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Innovation infrastructure is one of the key elements of the technology transfer process. In Russia, at the government level, great attention is paid to the construction of new infrastructure, but an appropriate framework for its efectiveness evaluation has not been suiciently developed. In recent years the government has tried out various forms of support for innovation through infrastructure developments, many of which have demonstrated lack of viability in the absence of government subsidies. In the course of the inspection of large-scale projects Supervisory bodies have also discovered the fragmentation and inconsistency of the state policy aimed at construction of regional research infrastructure complexes. The eiciency of local innovation systems is a subject of interest for academic economists. Many rankings are intended to explicitly indicate the comparative advantages of some subjects of the Russian Federation over the others in many diferent aspects. Another line of research relevant to the problem is Regression Modeling. Based on the knowledge production function, Russian researchers studied the dependence of the innovative output in the Russian regions on a large number of factors, including human capital, research funding, productivity of applied research, etc. Despite the impressive number of such works, signiicance of the innovative infrastructure has remained outside the focus of attention. This paper presents the estimates for panel data regression model, which has shown a positive relationship between the presence of innovative territorial cluster and the growth of innovative products. However, under this approach no evidence has been obtained for a positive impact of special economic zones and regional development corporations on regional innovation complexes.

The article demonstrates the predictive capabilities of the previously proposed method of scientiic and technological balance. In addition to linking the scientiic sector with the real economy the equation of the scientiic and technological balance allows for relatively simple forecast calculations. For this purpose, the article considers three forecast scenarios: ''evolutionary'', ''resolute'' and ''reformatory''. Each of them was considered in two variants – with a stimulating personnel policy aimed at stabilizing the number of employed in the scientiic sector, and with an optimizing personnel policy assuming the primacy of eiciency due to the active release of ineicient scientists. As a point of reference for the forecast calculations the averaged science and technology indicators of the G7 countries were taken. These indicators include the ratio of «transmission» of scientiic articles into patents, the productivity of science, the share of the scientiic sector in the total employment, the share of government funding of science, the average price of one patent, etc. Forecast scenarios are based on the target of increasing the efectiveness of the Russian economy to the level of labor productivity of the G7 countries. The calculations have shown that the domestic economy already has positive trends in several areas. Provided they are slightly accelerated through stimulating policies, this will make it possible to successfully implement the catch-up strategy for technological development. According to the forecast scenarios, by 2022 Russia will be able not only to catch up the developed countries in the labor productivity, but even to be slightly ahead of them. However, the calculations show that this can be done only within the framework of the "reformatory" scenario with a stimulating personnel policy. ; For citation: BALATSKY, E. V., YUREVICH, M. A. (2018) Forecasting the efectiveness of the Russian economy on the basis of the scientiic and technological balance. Science. Innovations. Education. No. 2 (28). ...