Региональное развитие сельского хозяйства в зарубежных странах Европы и в России в условиях глобальной продовольственной взаимозависимости и дефицита земельных ресурсов
The paper aims to investigate the process of establishing distribution network. The paper takes network paradigm as a main basis of investigation looking at the development of distribution networks in Russian chemical industry.
The article is devoted to the trends and determinants of the transformation of Russian regions' industrial specialization during the period of economic growth. Using the methodology of statistic and econometric analysis it is tested whether the tendency of diversification dominates the tendency of regions’ industrial specialization in 1997-2004 and whether there is a convergence of Russian regions' industrial structures. The considered factors of industries' development in a particular location include the initial industrial structure, inter- and intraregional technologic links between industries, quality of investment climate, R&D potential, international competition.
In this paper the public-private wage gap is estimated by means both of the OLS and the quantile regression, which will provide a more complex picture of the distribution of the public-private sector wage gap. The author finds the existence of significant public-private wage gap (about 30%) considering both observable and unobservable characteristics of workers and jobs. Using the decomposition based on quantile regression helps to answer the question about the nature of the wage differences. The author comes to the conclusion that the main reason for the gap is the institutional mechanisms of public sector wages in Russia. The analysis is based on the data from Russian Longitudinal Monitoring Survey (RLMS-HSE) 2000-2010.
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.