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A MODEL OF CROSS-BORDER MIGRATION FROM CENTRAL ASIAN COUNTRIES TO RUSSIA

The growth in the volumes of cross-border migration flows is one of the most important features of globalization. International migration is largely driven by global inequality. According to P. Collier, the volume of migration from the origin country depends on the size of the corresponding diaspora in the host country and on the gap in the average income of citizens between the two countries. Migration for a specific individual usually involves significant economic costs. Accordingly, the poorest individuals, being in poverty traps, find themselves deprived of the opportunity to emigrate from their country. The diaspora that has developed in the host country reduces these costs for its countrymen. This article is an attempt to test P. Collier’s hypothesis on the materials of cross-border migration from Central Asian countries to Russia in 2016-2020. Consideration of the influence of diaspora size and income gap on the size of the migration flow is formalized by constructing a regression model. The predictors of the model are the number of documented migrants (with valid temporary and permanent residence permits) and a macroeconomic indicator, which is the ratio of gross domestic products per capita (at purchasing power parity in current prices) of a Central Asian country and Russia. The migration flow is described by the number of citizens of Central Asian countries who were primary registered with migration. The indices of the numbers of individuals were replaced by their logarithms, which improved the model quality. In its final implementation, the regression model is characterized by a number of statistical criteria that take satisfactory values: p < 0.05 for the intercept and both predictors, the adjusted coefficient of determination is 0.911, the Durbin — Watson test is 1.637, and the Breusch — Pagan test is 1.9560 (p = 0.3761). Thus, the constructed model confirms P. Collier’s hypothesis. We use the data of the Ministry of Internal Affairs of the Russian Federation and the IMF. The modeling was carried out in the integrated development environment Jupyter Notebook (Python 3.8 as a programing language).