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.

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 Does Environmental Innovation Improve Environmental Productivity?—An Empirical Study Based on the Spatial Panel Data Model of Chinese Urban Agglomerations

2020 ◽  
Vol 17 (17) ◽  
pp. 6022
Author(s):  
Junwei Ma ◽  
Jianhua Wang ◽  
Philip Szmedra
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Spillover Effect ◽  
Value Added ◽  
Environmental Innovation ◽  
Spatial Panel ◽  
Different Types ◽  
Spatial Panel Data ◽  
Pollution Emissions ◽  
The Impact

Environmental productivity comprehensively measures economic growth and environmental quality. Environmental innovation is considered to be the key to solving economic and environmental problems. Therefore, discussing the impact of environmental innovation on environmental productivity will reveal its economic and environmental effects. This paper measures environmental productivity by value added per unit of pollution emissions (four types of emissions are used) using panel data of 10 Chinese urban agglomerations from 2003 to 2016 to analyze the spatial correlation of environmental productivity, and constructs a spatial panel data model to empirically test the impact of environmental innovation on environmental productivity. It was found that environmental productivity measured by value added per unit of carbon dioxide emissions (gross domestic product (GDP)/CO2) had a significant positive spatial spillover effect, and measured by value added per unit of sulfur dioxide emissions (GDP/SO2), smoke (dust) emissions (GDP/SDE), and industrial sewage emissions (GDP/IS) had a significant negative spatial spillover effect. Environmental innovation has a significant negative inhibitory effect on environmental productivity measured by GDP/SDE and GDP/IS, but no obvious effect measured by GDP/CO2 and GDP/SO2. Control variables such as economic development level, industrial agglomeration, foreign direct investment, and endowment structure factor also show significant differences in environmental productivity measured by value added per unit of pollution emissions. In addition, there are significant differences in direct effects of explanatory variables on environmental productivity of local regions and indirect effects on neighboring regions. These differences are also related to the types of pollution emissions. Therefore, policymakers should set different policies for different types of pollution and encourage different types of environmental innovation, so as to achieve reduced pollution emissions and improved environmental productivity.

scholarly journals Evaluating multiple spatial dimensions of economic growth in Brazil using spatial panel data models

2015 ◽  
Vol 56 (1) ◽  
pp. 1-31 ◽  
Author(s):  
Guilherme Mendes Resende ◽  
Alexandre Xavier Ywata de Carvalho ◽  
Patrícia Alessandra Morita Sakowski ◽  
Túlio Antonio Cravo
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Spatial Panel ◽  
Spatial Panel Data Models ◽  
Spatial Dimensions ◽  
Spatial Panel Data

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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