Social network analysis of textile trade in

doi:10.3969/j.issn.1004-9479.2024.05.20222270

Abstract

Abstract:

Based on the textile trade data of 66 countries along the Belt and Road from 2001 to 2020, this paper uses the social network analysis method to measure the network centrality, uses the block model method to investigate the competitive and complementary relationship of textile trade among countries along the Belt and Road, and uses the QAP method to analyze the structure of textile trade network and its influencing factors. The results show that since the implementation of the "Belt and Road" Initiative, the density of textile trade network has increased significantly, and the network presents an obvious core-edge structure. China has always been in the center of textile trade network. Before and after the "Belt and Road" Initiative is put forward, core countries such as China, India and Thailand changed from internal sector to two-way overflow sector, and textile trade competition existed among core countries. The influencing factors on the textile trade network of countries along the Belt and Road are, in descending order, economic scale, regional trade agreements between countries, population scale, cultural proximity and geographical distance. Based on the research conclusions and combined with the textile trade network pattern of the Belt and Road countries, this paper puts forward relevant policy suggestions to promote high-quality development of textile trade between China and "The Belt and Road" countries from the perspective of social network.

Key words: "The Belt and Road", textile trade network, centrality, CONCOR Model, QAP analysis

摘要：

基于2001—2020年“一带一路”沿线66个国家的纺织品贸易数据，运用社会网络分析方法测算网络中心性，运用块模型方法考察“一带一路”沿线内部各国纺织品贸易的竞争互补关系，并运用QAP方法对纺织品贸易网络结构及影响因素进行分析。研究发现：①“一带一路”倡议实施以来，纺织品贸易网络密度显著提升，网络呈现明显的核心-边缘结构，中国始终处于纺织品贸易网络中心位置。②“一带一路”倡议提出前后，中国、印度、泰国等核心国由内部型板块转为双向溢出板块且核心国之间存在纺织品贸易竞争关系。③影响因素对“一带一路”共建国家纺织品贸易网络的影响从大到小排列依次为：经济规模、国家间区域贸易协定、人口规模、文化近邻性以及地理距离。立足研究结论，结合“一带一路”共建国家纺织品贸易网络格局，提出社会网络视角下促进中国与“一带一路”共建国家纺织品贸易高质量发展的相关政策建议。

关键词: “一带一路”, 纺织品贸易网络, 中心性, 块模型, QAP分析

Xiaozhuo WANG. Social network analysis of textile trade in "The Belt and Road" countries[J]. World Regional Studies, 2024, 33(5): 18-30.

王晓卓. “一带一路”共建国家纺织品贸易的社会网络分析[J]. 世界地理研究, 2024, 33(5): 18-30.

Figures/Tables 7

Tab.1 QAP analysis variable selection and data sources

变量维度	变量名称	变量说明	数据来源
制度	区域贸易协定( $R T A i j$ )	i,j两国/地区在t年是否存在区域贸易协定，若存在记为1，否则为0，并以此构建0~1矩阵	WTO数据库
经济	两国/地区经济发展水平之和( $G D P i j_s u m$ )	以两经济体GDP之和构建自变量矩阵	世界银行全球发展指数(WDI)
人口	两国/地区人口之和( $P o P i j_s u m$ )	以经济体间人口之和构建自变量矩阵	世界银行全球发展指数(WDI)
空间	地理距离( $l n_d i s t w i j$ )	用经济体首都或主要城市间的距离构建自变量矩阵	CEPII数据库
文化	两国/地区是否相邻( $C o n t i g i j$ )	采用两国/地区是否同属于一个大陆板块，若属于记为1，否则为0，并以此构建0~1矩阵	CEPII数据库
文化	两国/地区是否有共同法律来源( $C o m l e g i j$ )	采用两国/地区是否拥有共同法律来源，若拥有记为1，否则为0，并以此构建0~1矩阵	CEPII数据库

Tab.1 QAP analysis variable selection and data sources

变量维度	变量名称	变量说明	数据来源
制度	区域贸易协定( $R T A i j$ )	i,j两国/地区在t年是否存在区域贸易协定，若存在记为1，否则为0，并以此构建0~1矩阵	WTO数据库
经济	两国/地区经济发展水平之和( $G D P i j_s u m$ )	以两经济体GDP之和构建自变量矩阵	世界银行全球发展指数(WDI)
人口	两国/地区人口之和( $P o P i j_s u m$ )	以经济体间人口之和构建自变量矩阵	世界银行全球发展指数(WDI)
空间	地理距离( $l n_d i s t w i j$ )	用经济体首都或主要城市间的距离构建自变量矩阵	CEPII数据库
文化	两国/地区是否相邻( $C o n t i g i j$ )	采用两国/地区是否同属于一个大陆板块，若属于记为1，否则为0，并以此构建0~1矩阵	CEPII数据库
文化	两国/地区是否有共同法律来源( $C o m l e g i j$ )	采用两国/地区是否拥有共同法律来源，若拥有记为1，否则为0，并以此构建0~1矩阵	CEPII数据库

Tab. 2 The centrality of the "Belt and Road" textile trade network in 2001, 2011 and 2020

年份	排名	点度中心度		介数中心性		接近中心性		特征向量中心性
年份	排名	国家	得分	国家	得分	国家	得分	国家	得分
2001	1	印度尼西亚	212	印度尼西亚	4 160	印度尼西亚	653	印度尼西亚	0.097
	2	泰国	206	泰国	3 196	泰国	665	泰国	0.097
	3	中国	199	印度	2 522	中国	679	中国	0.097
2011	1	中国	210	中国	2 809	中国	693	中国	0.091
	2	印度	206	印度	2 504	印度	701	印度	0.09
	3	泰国	200	印度尼西亚	2 093	泰国	713	泰国	0.089
2020	1	中国	211	中国	2 894	中国	656	中国	0.088
	2	印度	206	印度	2 384	印度	666	印度	0.087
	3	波兰	196	波兰	2 082	波兰	686	波兰	0.086

Fig. 1 Core-periphery structure of textile networks in the "Belt and Road" countries/regions in 2001， 2006， 2017 and 2020

Tab. 3 Segmentation of textile trade networks in the "Belt and Road" countries in 2006 and 2020

年份	分块	国家
2006年 (52国)	块1	阿尔巴尼亚、格鲁吉亚、亚美尼亚、摩尔多瓦、黑山、马其顿、波黑、斯洛文尼亚
	块2	斯洛伐克、匈牙利、巴林、捷克、拉脱维亚、立陶宛
	块3	也门、克罗地亚、巴基斯坦、印度、印度尼西亚、保加利亚、叙利亚、哈萨克斯坦、吉尔吉斯斯坦、柬埔寨、菲律宾、伊朗、斯里兰卡、阿联酋、泰国、阿塞拜疆、塞尔维亚、俄罗斯、以色列、马来西亚、越南、土耳其、乌克兰、罗马尼亚、新加坡、沙特阿拉伯
	块4	塞浦路斯、波兰、卡塔尔、马尔代夫、不丹、蒙古、约旦、阿曼、中国、爱沙尼亚、科威特、黎巴嫩
2020年 (48国)	块1	阿尔巴尼亚、黑山、阿塞拜疆、摩尔多瓦、波黑、保加利亚、斯洛伐克、格鲁吉亚、塞尔维亚
	块2	亚美尼亚、白俄罗斯
	块3	黎巴嫩、柬埔寨、俄罗斯、塞浦路斯、印度尼西亚、以色列、约旦、哈萨克斯坦、中国、吉尔吉斯斯坦、缅甸、阿联酋、乌兹别克斯坦、马来西亚、科威特、新加坡、巴基斯坦、菲律宾、塔吉克斯坦、卡塔尔、泰国、印度、斯洛文尼亚、沙特阿拉伯、蒙古、土耳其、越南、斯里兰卡
	块4	拉脱维亚、爱沙尼亚、捷克、匈牙利、克罗地亚、乌克兰、罗马尼亚、立陶宛、波兰

Fig. 2 Simplified diagram of the relationship between the blocks for 2006， 2011， 2017 and 2020

Tab. 4 QAP correlation analysis

变量	实际相关系数	显著性水平	平均值	标准差	最小值	最大值	$P ≥ 0$	$P ≤ 0$
$R T A i j$	0.242	0.000	0.000	0.062	－0.202	0.230	0.000	1.000
$G D P i j_s u m$	0.510	0.000	0.000	0.100	－0.334	0.331	0.000	1.000
$P o P i j_s u m$	0.348	0.001	－0.006	0.097	－0.252	0.391	0.001	0.999
$l n_d i s t w i j$	－0.137	0.040	0.000	0.080	－0.304	0.266	0.960	0.004
$C o n t i g i j$	0.294	0.000	－0.001	0.042	－0.134	0.157	0.000	1.000
$C o m l e g i j$	0.075	0.151	0.001	0.071	－0.233	0.255	0.151	0.849

Tab. 4 QAP correlation analysis

变量	实际相关系数	显著性水平	平均值	标准差	最小值	最大值	$P ≥ 0$	$P ≤ 0$
$R T A i j$	0.242	0.000	0.000	0.062	－0.202	0.230	0.000	1.000
$G D P i j_s u m$	0.510	0.000	0.000	0.100	－0.334	0.331	0.000	1.000
$P o P i j_s u m$	0.348	0.001	－0.006	0.097	－0.252	0.391	0.001	0.999
$l n_d i s t w i j$	－0.137	0.040	0.000	0.080	－0.304	0.266	0.960	0.004
$C o n t i g i j$	0.294	0.000	－0.001	0.042	－0.134	0.157	0.000	1.000
$C o m l e g i j$	0.075	0.151	0.001	0.071	－0.233	0.255	0.151	0.849

Tab. 5 QAP regression analysis

变量	非标准化回归系数	标准化回归系数	显著性概率	$P ≥ 0$	$P ≤ 0$
$R T A i j$	1.619 6	0.212 8	0.000	0.000	1.000
$G D P i j_s u m$	0.313 9	0.277 7	0.000	0.000	1.000
$P o P i j_s u m$	0.001 3	0.188 6	0.007	0.007	0.993
$l n_d i s t w i j$	－0.292 3	－0.075 4	0.023	0.978	0.023
$C o n t i g i j$	1.939 9	0.161 2	0.000	0.000	1.000
$C o m l e g i j$	0.343 2	0.047 9	0.059	0.059	0.941

Tab. 5 QAP regression analysis

变量	非标准化回归系数	标准化回归系数	显著性概率	$P ≥ 0$	$P ≤ 0$
$R T A i j$	1.619 6	0.212 8	0.000	0.000	1.000
$G D P i j_s u m$	0.313 9	0.277 7	0.000	0.000	1.000
$P o P i j_s u m$	0.001 3	0.188 6	0.007	0.007	0.993
$l n_d i s t w i j$	－0.292 3	－0.075 4	0.023	0.978	0.023
$C o n t i g i j$	1.939 9	0.161 2	0.000	0.000	1.000
$C o m l e g i j$	0.343 2	0.047 9	0.059	0.059	0.941

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