| 4,399 | 81 | 1266 |
| 下载次数 | 被引频次 | 阅读次数 |
数字经济发展对全球制造业增加值贸易网络产生系统性冲击。本文基于ADB-MRIO2022数据库,应用前沿的有向加权复杂网络分析方法刻画2010—2021年全球制造业增加值贸易网络特征、动态演化趋势和我国制造业在全球增加值贸易网络中的地位变化,并实证检验数字经济对一国制造业增加值贸易网络地位的影响。研究发现,制造业增加值贸易的区域化属性逐渐增强,其中美洲地区(包括北美洲和南美洲)最为明显,美国正逐步分散贸易风险,实现出口市场多元化,降低其主导的局域网络脆弱性,而中国对美国的贸易依存度过大,不利于以中国为主导的局域网络应对特定性冲击。中国在全球制造业增加值贸易网络中占据中心地位,但在高技术制造业网络中还有进一步上升空间。发展数字经济在总体上有利于提高一国在全球制造业增加值贸易网络中对核心资源的控制能力,占据“织网者”优势,扩大贸易影响力,但仅对处在网络中心与近中心的国家的接近中心性有显著促进作用。新兴经济体要打破发达国家的制造业封锁困境,必须提升增加值贸易流量与价值创造能力,实现主要出口对象国相对多元化。本文发现逆全球化产业链供应链重构具有长期结构性特征,揭示了利用数字经济推动我国制造业从中低端迈向高端,充分释放自身蕴藏的巨大发展空间具有重要性和迫切性。
Abstract:The development of digital economy has a systemically impact on the global manufacturing value-added trade network. Based on the ADB-MRIO2022 database, this paper uses cutting-edge directed weighted complex network analysis method to characterize the characteristics, dynamic evolutionary trend of the global manufacturing value-added trade network and position change of China's manufacturing industry in the global value-added trade network from 2010 to 2021, and empirically examines the impact of digital economy on the position of a country's manufacturing industry in the value-added trade network.The results show that the regionalization of value-added trade in manufacturing industry is increasing,especially in Americas, the United States is gradually dispersing trade risk, diversifying export markets, reducing the vulnerability of its dominant local area network, yet China's high trade dependence on the United States is not conducive to China-oriented local network dealing with specific impact. China occupies a central position in the global manufacturing value-added trade network, but there is room for further rise in the high-tech manufacturing network. On the whole, the development of digital economy is conducive to improving a country's ability to control core resources, hold netting advantage and expand trade influence in the global manufacturing value-added trade network, but it can only significantly promote the closeness centrality of countries in the center and near center of the network. In order to break the manufacturing blockade dilemma of developed countries, emerging economies must enhance value-added trade flow and value creation ability, and realize the relative diversification of main export target countries. This paper finds that the reconstruction of the anti-globalization industry chain and supply chain may have long-term structural characteristics, revealing the great importance and urgency of using digital economy to promote China's manufacturing industry from low and middle end to high end and fully release its huge potential of development.
[1]蔡跃洲,马文君.数据要素对高质量发展影响与数据流动制约[J].数量经济技术经济研究, 2021, 38(3):64-83.
[2]杜运苏,彭冬冬.制造业服务化与全球增加值贸易网络地位提升:基于2000-2014年世界投入产出表[J].财贸经济, 2018, 39(2):102-117.
[3]范鑫.数字经济发展、国际贸易效率与贸易不确定性[J].财贸经济, 2020, 41(8):145-160.
[4]黄祖南,郑正喜.复杂产业网络度中心性研究[J].统计研究, 2021, 38(5):147-160.
[5]荆林波,袁平红.全球价值链变化新趋势及中国对策[J].管理世界, 2019, 35(11):72-79.
[6]刘斌,魏倩,吕越,等.制造业服务化与价值链升级[J].经济研究, 2016, 51(3):151-162.
[7]刘斌,潘彤.人工智能对制造业价值链分工的影响效应研究[J].数量经济技术经济研究, 2020, 37(10):24-44.
[8]裴长洪,倪江飞,李越.数字经济的政治经济学分析[J].财贸经济, 2018, 39(9):5-22.
[9]施炳展,李建桐.互联网是否促进了分工:来自中国制造业企业的证据[J].管理世界, 2020, 36(4):130-149.
[10]王直,魏尚进,祝坤福.总贸易核算法:官方贸易统计与全球价值链的度量[J].中国社会科学, 2015(9):108-127, 205-206.
[11]吴翌琳.国家数字竞争力指数构建与国际比较研究[J].统计研究, 2019, 36(11):14-25.
[12]谢富胜,吴越,王生升.平台经济全球化的政治经济学分析[J].中国社会科学, 2019(12):62-81, 200.
[13] Abrell T, Pihlajamaa M, Kanto L, et al. The Role of Users and Customers in Digital Innovation:Insights from B2B Manufacturing Firms[J].Information&Management, 2016, 53(3):324-335.
[14] Almog A, Squartini T, Garlaschelli D. A GDP-Driven Model for the Binary and Weighted Structure of the International Trade Network[J]. New Journal of Physics, 2015, 17(1):169-184.
[15] Amador J, Cabral S. Networks of Value-Added Trade[J]. The World Economy, 2017, 40(7):1291-1313.
[16] Blondel V D, Guillaume J L, Lambiotte R, et al. Fast Unfolding of Communities in Large Networks[J]. Journal of Statistical Mechanics:Theory and Experiment, 2008(10):155-168.
[17] Bessen J. Automation and Jobs:When Technology Boosts Employment[J]. Economic Policy, 2019, 34(100):589-626.
[18] Chaney T. The Network Structure of International Trade[J]. American Economic Review, 2014, 104(11):3600-3634.
[19] Fortunato S. Community Detection in Graphs[J]. Physics Reports, 2010, 486(3-5):75-174.
[20] Goldfarb A, Tucker C. Digital Economics[J]. Journal of Economic Literature, 2019, 57(1):3-43.
[21] Graetz G, Michaels G. Robots at Work[J]. Review of Economics and Statistics, 2018, 100(5):753-768.
[22] Gupta R, Mejia C, Kajikawa Y. Business, Innovation and Digital Ecosystems Landscape Survey and Knowledge cross Sharing[J]. Technological Forecasting and Social Change, 2019, 147:100-109.
[23] Jahanmir S F, Silva G M, Gomes P J, et al. Determinants of Users’ Continuance Intention toward Digital Innovations:Are Late Adopters Different?[J]. Journal of Business Research, 2020, 115:225-233.
[24] Jones C I, Tonetti C. Nonrivalry and the Economics of Data[J]. American Economic Review, 2020, 110(9):2819-2858.
[25] Koopman R, Wang Z, Wei S J. Tracing Value-Added and Double Counting in Gross Exports[J]. American Economic Review, 2014, 104(2):459-494.
[26] Nambisan S, Wright M, Feldman M. The Digital Transformation of Innovation and Entrepreneurship:Progress, Challenges and Key Themes[J].Research Policy, 2019, 48(8):1-9.
[27] Wang Z, Wei S J, Zhu K. Quantifying International Production Sharing at the Bilateral and Sector Levels[R]. NBER Working Paper, 2013.
[28] Wang Z, Wei S J, Yu X, et al. Characterizing Global Value Chains:Production Length and Upstreamness[R]. NBER Working Paper, 2017.
[29] Wang Z, Wei S J, Yu X, et al. Measures of Participation in Global Value Chains and Global Business Cycles[R]. NBER Working Paper, 2017.
[30] Watanabe C, Naveed K, Tou Y, et al. Measuring GDP in the Digital Economy:Increasing Dependence on Uncaptured GDP[J]. Technological Forecasting and Social Change, 2018, 137:226-240.
①该方法源自“Wang Z, Wei S J, Zhu K. Quantifying International Production Sharing at the Bilateral and Sector Levels[R]. NBER Working Paper, 2013”,在后续学术研究中对该方法的引用通常取此三位作者姓氏的第一个字母,缩写为WWZ法。
①UIBE GVC Indicators由对外经济贸易大学全球价值链研究院构建,全称为“UIBE GVC Indicators?2016, Research Institute for Global Value Chains, University of International Business and Economics”,网址为http://rigvc.uibe.edu.cn/english/D_E/database_database/index.htm。
②数据来源:https://mrio.adbx.online。
③因篇幅所限,国家名称与制造业部门分类描述以附录1展示,见《统计研究》网站所列附件。下同。
④该方法源自“Wang Z, Wei S J, Yu X, Zhu K. Characterizing Global Value Chains:Production Length and Upstreamness[R]. NBER Working Paper, 2017”,在后续学术研究中对该方法的引用通常取此四位作者姓氏的第一个字母,缩写为WWYZ法。
①因篇幅所限,网络阈值密度变化迭代区间以附图1展示。
①因篇幅所限,2021年世界制造业增加值贸易静态网络以附图2展示。
②因篇幅所限,制造业增加值贸易动态网络密度演进图以附图3展示。
①“低端锁定”是指一国产业长期处于全球价值链低端环节的发展状态,由于资源配置效率低、产业关联效应和协同效应弱,因此难以提高产品附加值;“高端挤出”是指随着发展中国家制造业向全球价值链中高端升级,其优势产业领域和具有国际竞争力的产品与发达国家的重叠度进一步提高,全球价值链分工由产业上下游分工的协作关系逐步转变为同一产业链环节的竞争关系,发展中国家制造业向全球价值链中高端的升级将面临来自发达国家企业日益加剧的竞争与发达国家政府的阻碍。
①数据来源网址依次为:https://www.weforum.org;https://databank.worldbank.org/home.aspx;https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx;https://publicadministration.un.org/en/databases;http://unctadstat.unctad.org/EN/BulkDownload.html。
②受全球新冠疫情影响,截至2022年12月,WEF发布的全球竞争力指标仅更新至2019年,故本文测算的国家数字经济发展指数时间段为2010—2019年。
①因篇幅所限,稳健性检验结果分别以附表1~2展示。
②低技术行业LTI包括C3,C4,C5,C6,C7,C8,C16共7个产业类别;中技术行业MTI包括C10,C11两个产业类别;高技术行业HTI包括C9,C12,C13,C14,C15共5个产业类别。
③因篇幅所限,高、中、低技术制造业增加值贸易网络以附图4展示。
基本信息:
DOI:10.19343/j.cnki.11-1302/c.2023.05.001
中图分类号:F49;F414
引用信息:
[1]邓慧慧,徐昊,王强.数字经济与全球制造业增加值贸易网络演进[J].统计研究,2023,40(05):3-19.DOI:10.19343/j.cnki.11-1302/c.2023.05.001.
基金信息:
国家自然科学基金面上项目“高质量发展下区位导向性政策的产业升级效应:实现机制与经验辨识”(72073023); 对外经济贸易大学中央高校基本科研业务费专项资金资助“双循环新格局与高质量发展”(CXTD12-02);对外经济贸易大学杰出青年学者资助项目“中国制造业国内国际双循环新发展格局构建的现实逻辑与实现路径”(20JQ08)
2023-05-25
2023-05-25