City Size Distribution Dynamics in Transition Economies: A Cross-Country Investigation
Project type: CERGE/GDN IRC, 2009-2010
Project team:
- Ciprian Necula, Bucharest Academy of Economic Studies - coordinator
- Uladzimir Valetka, Belarus State Technological University, CASE
- Marat Ibragimov, Tashkent State University of Economics
- Gabriel Bobeica, Bucharest Academy of Economic Studies
- Alina Nicoleta Radu, Bucharest Academy of Economic Studies
- Kamila Mukhamedkhanova, Center for Economic Research
- Aliaksandr Radyna, Belarus State University
Abstract
The empirical literature in urban economics have come to an agreement that, in the long run,
the growth rate of a city is independent of its initial size (Gibrat`s law) and that the city size
distribution could be closely approximated by a Pareto distribution with an exponent that
varies among countries. It is of great interest to determine the factors that influence the value
of the exponent, for such a relationship may point to policy-related issues. The general
objective of the present Project is to study the dynamics of the city size distribution in CEE
and CIS transition economies, and identify the determinants of the variation of this
distribution in time and across countries. We will build a comprehensive unified database for
CEE and CIS countries concerning city dynamics. We will test the Gibrat`s law employing
Pesaran panel unit root test that takes into account the presence of cross-sectional dependence
and Nadaraya-Watson non-parametrical kernel regression. The main novelty of the Project
resides in constructing a consensus estimate of the Pareto exponent of the city distribution
using various econometric methods. We also test for non-Pareto behavior of the distribution
of when all the cities in a country are considered, using the Weber-Fechner law, the
logarithmic hierarchy model, and the log-normal distribution. Not only we consider various
distributions, but also study the �within distribution� dynamics by analyzing the individual
cities relative positions and movement speeds in the overall distribution using a Markov
chains methodology. In order to explain the differences in the city distributions and obtain
valid statistical inference, we will estimate, using Driscoll-Kraay robust standard errors, a
panel data fixed effects model to control for unobserved country specific determinants.
Completed paper
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