Spatiotemporal evolution and driving factors of global production networks: An analysis based on the input-output technique

doi:10.1007/s11442-021-1863-7

Abstract

Abstract:

Global production networks have become the most important organizational platforms for coordinating international production activities, and their evolution patterns profoundly affect value distribution across the world. In this study, we shall firstly carry out an in-depth quantitative research to analyze the patterns and evolution of global production networks, using a long time-sequenced multi-region input-output table and the network analysis approach. Then based on the method of value-added decomposition, we will develop an index system to measure the degree of participation of regions in global production networks. Finally, we will try to identify the factors affecting the degree of participation of countries in global production networks by constructing a regression model. The results show that from 1995 to 2015, the evolution of global production networks measured by input-output linkages experienced four stages: expansion, contraction, re-expansion, and re-contraction. In addition, the core communities of global production networks evolved from two major production communities (Europe and the Americas) to three pillars (Europe, Americas, and Asia) while more segmented communities are mainly affected by geographical proximity. The latter consists of European, North American, South American, African and Asian communities. The evolution of the global production network pattern primarily manifests as a process of cooperation strengthening or weakening among communities, based on changes in the external environment and the need for individual development strategies. Meanwhile, the United States, Germany, and the United Kingdom have consistently ranked among the top entities in global production networks, whereas China, Russia, and Southeast Asia have the fastest rises in ranking. In addition, government efficiency, resources endowment, infrastructure conditions and technology levels play important roles in the participation in global production networks.

Key words: input-output table, network analysis, evolution pattern, value-added decomposition, participation degree, indicator system

ZHENG Zhi, CHEN Wei, LIANG Yi, ZHANG Yajing. Spatiotemporal evolution and driving factors of global production networks: An analysis based on the input-output technique[J].Journal of Geographical Sciences, 2021, 31(5): 641-663.

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Explanatory variables			Sources	Overviews
Control variables	Economic aggregate	GDP	World Bank	GDP (logarithm)
	Economic growth	GDP growth	World Bank	GDP growth
	Output per capita	GDP per capita	World Bank	GDP per capita (logarithm)
Infrastructure level	Infrastructure	Total railway mileage	World Bank	Railway (total mileage)
State power and institutional arrangement	Legal level	Force of law	World Bank	Legal power index (0 = weak, 12 = strong)
	Legal level	International murder rate	World Bank	International murder rate (every 100,000 people)
	Government efficiency	Customs clearance efficiency	World Bank	Logistics performance index: Efficiency of clearance procedures (1 = very low, 5 = very high)
	Government efficiency	Taxation of GDP	World Bank	Revenue (proportion of GDP)
	Openness	Net foreign direct investment (FDI) inflows	World Bank	Net inflows of FDI
	Openness	Import share	World Bank	Import of goods and services (proportion of GDP)
Science and education level	Science and education level	Educational expenditure	World Bank	Total public expenditure on education (proportion of GDP)
Resource endowment	Natural resources	Export of ore and metal	World Bank	Export of ore and metal (proportion of merchandise exports)
Resource endowment	Labor price	The proportion of male agricultural personnel	World Bank	Male agricultural personnel (proportion of male personnel)

Influencing factors	Intensity of participation	Range of participation	Position of participation	Comprehensive participation	Variance inflation factor (VIF)
GDP (logarithm)	1.6781^* (0.9608)	-5.9862 (3.6460)	-0.0119 (0.0179)	-0.0523 (0.2540)	3.923
GDP growth	-0.4581^* (0.2734)	1.5063 (1.0375)	0.0103^** (0.0051)	0.0857 (0.0723)	1.507
GDP per capita (logarithm)	-0.1473 (0.0956)	-1.0325^*** (0.3628)	-0.0028 (0.0018)	-0.0717^*** (0.0253)	3.596
Total railway mileage	2.4680^** (1.0283)	10.6174^*** (3.9021)	0.2100^*** (0.0191)	2.1518^*** (0.2718)	5.467
Force of law	1.4806^*** (0.4373)	-3.1977 (2.6594)	-0.0261 (0.0281)	-0.16267 (0.1156)	1.525
International murder rate	-0.4299^*** (0.0905)	-0.7893^** (0.3435)	-0.0013 (0.0017)	-0.0870^*** (0.0239)	1.324
Customs clearance efficiency	3.6329 (3.7688)	57.9892^*** (14.3008)	0.3584^*** (0.0701)	5.5934^*** (0.9961)	4.834
Taxation of GDP	0.8331^*** (0.2149)	3.3062^*** (0.8153)	0.0015 (0.0040)	0.1978^*** (0.0568)	1.857
Net FDI inflows	0.3815 (0.3594)	-0.644 (1.3637)	0.0181^*** (0.0067)	0.2247^** (0.0950)	3.740
Import of GDP	0.2093^*** (0.0477)	-0.2911 (0.1812)	-0.0007 (0.0009)	0.0123 (0.0126)	1.664
Educational expenditure of GDP	-1.7135 (1.0610)	5.1972 (4.0262)	0.0703^*** (0.0197)	0.6463^** (0.2804)	1.897
Proportion of ore and metal exports	-0.2128^*** (0.0773)	-0.7980^*** (0.2935)	-0.0007 (0.0014)	-0.0524^** (0.0204)	1.132
Proportion of male agricultural personnel	0.0507^** (0.0200)	0.3326 (0.2488)	-0.0024* (0.0012)	0.0396^** (0.0173)	2.241
F value	8.985	5.839	65.498	42.853
R square	0.453	0.350	0.858	0.798
P value	0.000	0.000	0.000	0.000