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The article is devoted to the variable and disproportionate funding of science in the Russian economy. The paper is focused on the analysis of the Russian financial flows into scientific research and development. The paper explains the dynamics of the main investment flows trends into research and development, highlights the causes of financial flows variable dynamics directed to the high-tech industry. In the work, the investment situation in the Russian market was compared with the foreign experience. The genesis of the optimal financial distribution problems showed the need to develop a dynamic model with the built-in differential equations to forecast the behavioral dynamics of investment flows. We selected the statistical indicators, which have a significant impact on the dynamics of investment flows directed into science. To assess the dynamics of investment flows, we have developed a methodology, which provides a cumulative assessment of the territory investment attractiveness. The multifactor integral estimation allows to describe a data array, reflecting the accumulation of investment attractiveness over time depending on the dynamics of the resultant socio-economic proportional indexes. Due to the accumulation of a data array over time using a differential equation, it is possible to obtain a forecast of the volume of the territory investment attractiveness. The amount of the projected investment flows depends directly on the amount of the investment attractiveness accumulated for the previous step of model’s time. The integrated assessment of the investment attractiveness of the scientific sector in the region allows to reveal the investors preference of the regions with a high concentration of research institutions and higher education institutes.

Russia’s reasons for the continuing need of the foreign labor influx were researched. Forecast of the interdependent labor market development and migration processes in the regions of Russian is proposed as a scientific basis for better regulation of the international labor migration. The genesis of approaches to migration modeling, revealing the lack of tools for the simultaneous prediction of migration processes and the labor market was studied. A developed model complex allows to predict: 1) migration flows, determined by the wages level difference and the distance between the country of origin and the destination regions, number of potential migrants in countries of origin and the number of migrants who arrived earlier in the regions of destination; 2) migrants’ employment in the region, considering changes in the job availability as a result of the migrants influx to the regional labor markets, characterized by search tensions; 3) the level of wages in the region, considering the bargaining power of the worker. The application of the model complex to the statistical data resulted in obtaining of the forecast flows of migrants in the Russian regions from the CIS and their influence on the regional labor markets development up to 2020. Based on this forecast, the recommendations to improve the management of the international labor migration in the Russian regions were developed

This paper presents a dynamic model that can predict the dynamics of migration flows between source countries and host regions, as well as the dynamics of wage levels there. The model is constructed within the framework of neoclassical economics and human capital theory in continuous time. Thanks to liberalization of migration policy in Russia in 2007, the model could be successfully employed to Russian regions and the Commonwealth of Independent States (CIS), which have visa-free entry regulations with the Russian Federation. Employing the model on statistical data, we forecast the number and origin composition of foreign labor force from the CIS into Russian regions for 2010-2016. The purpose of our further research is to classify migrants by skills

This paper presents a dynamic multiregional model of labor migration in continuous time within the framework of the neoclassical economics. The model can predict the dynamics of migration flows as well as the dynamics of economic effects of migration simultaneously for host regions and source countries. Thanks to liberalization of the migratory legislation of the Russian Federation in 2007, the model can be used for Russian regions and CIS states, which citizens were granted entry without a visa and a simplified procedure of employment in Russia. Employing the model to statistical data, forecast trajectories of migration flows to Russian regions from CIS states have been received for the period since 2010 to 2016. The predicted migration looks quite realistic and has important policy consequences. In particular, these findings can be used for developing work permit quotas.