Evidence of a Threshold Size for Norwegian Campsites and Its Dynamic Growth Process Implications—Does Gibrat’s Law Hold?

Although campsites are an important segment of the tourist sector, few applied articles have analyzed their growth path and tested Gibrat’s Law for firms within this industry. This knowledge can be of importance to the authorities when analyzing the regional impacts of growth in this sector. With government statistics from the last decade, we use a GMM framework to test the stricter version of Gibrat’s Law, which consist of three parts: the campsites’ growth trend, how they carry over success and failure, and how volatile their size is. The first and third part are rejected for Norwegian campsites, leading to a rejection of Gibrat’s Law. To see if firms of different sizes follow different dynamics, we split the sample in three parts. Here, we find evidence of a threshold size, as large campsites follow a fundamentally different dynamic than small and medium campsites. Specifically, large campsites gain no stability in revenue by further increases in size, whereas they carry over success/failure across years. The opposite is true for the rest of the sector. Gibrat’s Law is rejected on at least one count for each of the sub-samples. Lastly, we supplement the analysis with economy-wide and firm-specific variables to test further hypotheses.

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

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.

Do Firm Growth Models Work in Service Industries in Developing Economies? An Investigation of the Relationship Between Firms’ Growth, Size and Age

Growth models such as Gibrat’s law and Jovanovic’s theory that examine the relationship between the firms’ growth, age and size have either been tested on data from developed economies or from the manufacturing sectors in developing economies. This study checks the suitability of these models in service sectors in developing economies as service sectors have distinct characteristics and developing economies such as India are heavily dependent on this sector. The current study considers three major service sectors contributing to India’s economy vis-à-vis financial services, information technology and real estate for the period 2002–2005. We observed that during 2002–2005, India’s economy was stable without wide fluctuations in economic performance, such as gross domestic product, unemployment or inflation. These sectors not only had a significant impact on economic growth but also had comprehensive microeconomic data. Our results negate both Gibrat’s law and Jovanovic’s theory. We argue that service sectors which are knowledge-intensive will experience different growth patterns compared to manufacturing sectors. We find a definite and significant relationship between firms’ growth and their size and age. Also, we find concluding evidence that younger firms up to 10 years of age struggle a lot more than older firms in the Indian service sector. JEL: D20, D21, D22, D02

Analysis of the Operation of Gibrat’s Law in Cities of Russia

Foreign researchers are testing Gibrat’s law on the example of firms, regions and countries. The importance of empirical confirmation of this law lies in the fact that it allows us to determine whether the population of a city, region or country as a whole has a common growth path and whether there is single size dependence between them. The relevance of this study is determined by the need to expand the indicators to assess the growth of cities using Gibrat’s law in modern Russian conditions. The purpose of the article is to analyze the feasibility of Gibrat’s law in Russian cities by indicators: population of the city, population density in the city, average annual number of employees in enterprises in the city, average monthly wage in the city, number of enterprises and organizations in the city, as well as to determine the appropriateness of using this law for urban systems of Russia. In the Ural, Siberian and Far Eastern federal districts (2009–2016), in the North-Western, Volga, Siberian and Far Eastern federal districts (2016–2018), the growth rate of cities does not depend on their initial size. Gibrat’s law was confirmed for the following indicators: population density in a city in 2009–2016 in the Siberian Federal District, in 2016–2018 in all federal districts, except for the North Caucasian Federal District; average annual number of employees in a city in the Southern (2003–2009, 2009–2016), Ural (2009–2016), Siberian (2009–2016), Northwestern (2016–2018), North Caucasian (2016–2018) and Far Eastern (2016–2018) federal districts; average monthly salary in the cities of the Siberian Federal District (2009–2016), in the Central, Northwestern and Ural Federal Districts (2016–2018); number of enterprises and organizations in the city in the Southern Federal District (2009–2016), in the North Caucasian, Volga, Ural and Siberian federal districts (2016–2018).

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

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.