Экономика городов Казахстана
The article, based on current accounting data migration, analyzed the age characteristics of internal migration in Russia. The degree of influence of migration on the age structure of the population of 4 groups regions. On the example of the Central Federal District of the distributions increase (decrease) of population and intra-regional capitals periphery by age groups. More detailed analysis of migration age profile in Moscow and Moscow region.
Basing on the data of migrant population surplus/decline in Russian cities for the period 1991-2009 the attempt is made to evaluate the impact of the population size of a city as well as the city position in the system of central-peripheral relations on its migration balance. The author also explains the existing migration mobility pattern through hierarchy of cities within a region.
Subject Pursuing the socio-economic policy in regions requires understanding the processes of concentration of resources, population, enterprises in certain territories, mostly, in cities. Recent studies show increasing interest of economists in the Zipf's Law manifestation in the regional system, and cities distribution under the rank-size principle.
Objectives The aims are to test the Zipf's Law in Russian cities, to support or reject the hypothesis that in Russia the Zipf coefficient depends on the size of the geographical territory of the federal district.
Methods We used the least square method to analyze the Zipf's Law in Russian cities in general, and in each federal district, in particular. The sampling includes 1,123 Russian cities with population over 1,000 people in 2014. Results The Zipf's Law manifests in the entire territory of the Russian Federation. In federal districts, the Zipf coefficient ranges from -0.65 (the Far Eastern Federal District) to -0.9 (the Ural and North Caucasian Federal Districts). The analysis of the sampling of cities with population over 100 thousand people demonstrated -1.13 Zipf’s coefficient.
Conclusions The test of the Zipf's Law for Russian cities shows that it is valid for small (8,600-15,300 people) and large cities (66,700-331,000 people). The Zipf's Law fails for cities with population exceeding one million people (except for the city of St. Petersburg). The study supports the hypothesis on dependence of the Zipf coefficient on the size of a federal district.
This article describes the tools for analysis scientific-industrial complex of the city, analysis of scenarios of development of industrial areas, sets out the basic parameters of a mathematical model of the balance of interests.
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.