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The article is devoted to studying and medium-term forecasting of the scientific personnel in Ukraine by use of multifactor regression model, with focus on change in R&D employment mainly through redistribution of qualified staff between economic activities and with consideration to possible transformation of the socio-economic model in Ukraine in the forthcoming three years. Most significant factors (indicators) are identified, which, if brought to control, enable for accelerating or hampering renovation of the scientific personnel: the share of GDP allocated for R&D, the ratio of compensation in R&D and the industry average, the ratio of compensation in R&D and the average in public administration, the ratio of compensation in R&D and the average in banking and financial intermediation sector, the ratio of compensation in R&D and the average across the national economy. Three development scenarios for the scientific personnel in Ukraine till 2012 are built on the basis of the constructed multifactor regression model (scenario 1 - moderate growth; scenario 2 - optimistic; scenario 3 - pessimistic), with measures of budgetary support to science, proposed specifically for each model. Results of the modeling allow for the conclusion that growth in R&D employment in Ukraine can only be expected through recruitment of new personnel, but this growth will only be feasible when proper working conditions in R&D are created and the ratio of compensation in R&D and 'competitive' sectors (finances and public administration in the first place) is changed.
The article aims to analyze methodologies for identification and distinguishing of industries by R&D capacity and technological intensity, and construct a methodology for measuring Ukrainian industries, in order to study structure of the national industry. Relevant methodologies and practices of leading international organizations, such as the US National Science Foundation (NSF), the OECD, Canadian Ministry for Science and Technology, as well as the former USSR are reviewed. A methodology for R&D capacity measurement in the Ukrainian industry (by industry) with accounting for science & technology, innovation and production capacities by use of existing official statistical indicators is built. Estimations of R&D capacity at industrial enterprises of Ukraine on the basis of data for 2008 are made. Results of comparisons of industries' grouping by technology level, practiced in Ukraine and in the OECD, are discussed, with emphasis on common and distinctive features of the two groupings. Analysis of groups of industrial objects in Ukraine with various R&D capacity (by economic activity), estimated on the basis of official statistical data, demonstrates full mismatch between science & technology, innovation and industrial policies in Ukraine, being an evidence of insignificance (underdevelopment) of technologically advanced productions in Ukraine.
A review of endogenous growth models is given. The USA can keep up as the global leader thanks to the vast domestic market, the effective corporate sector, capabilities for financing large-scale projects, and, most importantly, the excellent science & technology performance along with perfect education system and innovation capacities. The S&T performance in the USA is shown by use of abundant statistical data (including retrospective ones) on R&D expenditures compared with selected countries of the world, R&D financing by source, R&D expenditures by performing sector, R&D funds of leading universities, R&D funds of universities and colleges by research fields, R&D expenditures and domestic sales of high tech industries. The companies' role in R&D financing is emphasized, with reference to the Bayh-Dole Act as a powerful engine for R&D and technology transfer. The American economy's position in the world market as the largest supplier of commercial knowledge-intensive services, high tech products and information & communication services is shown. Also, concerns of the American scientific community are mentioned: the shrinking share of the USA in the global high tech exports, imbalance between the American investment in high tech and knowledge intensive activities of other countries and the foreign direct investment in these activities in the USA; negative balance of foreign trade in high tech commodities; the degrading quality of education when the demand for qualified workforce is met by immigrants.
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