The Russian regional convergence process: Where does it go?
This book focuses on the questions of how territorial differences in productivity levels and unemployment rates arise in the first place and why territorial differences in labor market performance persist over time. Unemployment divergence and unemployment club convergence have been touched on in a large number of works and have recently also been studied using spatial econometric analysis. In this book we aim to develop the debate to include several important new topics, such as: the reasons why structural changes in some sectors cause slumps in some regions but not in others; the extent to which agglomeration factors explain regional imbalances; the degree of convergence / divergence across EU countries and regions; the role of labor mobility in reducing / increasing regional labor market imbalances; the impact of EU and country-level regional policy in stimulating convergence; and the (unsatisfactory) role of active labor market policy in stimulating labor supply in the weakest economic areas.
The Higher Education and universities have high impact for regional development and youth migration. We suggest what the migration of people with a high level of knowledge (called “brain drain”) is detrimental for the region of emigration. High level universities attract the best students and growth the brain drain. There are close relationships between neighboring regions. Distance can be understood as a barrier of human capital growth. Geographical distance between parental home and college poses a potential barrier to higher education entry, and could be a deciding factor when choosing between institutions. Similar issues potentially arise when considering who goes to which universities, because students with different backgrounds and abilities choose different types and qualities of universities, and the spatial distribution of both university types and student characteristics is not uniform. But at the same time there are the researches which don’t find the impact of distance to accessibility of higher education. The distance a pupil lives from their nearest university has little effect on the likelihood that they go to university. There are many articles describe the social Neighborhood Effects of universities. But the question about geography and place is too often overlooked. The paper of Cullinan and Duggan presents a gravity model of student migration flows to HEIs in Ireland. Their analysis suggests that while geography plays a very important role in explaining student flows. Available studies about student migration cover the territory of England, Ireland, Romania, Poland, US, Canada etc. But we don’t have the works which explain the spatial effect of Russian universities to youth migration. In this article we observe the example of Kazan federal university and her spatial effect to educational migration. The case of Kazan federal university is very important. It’s a one of ten federal university of Russia. More of 30.000 students study in university, 80% of them is from Volga Federal district. The study allowed to find the neighbors of the first and second order, who are influenced by a strong neighbor.
What do foreign companies take into account when they invest in Russian food industry enterprises? The sample of about 5000 enterprises of the food industry from different Russian regions is analyzed to give the answer to this question. The most interesting points for the investigation are formulated as two hypotheses. The first one is connected with the level of economic development of a region where the particular company is situated, the other one is about the foreign direct investment during previous periods in this region and the neighboring ones. To test the hypotheses on the base of the idea of spatial effects of analyzed factors several special variables are constructed. The estimation of a multilevel binary model gives the idea for the possible explanation of the problem discovered above.
The present study suggests a generalization of the spatial autoregressive model for the case when considered regions are split into two different groups, which have a mutual influence on each other. The weighted matrix in this model is split into four parts and four spatial coefficients are estimated. The proposel model is applied for the analysis of three macroeconomic indicators using the data for Russian regions, previously divided into western and eastern. Our analysis revealed, 1) a positive spatial correlation of the main macroeconomic indicators for the western regions, 2) both positive and negative externalities for the eastern regions and 3) the asymmetric influence of eastern and western regions on each other. Usually "impulses" from the western regions have a positive effect on the eastern regions, but the "impulses" from the eastern regions usually do not affect the western regions.
Observed and unobserved regional determinants of FDI inflows: micro level analysis of the food industry firms in Russia The development of Russian food industry is strategically important. Theoretically, the foreign capital inflow will help to renovate, modernize it and increase the productivity. But is it also interesting for foreign investors? What do foreign companies take into account when they invest in Russian food industry enterprises? Could it be special aspects of regional development (observed or unobserved) or only firm level data matters? Does the institutional environment in Russian regions significantly stimulate the inflow of foreign direct investment in Russian food industry enterprises or is the investor interested only in the size of a market? Two samples for 2009 and 2012 years of correspondingly about 5000 and about 7000 food industry companies of different subindustries from different Russian regions are analyzed to give the answer to these questions. The main idea of this investigation is to determine significant regional factors which effect the distribution of the FDI or to show that these items are not important for foreign investors. Russia has more than 80 regions and all of them are highly heterogeneous in terms of climate, geographical characteristics, level of economic and institutional development, industrial specialization, etc. Moreover, enterprises of different industries and subindustrues are different. In this research we take into account these facts investigating a hierarchical structure of the FDI distribution levels. This research consists of several parts: the theoretical part with hypotheses and the overview of the background and the empirical part with testing whether different regional characteristics like the infrastructure, taxation and the regulations in the region and in the neighboring ones play an important role. Spatial effects of these factors and of the economic development are also of our interest. The estimation of a multilevel binary model with spatial effects of analyzed factors gives the idea for the possible solution on the problem discovered above. The comparison of the results for two samples for different years and the investigation of dynamics also are taken into consideration.
The purpose of this study is to identify the spatial effects of the main macroeconomic indicators of the eastern and western regions of Russia. These regions diffr significantly in population densities and distances between cities. The main research question is the following: How do events in one of the western or eastern regions affect similar indicators in other western and eastern regions? Our analysis revealed: 1) a positive spatial correlation of the main macroeconomic indicators for the western regions, 2) both positive and negative externalities for the eastern regions and 3) a mutual but asymmetric influence of eastern and western regions. Usually "impulses" from the western regions have a positive effect on the eastern regions, but "impulses" from the eastern regions usually do not affect the western regions.