Russia has one of the most complex geographical positions in the world. The total area of its territory includes 17.125 million square km accounting for nearly one-eighth of the Earth’s land surface and its coastline is the second longest in the world. Its geographical complexity is also reflected in its regional structure. In total, Russia has 83 regions that are grouped into 7 federal districts. Out of these 83 regions 47 have the status of an oblast, 21 have the status of a republic, 9 are krais, 4 are autonomous okrugs and 2 are federal cities. Although Russia is a Federative Republic, the level of autonomous power its regions differ in a great extent contributing further to its structural administrative complexity. In this regard, geographical complexity of Russia is also converted into its economic performance and creates huge differences between its regions in terms of economic development. Unlike its European neighbors or other developed large countries, Russia is a country with growing interregional inequalities (Zubarevich and Safronov, 2010). The regional disparities in Russia were always present throughout the Soviet period and started to increase further since transition started in the 1990s due to structural processes of reorganization and reallocations of resources taking place in the country (Benini and Czyzewski, 2007). Prior to that, the Soviet government pledged the equalization of socioeconomic conditions across regions, between rural and urban areas, and among the 100-plus ethnicities of the Soviet Union as one of their major goals (Liebowitz, 1989). Many regions containing relatively competitive economic sectors have coped with transition with relative success, although the overwhelming majority of the regions had serious problems trying to adjust to the market economy (Sutherland and Hanson, 1996). Due to the fact that many regions were economically tied to few economic sectors or even large enterprises (Benini and Czyzewski, 2007), development of certain sectors on a countrywide scale and stagnation of other sectors opened way to disparities further (Dunford and Smith, 2000). Majority of the regions of Urals, Siberia and the Far East are traditionally considered as disadvantaged ones due to low per capita GDP and slow economic growth whereas the cities of Moscow and St. Petersburg along with other major metropolitan areas are traditionally among the leaders in terms of economic development and the speed of economic growth (Lehmann and Silvagni, 2013).
In this analysis, we will try to address the determining role of the centrality and other basic locational parameters like costal vs. inland, border vs interior location of the Russian regions in determining the GRP growth, regional income per capita and the growth rate of the regional per capita. Thus, we will try to identify whether it plays a role to be located in the center compared to the rest of the regions or on the edge (border or coastal) in having higher or lower rates of regional economic growth. In doing this we rely on a simple logic that the economic performance of a region differs depending on its location relative to the rest of the regions. Thus, based on the logic of the classical theories of spatial economics, we expect the border and coastal regions to have higher income per capita, GRP per capita and GRP per capita growth rates compared to interior, inland and peripheral regions. This is due to the fact that regions adjacent to neighboring countries and coastal regions tend to have more trade with other countries simply because of their geographic location. Majority of the tradable goods pass through borders or seaports prior to be delivered to foreign markets. The role of centrality might seem ambiguous since being located in the center of the country and larger GRP implies more trade flows from other regions. Thus, being situated between core regions creates “development corridors” providing better communication and flows of innovation that enhance economic growth (Friedmann, 1967). Nevertheless, Vardomsky (2002), states that the border regions of Russia have low contacts with the world economy and tend to be depressive. The regions adjacent to the Baltic Sea region are far more integrated to the world economy than the rest of the Russian regions (Klemeshev et al. 2015).
For our analysis, we retrieve data on GRP and income per capita for all 83 internationally recognized regions of Russia. For centrality we use the most commonly used measure proposed by Keeble et al. (1982) and Keeble et al. (1988) where Mj is the GRP of region j and is the distance from region i to j . The larger the economic size of the region the higher the index. The more central the region relative to the rest of the regions the larger the index. We take the GRP and income per capita growth for periods of 2000-2004, 2005-2009, 2010-2014 and actual rates for 2014. Thus, we estimate the following OLS model taking the natural logarithmic form of the variables:
Here, Cj stands for the centrality degree of region i. The dummy1 = 1 for region i if it is a border region and dummyi = 0 if it is not a border region. Consequently, dummy1 = 1 if the region i is a coastal region and Coastral dummyi = 0 if it is not. The results of the estimation are shown in Table 1 below:
Table 1: Results of the OLS estimation:
GRP growth vs. centrality, border, coastal region 2000-2004:
Income per capita vs. centrality, border, coastal region 2000-2004:
Income per capita growth vs. centrality, border, coastal region 2000-2004:
* - statistical significance at α=0.10
** - statistical significance at α=0.05
*** - statistical significance at α=0.01
Source: Prepared by the Author based on data from Russian Federation Federal State Statistics Service
As it can be seen from Table 1, the effect of the centrality turns out to be ambiguous and in explaining the growth of GRP per capita and the growth of income per capita in Russian regions. The sign of the variable changes from period to period as well as its level of statistical significance. However, it has rather high explicative power in explaining the income per capita. Regarding the income per capita, its sign is strictly positive for all periods under consideration suggesting that central regions tend to have higher income per capita. However, if to take into account the coefficients obtained, then it becomes clear that the effect of centrality on income per capita at regional level is rather low. The other two dummy variables of border and coastal location also appear to be significant in explaining the per capita in the regions of Russia and insignificant in explaining the growth of GRP and of income per capita. Interestingly, the border dummy appears to have a negative sign suggesting a negative relationship with income per capita whereas the sign of the coastal location dummy turns out to be positive suggesting a positive relationship, which more reasonable. According to our estimations, regions of Russia located along land borders on average have nearly 20% lower income per capita confirming Vardomsky (2002). The coastal regions of russia, on the other hand, tend to have around 42% higher income per capita. The dummy variables of border location and coastal location do not have significant effect on the change of GRP per capita and income per capita.
The case of Russia remains to be an interesting subject for regional studies and geography is one of the key instruments in explaining and unraveling many regularities. Our attempts to explain the differences in GDP per capita growth, income per capita and income per capita growth between all 83 regions of Russia through such geo-economic and geographic variables like centrality, border and coastal location have shown that these explanatory variables are not relevant in explaining the dynamics of income and GRP per capita growth. However, the above mentioned variables appear to be relevant and statistically significant in explaining the interregional differences in income per capita. Moreover, contrary to the fundamental theories of the spatial economics, location along the land border has negative effect on income per capita whereas being located along a sea shore has a positive effect on income per capita.
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