The Russian Regional Convergence Process: Where is It Leading?
This paper investigates income convergence among Russian regions between 1998 and 2006. It makes two major contributions to the literature on regional convergence in Russia. First, it identifies spatial regimes using the exploratory spatial data analysis. Second, it examines the impact of spatial effects the convergence process. Our results show that the overall speed of regional convergence in Russia, being low by international standards, becomes even slower after controlling for spatial effects. However, when accounting for spatial regimes, we find a strong regional convergence among high-income regions located near other high-income regions. Our results indicate that estimating the speed of convergence using aggregate data may result in misleading conclusions regarding the nature of the convergence process among Russia's regions.
An important role of digital inequality for hindering the development of civil society is being increasingly acknowledged. Simultaneously, differences in availability and the practices of use of social network sites (SNS) may be considered as major manifestations of such digital divide. While SNS are in principle highly convenient spaces for public discussion, lack of access or domination by socially insignificant small talk may indicate underdevelopment of the public sphere. At the same time, agenda differences between regions may signal about local problems. In this study we seek to find out whether regional digital divide exists in such a large country as Russia. We start from a theory of uneven modernization of Russia and use the data from its most popular SNS “VK.com” as a proxy for measuring digital inequality. By analyzing user activity data from a sample of 77,000 users and texts from a carefully selected subsample of 36,000 users we conclude that regional level explains an extremely small share of variance in the overall variation of behavioral user data. A notable exception is attention to the topics of Islam and Ukraine. However, our data reveal that historically geographical penetration of “VK.com” proceeded from the regions considered the most modernized to those considered the most traditional. This finding supports the theory of uneven modernization, but it also shows that digital inequality is subject to change with time.
The article analyzes internal migration in Russia and identifies the main factors that influence it. The model of migration factors is estimated from panel data on Russian regions based on official Rosstat data for 1999–2010. Demographic factors, indicators of the labor market and housing, quality of life, the provision of public goods, infrastructure, and expenditures from regional consolidated budgets on various needs are considered as migration factors. Analysis showed that migration sensitivity is higher to demographic and economic factors (housing provision and per capita income), rather than to different social or other factors. Expenditures on education and healthcare in regions have the biggest impact on migration among all other regional budget expenditures.
The paper aims at revealing factors influencing the development of e-government in the Russian regions. Based mainly on the innovation diffusion concept we run quantitative analysis, testing the significance of political, tech-nological, socio-economic and administrative variables. Our study shows that the diffusion of e-government itself was to a large extent the result of a vertical influence of the federal government, however, disproportions of e-government performance can better be explained by internal characteristics of the regions. We argue that the key predictors for a more mature e-government are relatively democratic political regime, technological advancement, bureaucracy effective-ness and investment in ICT. The explanatory model could best be expanded by case studies focused on agency rather than the structure.
This volume discusses post-socialist urban transport functioning and development in Russia, within the context of the country’s recent transition towards a market economy. Over the past twenty-five years, urban transport in Russia has undergone serious transformations, prompted by the transitioning economy. Yet, the lack of readily available statistical data has led to a gap in the inclusion of Russia in the body of international transport economics research. By including ten chapters of original, cutting-edge research by Russian transport scholars, this book will close that gap. Discussing topics such as the relationship between urban spatial structure and travel behavior in post-soviet cities, road safety, trends and reforms in urban public transport development, transport planning and modelling, and the role of institutions in post-soviet transportation management, this book provides a comprehensive survey of the current state of transportation in Russia. The book concludes with a forecast for future travel development in Russia and makes recommendations for future policy. This book will be of interest to researchers in transportation economics and policy as well as policy makers and those working in the field of urban and transport planning.
This paper provides a pioneering approach to estimate the relationship between interregional human capital mobility and the occurrence of high-growth firms (HGFs). We construct and employ the dataset on mobility of university graduates from top-100 Russian universities. We find that the relationship between the mobility of high-skilled university graduates and high-growth firms is non-linear and U-shaped: the initial rise in the number of HGFs is due to the relatively low concentration of highly skilled migrants and availability of innovations only for a small number of firms. However, the competition effect strengthens at some point when innovations become available for larger number of firms simultaneously with large inflow of highly skilled university graduates.
The online edition contains mental maps of all major Russian macroregions & some regions & cities of Russia, representing ethnic, cultural & geographical specificity of the territories. Unique regional images & their localization are combined in vivid textual & visual materials, mental maps & regional onomasticons.
For the experts specialized in cultural geography & geihumanities, regional & local studies, cartography, and for a wider audience of those interested in geographical diversity of Russia.
The cahpter deals with the cross-regional variety of entrepreneurial activity in Russia and the factors which may determine it, basing on the results of a representative survey of ca. 56 000 adults in the regions of Russin (2011)/ It is shown that the quality of the entrepreneurial activity of population (prevalence of the opportunity driven entrepreneurship) does not correlate with the density of already existing SMEs as well as with the level of unemployment; but it correlates with with the level of urbanization as well as with the level of the well-being of population of rerspective regions. Besides, the regional TEA positively correlate with the perceived opportunity and the self-efficacy of adults i respective regions.
The fundamental idea underpinning spatial econometric models of economic growth is as follows: regional growth is determined not only by social, economic, geographic traits of a region but also by spillovers from other regions, most importantly adjacent ones. If one region starts booming, it can left neighbors unaffected (neutral mechanism), spur their growth (cooperation mechanism) or slow their growth by pulling resources over (competition mechanism). What mechanism and to which extent occurs in practice matters for designing balanced economic policy and evaluating efficiency of regional policy investment. Classic spatial econometric models make strong although simplifying assumption that the same mechanism matters for all regions in the same manner, and there is no variation in spillovers intensity across regions. This assumption seems plausible for relatively small and homogenous regions of European countries, but it looks excessively strong for large and diverse Russian regions. In this paper we attempt to relax this assumption and propose a new model, fitting better in Russian conditions and bringing only slight sophistication from the estimation point of view. We introduce sensitivity parameter governing regional exposure to externalities. We assume this parameter to be a linear function of region-level observables, like area, population density or urbanization rate. These hypotheses have been confirmed at least partially. We found that dense and urbanized regions were more sensitive to spillovers. In other words, a region surrounded by the fast-growing areas, will grow the more intense, the more its population density and the higher the level of urbanization.