Article
Attracting Skilled Labour to the North: Migration Loss and Policy Implications Across Russia’s Diverse Artic Regions
This paper identifies education, skills training and improved social infrastructure as key development issues to address population decline in regions of steady out-migration from the Russian Arctic. Migration flows are mostly stabilized after the sharp and unexpectedly large population decline in the Arctic in the 1990s, during the transition to a market economy. However, the trends set in motion during that collapse, including falling general levels of education, declining size of all but the largest cities, and aging of the populace, are deepening in consequence for some regions, even where government resettlement programs exist. As young professionals continue to leave, resettling com-patriates and hiring shift labour may contribute to the vitality of more resilient regions, for example, Krasnoyarsk and Yamalo-Nenets. However, the European part of the Russian Arctic, despite its critical importance to commerce and to military security, and despite assistance programs and subsidies, is conforming more to the aging, less productive contours of neighbouring Artic states on the periphery of Europe.
This paper analyzes German and Russian ideas of nationhood as conceived by the state through the states’ migration and repatriation policies. Immigration policies at large and repatriation policies in particular are viewed in this paper as symptomatic means of understanding inclusion and exclusion in a nation-state, and evolution of such policies are taken as indicators of changes in idioms of the national self. The main argument of the paper is that German national identity is slowly moving away from an ethno-centric conceptualization of nationhood, while Russia has failed to formulate a conception of the Russian nation-state. The findings of this study merit further reflection the effectiveness of repatriation policies, on the relationship between the state and society, on the transnational essence of migration pathways, and on the “post-Soviet condition” which has set the stage for all of the aforementioned processes and transformations.
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.