New Evidence on Gibrat’s Law for Cities

Abstract

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.

Urbanization and Structural Transformation

“…Gabaix 1999, Eeckout 2004, Eaton and Eckstein 1997, Ioannides and Overman 2004, other studies …nd evidence of violations of Gibrat's Law even for urban population samples (e.g. Black and Henderson 2003, González-Val et al 2008, Soo 2007. In contrast to these studies, we examine population growth for both urban and rural areas.…”

“…Evidence of departures from a Pareto distribution is also found by Eeckhout (2004), who uses data on Census Designated Places (CDPs) in the United States to provide evidence that the population distribution is log normal, as implied by Gibrat's Law of constant proportional growth. 10 While Gibrat's Law is generally seen as a good approximation to observed city population growth rates, there is also some evidence of departures from Gibrat's Law, as found for example by Black and Henderson (2003), González-Val et al (2008) and Soo (2007). 11 Two empirical issues in the existing literature on cities are the treatment of new cities and the minimum population size to be considered as a city, both of which are addressed in our approach by considering the entire distribution of population across both rural and urban areas.…”

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

“…To do this, we estimate Equation (1) for different sample sizes, namely 50, 100, 200, 500, 1000 and so on, adding groups of 500 cities at a time until they are all considered 4 . We estimate starting from the largest cities (from the upper tail) 5 . Figure 1 shows the values estimated by OLS of the beta parameter from Equation (1) for the six studied countries.…”

“…As González-Val (2010) and González-Val et al (2012) show, variance of the growth rates usually depends negatively on city size because small cities display higher growth and variance compared with larger cities. Therefore, the results derived from Equation (1) depend on the number and size of the cities included in the sample.…”

Firm-level intra-industry links in Croatia’s tourism industry

“…Thus, although usual statistical publications contain data on tourism arrivals and overnights, the impact of tourism activities on these n-tier industries and firms (henceforward supporting industries and supporting firms) is more important for the domestic value added and for domestic economy in general. In our paper we empirically test the impact of accommodation industry division growth on supporting industry divisions within tourism industry, while the theoretical bedrock of the paper is Gibrat's law (Gibrat, 1931). However, the aim of our research is not only to test Gibrat's law, but to identify linkage between growth of different industry divisions within tourism industry in Croatia.…”