Прогнозирование межрегиональных миграционных потоков

Abstract

The article explores the problem of interregional migration flows modelling. As a rule, the existing models of interregional migration flows use the aggregated data and do not take into consideration the fact that a decision to relocate is formed and taken at the micro-level, at the level of households. The purpose of the present research is to develop the forecasting method for interregional migration flows of the region taking into consideration the behaviour of households at the micro-level. The research tests the hypothesis that the modelling of household behaviours at the local level as regards taking a decision to relocate to other regions, which takes into account the existing interactions with relatives and other communities, allows to obtain adequate forecasts of interregional migration flows. To forecast the interregional migration flows of the region, we develop a methodological approach and software solution, based on cellular automaton model modification proposed in the current work and on econometric models of birth and death processes, which have been tested on the Primorsky Krai data. The authors' model of the cellular automaton is a kind of the combined probabilistic cellular automaton in which the condition of each cell changes depending on a condition of four closest neighbours (von Neumann vicinity) and four cells chosen in a random way. The article builds a mid-term forecast of Primorsky Krai interregional migration flows. The research demonstrates the possibility and reasonability of modelling the interregional migration flows by cellular automatons. The highly perspective direction of the research is the modelling of other macroeconomic processes based on modelling by cellular automatons of a behaviour of households, companies and other economic entities at the local level.

Рассмотрена проблема моделирования межрегиональных миграционных потоков. Дано описание модели клеточного автомата. Разработанный клеточный автомат был апробирован на статистических данных по Приморскому краю.