Инновационное развитие экономики России и рынок интеллектуальной собственности
In the article the main features of the change over to the innovation-based development are examined and major problems faced in creating and functioning of the intellectual property market are stated. Also suggestions on a creating civilized intellectual property market as a way of a change over to the innovative economy are offered.
Article gives a characteristics of workforce and examines principals and approaches to development of it s innovation in modern conditions.
Chapter 6 presents an analysis of Russian innovation system accompanied by an overview of state science, technology and innovation (STI) policy practice.
The authors cover the most urgent institutional cleavages, including the split-offs of science and industry, issues of institutional model of the R&D sector, sectoral discrepancies and regional polarization.
An outline of STI policy framework evolution is presented, including the most recent Strategy for Socio-Economic Development of Russia till 2020 topics. A special regard is paid to linkage-stimulating policy instruments, including grants for joint research for Universities, R&D organisations and companies, technology platforms, regional innovation clusters program and elaboration of innovation development plans for state-owned companies.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.