The article is devoted to the problem of estimating dynamic of shadow economy in the context of socio-economic crisis negative influence. Factors and threats supporting the increase of shadow economy during the economic crisis were studied. Current situation in the Ural Federal District with regard to shadow economic activity for the period from 2006 to 2012 was represented. The results of forecasting shadow activity were given in the article on the example of Ural Federal District regions. Data obtained was used for developing target program activities, aimed at minimization of negative influence of shadow economy during the economic crisis. Special attention was given to the problem of improving the effectiveness of international cooperation concerning counteraction of shadow economic activity.

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.