Tag: R12

ASSESSMENT OF THE INTERCONNECTEDNESS OF CITIES IN THE RUSSIAN FAR EAST

Inna MANAEVA

Associate Professor of the Department of World Economy, Belgorod State National Research University, Russia, http://www.bsu.edu.ru

In.manaeva@yandex.ru

Anna TKACHEVA

Assistant of the Department of World Economy, Belgorod State National Research University, Russia, http://www.bsu.edu.ru

atkacheva1993@gmail.com

Elena CHENTSOVA

Associate Professor of the Department of Economics and Management, National University of Science and Technology “MISiS”(Stary Oskol branch), Russia,http://sf.misis.ru

chencowa@mail.ru

Elena ILYICHEVA

Deputy Director for Educational and Methodological Work, National University of Science and Technology “MISiS”(Stary Oskol branch), Russia,http://sf.misis.ru

EV.ilicheva@yandex.ru

Abstract

Today, determining the priorities of spatial and economic development of Russian cities is a key strategic goal in the Russian Federation. Acting as points of growth and connecting elements of economic processes, cities form a common framework of settlement. Existing urban disparities distort the territorial space, demonstrate its insufficient integrity, which reduces the quality of life of the population and poses a threat to socio-political stability.

The purpose of the study is to determine the features of the interconnectedness of cities in the Russian Far East using the Moran index. The estimation method is based on the calculation of the global and local Moran indices to determine the effects of connectivity of territories by indicators: “population size”, “population density”, “volume of products shipped per capita”, “average monthly salary”. The information base was the data of the Federal State Statistics Service, the distance calculation was carried out according to the data of the automobile portal. The object of the study is the cities of the Far Eastern Federal District, with a population of more than 100 thousand people in 2017.  The calculations made it possible to determine the type (direct and reverse) and the strength of interterritorial relations according to the considered parameters. According to the indicators “population size”, “population density”, there is a negative autocorrelation, according to the indicators “volume of products shipped per capita”,” average monthly salary”, there is a positive autocorrelation. The calculations revealed the presence of polarization in the territory of the Russian Far East. The strongest relationships are between Vladivostok (LISA -0,314), Khabarovsk (LISA -0,026) in terms of population; Artem (LISA -0,165) Vladivostok (LISA -0,084) – population density; Artem (LISA 0.116), Komsomolsk-on-Amur (LISA -0,036) – the volume of products shipped per capita; Ussuriysk (LISA 0.081), Artem (LISA 0.092) – average monthly wages.

The scientific significance of the conducted research consists in the development of theoretical and methodological provisions in relation to the assessment of spatial interterritorial relations. In the future, work will continue in terms of studying autocorrelation in dynamics, expanding the analyzed indicators and identifying spatial and temporal shifts, for a deeper understanding of the patterns of spatial development of cities.

Keywords: Moran index, spatial autocorrelation, inter-territorial connection, city

JEL classification: R12

 pp. 123-133

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DIVERSITY OR SPECIALIZARION? UNDERSTANDING KNOWLEDGE SPILLOVER MECHANISMS IN CHINA

Shicong XU

PhD candidate, Department of Agricultural, Environmental and Development Economics, Suite 250 Ag. Administration Building, 2120 Fyffe Rd, The Ohio State University, Columbus OH 43210, USA

Shicong.x@gmail.com

Abdoul G. SAM

Professor, Department of Agricultural, Environmental and Development Economics, Suite 250 Ag. Administration Building, 2120 Fyffe Rd,  The Ohio State University, Columbus OH 43210, USA, Corresponding author

Sam.7@osu.edu

Abstract

China’s rise to the top echelons of the world’s economies was accompanied by an expeditious growth in domestic patent applications. Not surprisingly, this phenomenon has spawned a growing literature trying to sort out the determinants of patented research in China. However, mostly due to data limitations, many of the papers on this topic use aggregated innovation data at the industry, prefecture, or province levels. In this paper, we examine the empirical validity of important theories of knowledge spillover in the context of China at a micro-level, using a firm-level panel dataset comprised of publicly traded companies listed in the Shanghai and ShenZhen Stock Exchanges during the 2006-2010 period. Our study sheds light on whether locating near innovative firms increases patenting activity in general, regardless of the industry membership of these neighboring firms. We also explore how industry makeup, measured by the number of firms in the same or different industries, affects firm-level patenting activity. Our econometric results show that the number of patent applications by firms in close geographic proximity of a firm of interest has a significant and positive impact on that firm’s successful patent applications. In addition, we find that proximity to firms in the same industry reduces innovation while locating near firms from different industries stimulates innovation.

Keywords: patents, knowledge diffusion, MAR spillover, Jacobs spillover, China

JEL classification: O31, O32, O33, R12, D22

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ANALYSIS OF URBAN CONNECTIVITY EFFECTS OF THE SOUTHERN FEDERAL DISTRICT

Inna MANAEVA

Associate Professor of the Department of World Economy, Belgorod State National Research University, Russia, http://www.bsu.edu.ru

In.manaeva@yandex.ru

Anna TKACHEVA

Assistant of the Department of World Economy, Belgorod State National Research University, Russia, http://www.bsu.edu.ru

atkacheva1993@gmail.com

Abstract

Today, existing urban imbalances in Russia distort the territorial space, demonstrate its insufficient integrity, which leads to high differences in the quality of life of citizens and social instability. In order to make effective and scientifically sound management decisions, it is necessary to understand the mechanisms underlying the functioning of cities, which actualizes the study of the effects of their connectivity in territorial space. The aim of the study is to develop an approach that allows us to determine the effects of urban connectivity in territorial space. The estimation method is based on the calculation of global and local Moran indices to determine the effects of the connectivity of territories by indicators: “population size”, “migration growth”, “volume of shipped products per capita”. The information base was the data of the Federal State Statistics Service, the calculation of distances was carried out according to the data of the automobile portal. The object of the study is the cities of the Southern Federal District, with a population of more than 100 thousand people in 2017. The calculations made it possible to determine the type (direct and reverse) and the strength of inter-territorial relations according to the parameters under consideration: according to the indicator “population size” there is a negative autocorrelation, according to the indicator “migration growth” and “volume of shipped products per capita” – positive autocorrelation. According to the indicator of the volume of shipped products per capita, polarization was revealed: Volgograd and Volzhsky are disconnected from the rest of the group of cities, while they do not have a significant impact on nearby territories. The strongest direct inter-territorial links are identified in the group of leading cities relative to each other. The indicator of «migration growth» observes significant inverse effects, largely Maykop falls into the zone of influence of leading cities.The practical importance lies in the possibility of using the results obtained by regional authorities in developing a strategy for the development of cities and regions in terms of identifying the production clusters of the region.

Keywords: Moran index, spatial autocorrelation, inter-territorial connection, city

JEL classification: R12

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