Gibrat’s law for cities, growth regressions and sample size

González‐Val, Rafael; Lanaspa, Luis; Sanz‐Gracia, Fernando

doi:10.1016/j.econlet.2012.11.020

Cited by 12 publications

(5 citation statements)

References 5 publications

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“…Consequently, most studies focus on analysing the most populous cities, the upper-tail distribution. However, any analysis using this kind of sample will have a local character because the behaviour of the entire distribution cannot be extrapolated from that of large cities (for an analysis of the effect of sample size on Gilbrat’s law, see Gonzzález-Val et al , 2013).…”

Section: Gibrat’s Law For Cities: An Overview Of the Literaturementioning

confidence: 99%

New Evidence on Gibrat’s Law for Cities

González‐Val¹,

Lanaspa²,

Sanz‐Gracia³

2013

Urban Studies

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The aim of this work is to test empirically the validity of Gibrat’s law on the growth of cities, using data on the complete distribution of cities (without size restrictions) from three countries (the US, Spain and Italy) for the entire 20th century. In order to achieve this, different techniques are used. First, panel data unit root tests tend to confirm the validity of Gibrat’s law in the upper-tail distribution. Secondly, when the entire distribution is considered using non-parametric methods, it is found that Gibrat’s law does not hold exactly in the long term (in general, size affects the variance of the growth process but not its mean). Moreover, the log-normal distribution works well as a description of city size distributions across the whole century when no truncation point is considered.

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Section: Gibrat’s Law For Cities: An Overview Of the Literaturementioning

confidence: 99%

New Evidence on Gibrat’s Law for Cities

González‐Val¹,

Lanaspa²,

Sanz‐Gracia³

2013

Urban Studies

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“…For urban size distribution, various functions have been studied using statistical indicators such as LN, the double Pareto-lognormal distribution (dPLN), loglogistic, the threshold double Pareto Singh-Maddala, lognormalupper Pareto, Pareto tails -lognormal, three log-normal (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2). Relevant references to this research includes [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Initial Value Dependence of Urban Population's Growth-Rate Distribution and the Long-Term Growth

et al. 2020

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This paper uses census municipal population data for the United States, Italy, and Spain to analyze the statistical properties of their 10-year growth (short-term property). As a result, it was confirmed that the smaller the initial urban population is, the greater the probability that the urban population will decrease and that the probability that the urban population will increase does not depend on the initial urban population. We also observed the statistical properties of long-term growth of urban populations in each country over 100 years. Specifically, we identified the following properties by observing the geometric mean of logarithmically equal sized bins of the oldest urban population in the data used in the analysis. (1) The average urban population increases or decreases exponentially with time. (2) The smaller the initial average urban population, the smaller the exponent, which can be negative in Italy and Spain. (3) When the average urban population is large, exponential growth may stop. We showed that these long-term properties are derived from the short-term property by random sampling simulations from real data.

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“…For the case of France, as in [20], we consider the lowest spatial subdivision, the communes, as listed by the Institut National de la Statistique et des Études Économiques (www.insee.fr). We have employed the data for the years 1999 and 2009.…”

Section: The Databasesmentioning

confidence: 99%