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.

On the Effectiveness of Growth- Enhancing Policies in a Model of Growth Without Scale Effects

2002 ◽  
Vol 3 (3) ◽  
pp. 339-346 ◽  
Author(s):  
Lutz G. Arnold
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Human Capital ◽  
Population Growth ◽  
Capital Accumulation ◽  
Scale Effects ◽  
Growth Models ◽  
Empirical Studies ◽  
Human Capital Accumulation ◽  
Accelerate Growth

Abstract Standard R&D growth models have two disturbing properties: the presence of scale effects (i.e., the prediction that larger economies grow faster) and the implication that there is a multitude of growth-enhancing policies. Recent models of growth without scale effects, such as Segerstrom's (1998), not only remove the counterfactual scale effect, but also imply that the growth rate does not react to any kind of economic policy. They share a different disturbing property, however: economic growth depends positively on population growth, and the economy cannot grow in the absence of population growth. The present paper integrates human capital accumulation into Segerstrom's (1998) model of growth without scale effects. Consistent with many empirical studies, growth is positively related not to population growth, but to investment in human capital. And there is one way to accelerate growth: subsidizing education.

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.

On Pareto’s law and the determinants of Pareto exponents

2004 ◽  
Author(s):  
William J. Reed
Keyword(s):  
Income Distribution ◽  
Stochastic Model ◽  
Population Growth ◽  
Gibrat’S Law ◽  
Income Distributions ◽  
Basic Assumptions ◽  
Rate Analysis ◽  
Gibrat's Law ◽  
Pareto Exponent ◽  
Pareto’S Law

A stochastic model for the generation of observed income distributions is used to provide an explanation for the Pareto law of incomes. The basic assumptions of the model are that the evolution of individual incomes follows Gibrat's law and that the population or workforce is growing at a fixed (probabilistic) rate. Analysis of the model suggests that Paretian behaviour can occur in either or both tails of an income distribution. It is shown that the magnitude of the upper-tail Pareto exponent depends on the interaction between the distribution of the growth in incomes and the growth in the size of the earning population. In particular a small Pareto exponent can be expected to occur for a population exhibiting fast or highly variable growth in incomes coupled with relatively slow population growth.

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

2021 ◽  
pp. 026010792198991
Author(s):  
Boby Chaitanya Villari ◽  
Balaji Subramanian ◽  
Piyush Kumar ◽  
Pradeep Kumar Hota
Keyword(s):  
Firm Growth ◽  
Developing Economies ◽  
Growth Models ◽  
Growth Patterns ◽  
Service Industries ◽  
Gibrat’S Law ◽  
Gibrat's Law ◽  
Manufacturing Sectors ◽  
Service Sectors ◽  
The Relationship

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

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.

scholarly journals Investigation of urban regularities for Croatia in the period from 1857 to 2011

2020 ◽  
Vol 71 (4) ◽  
pp. 307-330
Author(s):  
Hrvoje Jošić ◽  
Berislav Žmuk
Keyword(s):  
Size Distribution ◽  
Unit Root ◽  
Urban Economics ◽  
Unit Roots ◽  
City Size ◽  
Zipf’S Law ◽  
Urban Hierarchy ◽  
Zipf's Law ◽  
Gibrat’S Law ◽  
Gibrat's Law

Two main regularities in the field of urban economics are Zipf’s law and Gibrat’s law. Zipf’s law states that distribution of largest cities should obey the Pareto rank-size distribution while Gibrat’s law states that proportionate growth of cities is independent of its size. These two laws are interconnected and therefore are often considered together. The objective of this paper is the investigation of urban regularities for Croatia in the period from 1857 to 2011. In order to estimate and evaluate the structure of Croatian urban hierarchy, Pareto or Zipf’s coefficients are calculated. The results have shown that the coefficient values for the largest settlements in different years are close to one, indicating that the Croatian urban hierarchy system follows the rank-size distribution and therefore obeys Zipf's law. The independence of city growth regarding the city size is tested using penal unit roots. Results for Gibrat's law testing using panel unit root tests have shown that there is a presence of unit root in growth of settlements therefore leading to the acceptance of Gibrat’s law.

Gibrat's Law for (All) Cities: Reply

2009 ◽  
Vol 99 (4) ◽  
pp. 1676-1683 ◽  
Author(s):  
Jan Eeckhout
Keyword(s):  
Size Distribution ◽  
City Size ◽  
Visual Interpretation ◽  
Gibrat’S Law ◽  
Gibrat's Law ◽  
Lognormal Size Distribution ◽  
City Size Distribution ◽  
Distribution Tail ◽  
The City

This reply refutes the objection raised by Levy (2009) about the fit of the upper tail of the city size distribution in Eeckhout (2004). I show that the method on which his conclusion is based is unsubstantiated. The visual interpretation of the fit on log-log plots is misleading. In addition, the methodology used to estimate a truncated subsample of the distribution while testing its significance against a distribution with prespecified parameters is ill-founded. The main conclusion is that Gibrat's law holds: city sizes follow proportionate growth, thus giving rise to a lognormal size distribution, tail included. (JEL R11, R12, R23)

Welfare Reform and the Labor Market

2018 ◽  
Vol 10 (1) ◽  
pp. 347-381 ◽  
Author(s):  
Marc K. Chan ◽  
Robert Moffitt
Keyword(s):  
Human Capital ◽  
Welfare Reform ◽  
Income Tax ◽  
Theoretical Models ◽  
Empirical Literature ◽  
Negative Income ◽  
Different Types ◽  
Supply Model ◽  
Negative Income Tax ◽  
Welfare Reforms

This article reviews the basic theoretical models that are appropriate for analyzing different types of welfare reforms, as well as the related empirical literature. We first present the canonical labor supply model of a classical welfare program and then extend this basic framework to include in-kind transfers, incomplete take-up, human capital, preference persistence, and borrowing and saving. The empirical literature on these models is presented. The negative income tax, earnings subsidies, US welfare reforms with features that differ from those in other countries, and childcare reforms are then surveyed in terms of both the theoretical models and the empirical literature surrounding each.

Gibrat's Law for (All) Cities: Comment

2009 ◽  
Vol 99 (4) ◽  
pp. 1672-1675 ◽  
Author(s):  
Moshe Levy
Keyword(s):  
Income Distribution ◽  
Size Distribution ◽  
Power Law ◽  
Empirical Distribution ◽  
Wealth Distribution ◽  
City Size ◽  
Power Law Distribution ◽  
Gibrat’S Law ◽  
Gibrat's Law ◽  
City Size Distribution

Jan Eeckhout (2004) reports that the empirical city size distribution is lognormal, consistent with Gibrat's Law. We show that for the top 0.6 percent of the largest cities, the empirical distribution is dramatically different from the lognormal, and follows a power law. This top part is extremely important as it accounts for more than 23 percent of the population. The empirical hybrid lognormal-power-law distribution revealed may be characteristic of other key distributions, such as the wealth distribution and the income distribution. This distribution is not consistent with a simple Gibrat proportionate effect process, and its origin presents a puzzle yet to be answered. (JEL R11, R12, R23)

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