scholarly journals The Effect of Urban Services Development on Regional Economic Growth in China ——Based on Provincial Panel Data Analysis

2013 ◽  
Vol 04 (01) ◽  
pp. 6-11
Author(s):  
Yuan Gao ◽  
ABDUL Razak bin Chik
Keyword(s):  
Economic Growth ◽  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Regional Economic Growth ◽  
Regional Economic ◽  
Urban Services

scholarly journals Government Size dan Pertumbuhan Ekonomi di Indonesia

2020 ◽  
Vol 20 (2) ◽  
pp. 212-230
Author(s):  
Ary Ratna Santika ◽  
Riatu Mariatul Qibthiyyah
Keyword(s):  
Economic Growth ◽  
Data Analysis ◽  
Panel Data ◽  
Linear Relationship ◽  
Panel Data Analysis ◽  
Interval Data ◽  
Regional Economic Growth ◽  
Government Size ◽  
Regional Economic ◽  
Negative Effect

The aim of this research to investigate the effect of government size on regional economic growth in Indonesia. This study uses panel data analysis with using two years of interval data from 2009 until 2015. The results showed that government size has a significant effect on economic growth and it has a non-linear relationship. According the estimation from the model, the threshold government size on regional economic growth is 38.98%. Above the threshold, an increase in government size will instead have a negative effect on economic growth. ------------------------------------------ Penelitian ini bertujuan untuk meneliti pengaruh government size terhadap pertumbuhan ekonomi daerah di Indonesia. Penelitian ini menggunakan analisis data panel dengan menggunakan data dua tahun interval dari tahun 2009 hingga 2015. Hasil penelitian menunjukkan bahwa government size berpengaruh signifikan terhadap pertumbuhan ekonomi daerah dan berpengaruh secara tidak linier. Berdasarkan hasil model, dapat dihitung bahwa threshold untuk government size terhadap pertumbuhan ekonomi daerah adalah sebesar 38,98%. Jika pertumbuhan ekonomi daerah lebih dari itu, peningkatan government size akan berdampak negatif pada pertumbuhan ekonomi.

Assessing the Impact of the Factors in the Digital Transformation on the Regional Economic Growth

REGIONOLOGY ◽  
2021 ◽  
Vol 29 (3) ◽  
pp. 486-510
Author(s):  
Tatyana V. Mirolyubova ◽  
Marina V. Radionova
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Fixed Effects ◽  
Digital Transformation ◽  
Regional Economic Growth ◽  
Regional Economic ◽  
The Russian Federation ◽  
Regional Product ◽  
The Impact ◽  
Regions Of Russia

Introduction. The scientific problem under consideration is of particular relevance due to the need to assess the impact of the factors in the digital transformation of the regional economy and in the economic growth on the economic development of the regions of the Russian Federation. Based on the research conducted, the article presents an econometric assessment of the dependence of the level of the gross regional product per capita in the regions of Russia on such factors as digital labor and digital capital. Materials and Methods. The authors analyzed panel data from the Federal State Statistics Service covering 87 regions of Russia for the period from 2010 to 2018. The research methodology is based on the use of the Cobb–Douglas production function, statistical and correlation data analysis, as well as on econometric methods for studying panel data. Results. To analyze the impact of the digital transformation of the economy on the regional economic growth of the regions of Russia, various models based on panel data have been considered, such as the pooled model, fixed effects models, random effects models, as well as time-varying effects models using dummy variables. Based on statistical criteria, the best model has been chosen and conclusions have been drawn about the nature of the impact of the digital transformation indicators on the gross regional product per capita in the regions of Russia. Discussion and Conclusion. The results of econometric modeling have demonstrated that digital factors in economic growth (digital labor, digital capital), along with common factors in economic growth (labor and capital), affect the regional economic growth. According to the regional data for the period from 2010 to 2018, the time fixed effects model has proved to be the best model of the impact of the factors in economic growth and digital transformation on the economic development of the regions of the Russian Federation. The research results can be used when developing a public policy aimed at stimulating the digital transformation of the regional economy.

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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