The textile and garment industries in the Russian regions: Spatial and econometric modeling

2021 ◽  
Vol 19 (9) ◽  
pp. 1685-1705
Author(s):  
Angi E. SKHVEDIANI ◽  
Kseniya S. KOZHINA
Keyword(s):  
Panel Data ◽  
Labor Productivity ◽  
Econometric Modeling ◽  
Russian Regions ◽  
Spatial Panel ◽  
Industrial Specialization ◽  
Analysis Technique ◽  
Spatial Panel Data ◽  
The Given ◽  
Garment Manufacturing

Subject. The article focuses of the industrial specialization of the Russian regions. Objectives. We test the technique for analyzing the regional industrial specialization with econometric toolkit, referring to the textile and garment industries in Russia. Methods. We conducted the econometric analysis, relying upon spatial panel data on the regional industrial specialization. We used localization coefficients of the metrics, such as revenue from sale of goods, average monthly pay of workers in the given industry, average headcount in the given industry and labor productivity. Results. We discovered that there is a spatial correlation of labor productivity in the textile and garment industries. The localization of those employed in the textile and garment manufacturing has a negative correlation with labor productivity in the regions. We traced a positive correlation of labor productivity in the regions and the localization of workers’ wages. Conclusions. The proven economic analysis technique helps identify and analyze correlations of regional industrial specialization indicators.

scholarly journals Spatial Dimensions of Sectoral Labor Productivity Convergence in Turkey: A Spatial Panel Data Approach

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Tuğrul Çınar
Keyword(s):  
Panel Data ◽  
Labor Productivity ◽  
Value Added ◽  
Conditional Convergence ◽  
Spatial Panel ◽  
Agriculture Sector ◽  
Structural Problems ◽  
Productivity Convergence ◽  
Spatial Dimensions ◽  
Spatial Panel Data

AbstractThe purpose of this study is to investigate spatial dimensions of interregional labor productivity convergence in Turkey between 2005 and 2011 period in three sector disaggregation. We employed spatial panel data approach to investigate the absolute and conditional beta convergence. Annual gross value added per worker data has been used as labor productivity proxy for 26 sub-regions. Analysis results show us that absolute and conditional convergence is highly significant for all agriculture, industry and services sector and also in sectors total. We also found that, while industry, services and sectors total show significant spatial dependency, there is no strong evidence of spatial interaction in agriculture sector for Turkey. Structural problems of Turkish agriculture sector are considered to be the main reasons behind this finding.

An econometric analysis of the regional industrial specialization: The Russian manufacturing industry case study

2020 ◽  
Vol 19 (9) ◽  
pp. 1765-1790
Author(s):  
T.Yu. Kudryavtseva ◽  
A.E. Skhvediani
Keyword(s):  
Manufacturing Industry ◽  
Service Sector ◽  
Econometric Models ◽  
Regional Economies ◽  
Russian Regions ◽  
Spatial Panel ◽  
Industrial Specialization ◽  
Spatial Panel Data ◽  
Workforce Productivity ◽  
Post Industrial

Subject. The article reviews the manufacturing industry in Russian regions, calculates the indicators of regional industrial specialization needed for development of econometric models of spatial panel data. Objectives. The purpose is to create a methodology for analyzing the regional industrial specialization based on econometric tools; to test it, using the case of the manufacturing industry, for determining the type of externalities in the Russian Federation. Methods. To build econometric models, we use methods of least squares and maximum likelihood. We apply localization ratios to assess regional industrial specialization in terms of the volume of employment, revenue and investment in manufacturing, workforce productivity, etc. Results. The findings show the clustering of regions by the level of productivity. The localization of manufacturing industry in regions in terms of localization of employment and localization of productivity is negatively related to productivity in the region. This can be explained by the transition of regional economies to the post-industrial mode, where the service sector becomes more important, and by possible over-industrialization and specialization of certain regions in the context of the need to develop related sectors and to build links between them. The presence of direct negative MAR externalities may indicate a need for further research in positive Porter and Jacobs externalities for Russian regions manufacturing industry. Conclusions. The developed methodology enables to identify and analyze relationships between regional industrial specialization and regional indicators; to specify the type of externalities and determine the existence of indirect and direct effects of industry localization.

scholarly journals A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns

2018 ◽  
Vol 10 (8) ◽  
pp. 2800 ◽  
Author(s):  
Rui Jin ◽  
Jianya Gong ◽  
Min Deng ◽  
Yiliang Wan ◽  
Xuexi Yang
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Interaction Model ◽  
Spillover Effects ◽  
Spatiotemporal Pattern ◽  
Regional Economic ◽  
Spatial Panel ◽  
Economic Agglomeration ◽  
Spatial Panel Data

Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern.

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