Using the Survivor Technique to Estimate Returns to Scale and Optimum Firm Size

2003 ◽  
Vol 3 (1) ◽  
Author(s):  
James N Giordano
Keyword(s):  
Size Distribution ◽  
Firm Size ◽  
Firm Growth ◽  
Returns To Scale ◽  
Gibrat’S Law ◽  
Individual Firm ◽  
Gibrat's Law ◽  
Alternative Measures

Abstract The survivor technique for estimating returns to scale and optimum firm size has generated a slow but steady literature since its 1958 pilot presentation by George Stigler. This article (1) integrates advances in its application into a complete demonstration of how the technique works, (2) distinguishes a survivor analysis from the related but different analyses of individual firm growth and size distribution as addressed, for example, by Gibrat's Law of Proportionate Effect, (3) surveys a few exemplary survivor analyses, highlighting their alternative measures of scale and survival, and (4) unifies the scattered discussion of criticisms and qualifications that surround the technique. Accordingly, this essay seeks to reposition the survivor technique as a viable statistical option for research on those industries which meet its criteria.

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.

scholarly journals The Relationship Between Firm Size and Firm Growth: The Case of the Czech Republic

2015 ◽  
Vol 63 (5) ◽  
pp. 1639-1644 ◽  
Author(s):  
Roman Fiala ◽  
Veronika Hedija
Keyword(s):  
Czech Republic ◽  
Firm Size ◽  
Small Firms ◽  
Firm Growth ◽  
Autoregressive Process ◽  
The Czech Republic ◽  
Gibrat’S Law ◽  
First Order ◽  
Gibrat's Law ◽  
The Relationship

This paper deals with the investigation of the relationship between firm size and firm in the Czech Republic during 2007–2012. The study aims to examine to what extent the confirmation or rejection of Gibrat’s law depends on the indicator of firm size. For measuring firm size we use three indicators: revenues, number of employees and total assets. The study uses data collected from the database Albertina CZ Gold Edition. Final dataset includes the data about more than 35,000 firms. The validity of Gibrat’s law was tested with the help of linear regression model with first-order autoregressive process. Gibrat’s law is rejected for all three indicators of firm size. Hence, the selected indicator of firm size is not proved to be important factor in verification of Gibrat’s law validity. It is also found out that the small firms in profit industries (A-N according to CZ-NACE classification) grow faster than their larger counterparts in the Czech Republic.

Is Gibrat’s law valid for travel agencies and tour operators? Evidence from the Visegrad group countries

2019 ◽  
Vol 24 (3) ◽  
pp. 447-465
Author(s):  
Veronika Hedija ◽  
Roman Fiala
Keyword(s):  
Firm Size ◽  
Firm Growth ◽  
Ordinary Least Squares ◽  
Gibrat’S Law ◽  
Travel Agents ◽  
Gibrat's Law ◽  
Size Growth ◽  
Minimum Efficient Scale ◽  
Relationship Of ◽  
Visegrad Group

The purpose of this paper is to investigate the validity of Gibrat’s law for a sample of travel agents from the Visegrad group (V4) countries and to identify the size-growth relationship. Using a linear auto-regressive model and ordinary least squares estimator, we rejected the validity of Gibrat’s law in the V4 countries (except Poland, where the results were mixed). The smallest firms tend to grow faster than their larger counterparts. Using quantile regression models, we concluded that the size-growth link differed depending on actual firm size. Before reaching minimum efficient scale (MES), there is a positive relationship between firm growth and firm size. This relationship is negative after reaching MES: the smaller firms grow faster than bigger ones. Gibrat’s law tends to be valid in the population of firms that have reached MES. This shows that economies and diseconomies of scale could play a significant role in explaining the size-growth relationship of travel agents.

scholarly journals Firm size and economic concentration: An analysis from a lognormal expansion

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254487
Author(s):  
Lina M. Cortés ◽  
Juan M. Lozada ◽  
Javier Perote
Keyword(s):  
Firm Size ◽  
Lognormal Distribution ◽  
Firm Growth ◽  
Dynamic Panel Data ◽  
Firm Characteristics ◽  
Dynamic Panel ◽  
Gibrat’S Law ◽  
Gibrat's Law ◽  
Distribution Tail ◽  
Economic Concentration

This paper studies the distribution of the firm size for the Colombian economy showing evidence against the Gibrat’s law, which assumes a stable lognormal distribution. On the contrary, we propose a lognormal expansion that captures deviations from the lognormal distribution with additional terms that allow a better fit at the upper distribution tail, which is overestimated according to the lognormal distribution. As a consequence, concentration indexes should be addressed consistently with the lognormal expansion. Through a dynamic panel data approach, we also show that firm growth is persistent and highly dependent on firm characteristics, including size, age, and leverage −these results neglect Gibrat’s law for the Colombian case.

Gibrat's Law and the firm size/firm growth relationship in italian small scale services

2003 ◽  
Vol 102 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Roberta Piergiovanni ◽  
Enrico Santarelli ◽  
Luuk Klomp ◽  
A. Roy Thurik
Keyword(s):  
Firm Size ◽  
Firm Growth ◽  
Small Scale ◽  
Gibrat’S Law ◽  
Gibrat's Law ◽  
Size Firm

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.

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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