Российские корейцы: всесилие власти и бесправие этнической общности. 1920-1930
The book examines the history of the Soviet Koreans in 1920-1930 gg. as part of the history of poly-ethnic state - Soviet Union. The author's conclusions are based on the identified new archival materials, generalized results of their predecessors. The main attention is paid to the analysis of the dual nature of Stalin's policy in relation to the Koreans, causes and forms of political repression, applied to the Korean population, whose fate in the Far East was largely dependent on the state of foreign relations between the USSR and Japan. On the basis of statistics identified losses among Koreans in political repression, demographic changes, the position of Koreans in the late 1930s. Publication of the book is timed to the 150th anniversary of the voluntary resettlement of Koreans in Russia.
In 2006, Russia amended its competition law and added the concepts of ‘collective dominance’ and its abuse. This was seen as an attempt to address the common problem of ‘conscious parallelism’ among firms in concentrated industries. Critics feared that the enforcement of this provision would become tantamount to government regulation of prices. In this paper we examine the enforcement experience to date, looking especially closely at sanctions imposed on firms in the oil industry. Some difficulties and complications experienced in enforcement are analysed, and some alternative strategies for addressing anticompetitive behaviour in concentrated industries discussed.
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.