesis, the classic test for Gibrat’s law is shown to be a test for [Formula: see text]-convergence. Testing shows that there has been absolute, not relative, divergence in human capital since the 1970s. However, the combination of even absolute divergence and the growth hypothesis is shown to violate the condition that a city’s population growth is independent of its size. Additional testing finds that the relation between college share and city growth is concave rather than monotonic. These results imply that stochastic growth models can survive the challenge posed by divergence in the distribution of human capital.

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