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Research on the Impact of Logistics Infrastructure on Manufacturing Agglomeration

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DOI: 10.23977/ieim.2022.051208 | Downloads: 8 | Views: 259


Binghao Wei 1, Mingchang Chu 1, Jing Wang 1


1 Liaoning University of Technology School of Economics and Management, Jinzhou, China

Corresponding Author

Mingchang Chu


Logistics infrastructure is the foundation to guarantee the social activities of manufacturing enterprises. This paper collects and collates the panel data of 11 provinces and municipalities in eastern China from 2010 to 2019, selects the comprehensive road network density of roads and railways as the proxy variable of land transportation logistics infrastructure, uses location entropy to represent the degree of manufacturing agglomeration, applies the frontier econometric analysis method, establishes the threshold panel model, empirically tests the theory, and tests the spatial nonlinear economic impact of logistics infrastructure on manufacturing agglomeration. This paper draws the following conclusions: under the condition that the comprehensive road network density of logistics infrastructure is the threshold variable, the impact of logistics infrastructure on manufacturing agglomeration is non-linear, and the specific performance is the "inverted U" relationship of first strong and then weak. Consumption level, urbanization development level and opening degree promote manufacturing agglomeration. Labor cost, economic development level and government intervention negatively affect manufacturing agglomeration in the eastern region in order to guide the manufacturing enterprises to rationally distribute in space, to further guide the industrial clusters, to optimize the allocation of resources, and to provide a certain reference for the formulation of government policies and systems.


Logistics infrastructure, Manufacturing agglomeration, Sill panel model, non-linear, Impact study


Binghao Wei, Mingchang Chu, Jing Wang, Research on the Impact of Logistics Infrastructure on Manufacturing Agglomeration. Industrial Engineering and Innovation Management (2022) Vol. 5: 65-76. DOI:


[1] X.W, Wang. (2020) Research on Heterogeneity of Spatial Effects of Logistics Infrastructure on Regional Economic Development in China: Based on Spatial Dubin Model. Commercial Economic Research, (24): 95-98.
[2] K, Chen., L.F, Yin., J.Y, Wang., B.W, Han. (2020) An Empirical Study on Influencing Factors of Manufacturing Industry Agglomeration in China. Shanghai Economic Research, (10): 97-108.
[3] J, Lei. (2018) The Impact of China's Transportation Infrastructure Investment on Regional Economic Development. Beijing: Beijing Jiaotong University.
[4] Z.Q, Hou. (2018) An Empirical Analysis of the Effect of Transportation Infrastructure on Regional Tourism Economic Growth: A Spatial Econometric Model Based on Panel Data of China Province. Macroeconomic Research, (06): 118-132.
[5] L, Shen., Q.L, Dong., L, Zhang. (2014) Transportation Infrastructure Threshold, Logistics and Manufacturing Profitability. China Business and Market, 28(08): 14-19.
[6] B.L, Zhang. (2019) Research on the Threshold Effect of Highway Traffic Infrastructure on Economic Development. Economist, (06): 53-54.
[7] Y, Ma., Y.H, Qiu., X.Y, Wang. (2019) Urban Infrastructure, Technological Innovation and Regional Economic Development: Based on the Analysis of Intermediary Effect and Panel Threshold Model. Industrial Technology Economy, 38(08): 116-123.
[8] N.Y, Xu., Q, Chen. (2019) Research on Spatial Agglomeration Measurement and Dynamic Evolution of Manufacturing Enterprises in China. Statistics and Decision, 35(07):122-126.
[9] A. Condeço-Melhorado., Javier, Gutiérrez Puebla., Juan Carlos García Palomares. (2013) Influence of distance decay on the measurement of spillover effects of transport infrastructure: a sensitivity analysis. Geofocus: Revista Internacional de Ciencia y Tecnología de la Información Geográfica, (13_1): 22-47.
[10] Arup, M., Chandan, S., Marie-Ange, V.V. (2014) Trade liberalization, technology transfer,and firms’productive performance: The case of Indian manufacturing. Journal of Asian Economics, 33: 1-15. 
[11] H.X, Tang. (2017) Location Entropy Analysis of Manufacturing Agglomeration in Western Region from the Perspective of Transportation Infrastructure. Managing the World, (06): 178-179.
[12]  Moh’d Anwer Al-Shboul. (2017) Infrastructure framework and manufacturing supply chain agility: the role of delivery dependability and time to market. Supply Chain Management: An International Journal, 22(2): 172-185.
[13] Piyali, M., Aparna, S. (2020) Manufacturing agglomeration and export dynamics across Indian states. Indian Economic Review: Journal of Delhi School of Economics, 55(1): 3-26. 
[14] Z, Lu. (2019) Research on the Effect of Transportation Infrastructure Improvement on Manufacturing Agglomeration. Guiyang: GuiZhou University of Finance and Economics.
[15] G.C, Liu., J, Qin., Y, Wang. (2020) Empirical Analysis of Logistics Industry to Regional Economic Development under the Threshold of Transportation Infrastructure. Commercial Economic Research, (17): 97-100.
[16] H.B, Chen., C.P, Chen. (2018) FDI, Transportation Capacity and Manufacturing Industry Development: Empirical Analysis of Panel Threshold Model Based on 224 Cities. World Economy Study, (06): 123-134+137.
[17] Z.L, You., J.P, Zhao. (2018) Research on Threshold Effect of Logistics Industry Agglomeration on Manufacturing Upgrading. Logistics Technology, 41(07): 14-19.
[18] D.D, Wu. (2020) An Empirical Study on the Impact of Diversified Agglomeration on Economic Growth in China's Urban Agglomerations: Based on Panel Data Threshold Regression. Inquiry Into Economic Issues, (09): 90-99.
[19] Y.Q, Cao., X,L, Zhou. (2020) The Heterogeneous Characteristics of Manufacturing Agglomeration and Regional Economic Growth: A Threshold Effect Analysis from the Perspective of Factor Input. Shanghai Economy, (04): 5-20.
[20] Ermakova, A. (2020) Development of road transport infrastructure through the construction of a runway in the city of Tobolsk//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 918(1): 012-030. 
[21] M.H, Seo., S, Kim., Y.J, Kim. (2019) Estimation of dynamic panel threshold model using Stata. The Stata Journal, 19(3): 685-697.

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