To Be Continued: The Religious and Social Life of Russia’s Regions (Review of: The Religious and Social Life of Russia’s Regions. Vol. 3 /Ed. S. Filatov. Moscow: Letnii sad, 2018 (in Russian). – 468 pages)
Review of: The Religious and Social Life of Russia’s Regions. Vol. 3 /Ed. S. Filatov. Moscow: Letnii sad, 2018 (in Russian). – 468 pages
The paper analyzes the regional diversity of the relationship between religiosity and the voting behavior in Russia in the 2010s. The existing studies of the Russian case demonstrate that there is a correlation to a certain extent; however, there are no studies focused on the regional level. The results of the statistical analysis based on a religious survey, conducted by the research group SREDA, show a correlation between religiosity and the outcome of the federal elections in 2011–2012. Religiosity is positively associated with voting for the party “United Russia” in 2011 and for Vladimir Putin in 2012.
This article is talking about state management and cultural policy, their nature and content in term of the new tendency - development of postindustrial society. It mentioned here, that at the moment cultural policy is the base of regional political activity and that regions can get strong competitive advantage if they are able to implement cultural policy successfully. All these trends can produce elements of new economic development.
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.