How Do The Characteristics Of The Environment Influence University Efficiency? Evidence From A Conditional Efficiency Approach
We consider a model of regions’ ranking in terms of their vulnerability to natural and technological disasters. Regions are different in terms of their resistance to different disasters, by their population, by the distribution of the sources of potential disasters, etc. We consider different models of a data envelopment analysis (DEA) approach taking into account the risks of the implementation of different measures, their cost as well as the heterogeneity of regions. The numerical examples demonstrate the application of the constructed model for the regions of Russian Federation.
We estimate efficiency scores for Russian universities based on data set of input and output criteria by using Data Envelopment Analysis. In addition, we use a reputation index as another indicator of a university’s productivity. To construct it, 4000 contexts are analyzed and 13 reputation criteria are found. The threshold procedure is used to aggregate them into a reputation indicator. Factors which lead a university to be efficient are studied.
The article investigates the financial resource management for the development of mass and elite sports at the regional level. The authors used statistical data of the Ministry of Sports that include 28 socio-economic indicators and 39 indicators of sports development in 82 regions for 2012 — 2015. A model of sports development was built using PLS-SEM method. We identified the following latent variables: economic development of the region; funds allocated to sports development; availability of resources; development of mass sports; development level of professional sports; results in elite sports, results in adaptive sports. The level of regional economic development affects the amount of funding allocated to the sports, which in turn determines the availability of resources. Availability of resources affects the success in the development of mass and professional sports. Success in professional sports determines results in great sporting achievements and adaptive sports. Structural modelling allowed us to identify measurable indicators of resources (model inputs) and results of sports development (model outputs). The authors assessed the effectiveness of transformation of inputs into outputs using DEA method. We investigated two models. The first one uses the indicators of mass sports development as outputs, the second one uses the indictors of professional sports development as outputs. The inputs of both models are the indicators of financial resources for sports. The simultaneous review of the effectiveness of two directions allows to emphasize the features of each region and evaluate balance in the development of mass and professional sports. The modelling results allow to identify several groups of regions with similar parameters, which may be due to their similar locations.
DEA-analysis is performed based on publicly available data on 94 world largest fashion retailers. Standard clusterization of coefficients obtained from DEA-analysis gives clusters that are analyzed with respect to homogeneity and fit to the types of strategic behavior outlined in strategic management.
In this paper we research changes of total factor productivity (TFP) and its components in Russian plastic production sector in 2006–2012 by using DEA. We decompose TFP change into technical change, technical efficiency, scale efficiency, and mix efficiency changes. The dynamics of TFP and its components are slightly different for various quantile groups of firms. Anyway there is a significant fall for almost all indices in 2009. The results’ robustness is checked by comparison of technical efficiency estimates obtained with DEA and SFA methods.
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.