Индустриальные парки как форма государственной поддержки экономического развития
The article examines the national policy towards economic development through promotion of such a form as industrial park. National regulations of industrial parks’ requirements are discussed in terms of complexity to fulfill for regions to become a recipient of state support. We investigate options of combination of state support for implementation of investment projects for creation and operation of industrial parks with such forms of stimulating economic development as support of special economic zones, territories of advancing social and economic development, industrial and innovation clusters.
The article is devoted to studying the main features of Russia’s cluster policy, comparing it with the policies of a number of other countries, determining the effect of this policy on Russian enterprises and formulating recommendations for improving the situation. A comparison of the main features of the cluster policy of several countries suggests that more domestic approaches are similar to the characteristics of the Czech Republic and Poland, although in Russia this policy was launched later than in other European countries and domestic activities are not financed from external sources. Unlike the US in Russia, cluster policy is mainly carried out “from the top down”, and is not a response to enterprise initiatives. In Russia, also typical for other countries are measures of cluster policy, in particular, on the promotion of start-ups, the development of human capital, the improvement of innovative culture, the creation and development of communication channels. At the same time, the regression analysis showed that the fact that it operates in a state-supported cluster does not have any significant impact on enterprises. From this in the end, conclusions are drawn that the existing cluster policy needs to be adjusted, in particular, in a deeper connection to the strategic goals of the region’s development and taking into account the specific characteristics of clusters working on the territory, especially the stages of their life cycles.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.
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.