Journal 2017#3


Author V. V. Andreev,
Territorial Distribution of the Population in the Russian Federation

In 1931, Robert Gibrat found that the number of the employees of a firm and urban population follow the lognormal distribution. Numerous studies results show that the Gibrats law provides a basis for the analysis of the dynamics of the number of the employees of mature and large firms, which have already carved out a niche. Furthermore, the Gibrats law allows analyzing the dynamics and laws of the spatial distribution of the population of different countries in a case if their socioeconomic development is sustainable and equilibrium. The purpose of the study is testing the Gibrats law for Russian cities and towns of different sizes. If the Gibrats law is valid, we can conclude that the spatial distribution of the population in the country is equilibrium and the labour distribution is close to optimal. The opposite result demonstrates the imbalance between the allocation of manufacture and labour force. The author took 2010 national census results as source data. I have tested the hypothesis of the lognormal law of population distribution in Russia over different cities and towns using the Pearson fitting criterion with the value a = 0,05. The results of the study have shown that the distribution of the population over Russia does not follow the Gibrats law. As a result, the distribution of the population is uneven, which translates into the significant labour migration from settlements with the small population to large cities. The knowledge of the laws of the territorial distribution of the population and driving factors of population mobility is of importance for the development and implementation of effective socio-economic policy in the country. The definition of the population distribution imbalance over various population centers and the development of recommendations for the creation and optimal location of new production in the country may be the promising area for future research. The study of spatial clustering of population centers and comparative analysis of such clusters with Russian regions in their current administrative borders are important as well.