The paper examines the changes in higher education sector with a new configuration of resources. It presents the results of empirical analysis conducted to identify the factors influencing the value added creation in education, taking into account the scientific innovation development of regional clusters, and based on assessment of the interrelations of the resource base of higher education and industrial and economic potential of Russian regions.
In the article results of analysis of characteristics and dynamics of income and subjective stratification models of Russian society are presented, based on data from several nationwide surveys carried out in 1999-2016. It is shown that the current model of income stratification is characterized by the dominance of the middle strata and is adequately reflected in public consciousness, based on the self-assessment of the positions people hold in the society. Economic crisis that started in 2014 so far did not cause any serious changes in the income stratification model or the assessment of their positions in society by Russians.
As for the methodological results of the analysis, it is shown that the optimal methods for income stratification of Russian society should be found among the relative methods used in developed countries, but not among the absolute methods used in developing countries. In addition, given Russia's regional heterogeneity in terms of modernization progress, it is more expedient to use the aggregate model of income stratification constructed on the basis of pre-stratification of regional communities than models based on the average measures for the country as a whole for the analysis of the social structure.
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.