Evolution and influencing factors of global high-tech products trade network

doi:10.3969/j.issn.1004-9479.2023.06.2021644

Abstract

Abstract:

High-tech industry and commodity trade are the direct embodiment of a country or region's scientific and technological innovation strength. Based on the data from the UN Comtrade, this paper selected four years of 2003, 2008, 2013 and 2018 to build a global high-tech products trade network, analyzed and summarized the development situation, network structure characteristics and development trend of international trade in the high-tech industry. Based on the trade gravity model, technology gap theory and multi-dimensional proximity analysis, this paper used the QAP regression analysis method to study the influencing factors on the trade of high-tech products. The results show that: (1) the global trade volume of high-tech products is increasing, the network scale is expanding, and trade ties are becoming closer and more active. (2) The trade network presents the phenomenon of central polarization, forming the network structure of "three reunion cores" with China as the center in East and Southeast Asia, the United States as the center in North America and Germany as the center in Europe, showing the coexistence of "hub and spoke backbone network" and "radial regional network". (3)The technological gap and economic gap between the two countries play a positive role in promoting commodity trade, while geographical distance, language difference and institutional difference inhibit commodity trade, but the impact is weakening. The contribution of this paper is to explore the characteristics and evolution trend of global high-tech product trade network, and to explore the influencing factors of commodity trade from the dynamic and relational perspectives, which has certain policy implications for the development of high-tech industry and commodity trade in China.

Key words: high-tech products, trade network, evolution characteristics, multi-dimensional proximity, influencing factors

摘要：

高技术产业和商品贸易是一个国家或地区科技创新实力的直接体现。本文利用联合国商品贸易数据，选取2003、2008、2013和2018年四个年份构建全球高技术产品贸易网络，分析总结高技术产品国际贸易发展情况、网络结构特征及发展演化趋势。文章基于贸易引力模型、技术差距理论和多维邻近性关系分析视角，采用QAP回归分析方法，实证研究了经济、技术、文化、地理和制度等因素对高技术产品贸易的影响。研究发现：（1）全球高技术产品贸易量不断增加，网络规模不断扩大，贸易联系日趋紧密。（2）贸易网络出现中心极化现象，形成以中国为中心的东亚和东南亚、以美国为中心的北美和以德国为中心的欧洲“三团聚核”的网络结构，呈现跨洲大国贸易和地区邻近贸易的双重特点，以及“轴辐式骨干网络”和“放射状区域网络”双网叠加并存的发展态势。（3）两国/地区技术差距、经济差距对商品贸易起正向促进作用，地理距离、语言差异和制度差异对商品贸易有抑制作用，但抑制作用呈减弱趋势。本文的贡献在于探讨了全球高技术产品贸易网络特征及其演化趋势，并从动态演化和关系视角探讨了商品贸易的影响因素，对我国高技术产业发展和商品贸易有一定的政策启示。

关键词: 高技术产品, 贸易网络, 演化特征, 多维邻近性, 影响因素

Ruibo ZHOU, Yinan CHEN, Yuanhong QIN. Evolution and influencing factors of global high-tech products trade network[J]. World Regional Studies, 2023, 32(6): 1-13.

周锐波, 陈依楠, 覃远红. 全球高技术产品贸易网络演化及影响因素[J]. 世界地理研究, 2023, 32(6): 1-13.

Figures/Tables 8

Tab.1 Comparison table of high-tech products

高技术产业门类	《国民经济行业分类》（GB/T 4754-2017）	《国际贸易标准分类》SITC Rev.3编码
电子及通信设备制造业	3562/3563/3569/3832/3833/3841/3921/3922/3940/3931/3932/3933/3934/3939/3951/3952/3953/3971/3972/3973/3974/3975/3976/3979/3981/3983/3984/3985/3989/3982/3961/3962/3963/3969/3990	7284/76281/76382/76491/7285/76282/76383/7722/77318/76289/76384/7723/7611/76331/7641/776/7612/76333/7642/7781/76221/76335/76482/7786/76222/76381/76483/7787
医药制造业	2710/2720/2730/2740/2750/2761/2762/2770/2780	51393/51479/51571/5158/51461/51481/51572/541/51464/51563/51576/542/51471/51569/51578/89991
航空、航天器及设备制造业	3741/3742/3743/3744/3749/4343	71311/71441/71481/792/71319/71449/71491
医疗仪器设备及仪器仪表制造业	3581/3582/3583/3584/3585/3586/3589/4011/4012/4013/4014/4015/4016/4019/4021/4022/4023/4024/4025/4026/4027/4028/4029/4040/4090	74183/871/873/8996/774/872/874
计算机及办公设备制造业	3911/3912/3913/3914/3915/3919/3474/3475	76431/75122/7513/75995/76432/75123/752/75997/75121/75124/7591

Fig.1 Development of global high-tech products trade

Fig. 2 Spatial distribution of global trade volume of high-tech products

Tab.2 Top 10 countries or regions in the global import and export trade of high-tech products (US$100 million)

美国

2 282

美国

2 598

中国

3 963

美国

3 737

中国

6 131

美国

4 449

中国

7 886

美国

6 158

中国

1 446

德国

1 146

美国

3 155

中国

2 177

美国

3 138

中国

3 305

美国

3 577

中国

4 341

日本

1 309

中国

1 030

德国

2 185

德国

1 894

德国

2 479

中国香港

2 440

德国

3 017

中国香港

3 120

德国

1 241

中国香港

805

日本

1 554

中国香港

1 417

韩国

1 561

德国

1 948

韩国

2 191

德国

2 435

新加坡

900

英国

785

韩国

1 165

法国

1 101

日本

1 406

日本

1 214

中国香港

1 626

日本

1 352

英国

718

日本

738

法国

1 103

英国

990

法国

1 287

法国

1 191

日本

1 532

荷兰

1 328

韩国

694

新加坡

726

新加坡

987

日本

955

新加坡

1 180

英国

1 028

新加坡

1 354

英国

1 241

法国

672

法国

714

英国

844

荷兰

921

中国香港

958

荷兰

1 010

法国

1 314

法国

1 185

马来西亚

638

荷兰

620

荷兰

797

新加坡

829

荷兰

929

新加坡

842

马来西亚

1 269

新加坡

1 012

爱尔兰

534

意大利

419

马来西亚

726

比利时

689

马来西亚

874

韩国

797

荷兰

1 115

韩国

932

Fig.3 Spatial structure of global high-tech products trade network at the year of 2003， 2008， 2013 and 2018

Tab.3 Classification method of community sections based on weighted network

板块内部贸易比例与期望比例关系	板块对其他板块出口和进口贸易量之比
板块内部贸易比例与期望比例关系	$w l, e x / w l, i m ≥ 1$	$w l, e x / w l, i m < 1$
$w l / w l, t ≥ n l - 1 / (n - 1)$	双向溢出板块	主受益板块
$w l / w l, t < n l - 1 / (n - 1)$	主溢出板块	经纪人板块

Tab.3 Classification method of community sections based on weighted network

板块内部贸易比例与期望比例关系	板块对其他板块出口和进口贸易量之比
板块内部贸易比例与期望比例关系	$w l, e x / w l, i m ≥ 1$	$w l, e x / w l, i m < 1$
$w l / w l, t ≥ n l - 1 / (n - 1)$	双向溢出板块	主受益板块
$w l / w l, t < n l - 1 / (n - 1)$	主溢出板块	经纪人板块

Tab.4 Characteristics and types of the community sections in 2018

板块	主要国家或地区	内部贸易额/亿美元	总贸易额/亿美元	期望比例 /%	实际比例 /%	出口额/亿美元	进口额/亿美元	出口进口之比	板块类型
1	美国、加拿大、阿根廷、秘鲁、巴西、智利、澳大利亚等	1 836	18 109	0.37	0.10	4 580	11 693	0.39	经纪人板块
2	德国、以色列、瑞士、丹麦、英国、挪威、比利时、芬兰等	1 673	11 341	0.18	0.15	5 500	4 168	1.32	主溢出板块
3	荷兰、法国、丹麦、西班牙、意大利、瑞典、捷克、波兰等	1 351	9 181	0.22	0.15	3 547	4 283	0.83	经纪人板块
4	中国、马来西亚、日本、韩国、新加坡、越南、泰国、菲律宾等	5 840	21 438	0.16	0.27	11 058	4 540	2.44	双向溢出板块

Tab.5 QAP regression results of high-tech products trade network

变量	年份
变量	2003	2008	2013	2018
Intercept	8.934 (0.000)	10.050 (0.000)	8.172 (0.000)	10.232 (0.000)
GDP	0.603^*** (0.393)	0.573^*** (0.389)	0.583^*** (0.421)	0.522^*** (0.391)
PGDP	0.205^** (0.100)	0.179^* (0.086)	0.250^*** (0.131)	0.252^** (0.132)
TECH	0.298^*** (0.205)	0.246^*** (0.177)	0.236^*** (0.174)	0.256^*** (0.187)
LAN	1.479^*** (0.158)	1.186^*** (0.138)	1.062^*** (0.131)	1.210^*** (0.148)
DIS	－0.905^*** (－0.326)	－0.953^*** (－0.372)	－0.833^*** (－0.345)	－0.861^*** (－0.353)
INS	－0.291^*** （－0.147)	－0.217^** (－0.120)	－0.294^*** (－0.174)	－0.245^*** (－0.136)
R²	0.471	0.432	0.446	0.428
Adj R²	0.470	0.431	0.445	0.427
样本体积	2 450	2 450	2 450	2 450

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