统计研究

• 论文 • 上一篇    下一篇

中国加工贸易的价值攀升:嵌入NVC会优于GVC吗

袁凯华 彭水军   

  • 出版日期:2017-08-15 发布日期:2017-08-25

The Rising Value of China's Processing Trade: Is NVC Embedded Better than GVC

Yuan Kaihua & Peng Shuijun   

  • Online:2017-08-15 Published:2017-08-25

摘要:

受制于全球价值链(GVC)分工体系下的低值困境,嵌入国内价值链(NVC)被视为是助推中国加工贸易价值攀升的重要途径。本文基于OECD-ICIO数据库,采用Wang 等(2017)的增加值核算模型,测算和比较分析了1995-2011年中国代工行业嵌入GVC与NVC下的国内增加值率,并通过SDA方法考察了GVC和NVC下国内增加值率变动的驱动因素。结果发现,观察期内中国代工行业呈现出明显的“出口转内销”的发展态势,但嵌入NVC对国内增加值率的拉动效应明显弱于GVC,加工贸易快速的价值攀升主要来自GVC的支撑;NVC下的价值攀升相对滞缓主要缘于“国内配套政策缺失→内销不畅→更多国外要素嵌入”的困境。因此,未来的转型升级应当强化国内产业配套措施,扭转粗放经营模式,才能真正借助NVC促进中国加工贸易的价值攀升。

关键词: 加工贸易, 国内增加值率, 价值攀升, 结构分解分析

Abstract:

The embedded national value chain (NVC) is regarded as an important way to boost the value of China's processing trade, subject to the low value predicament under the division of labor system of the global value chain (GVC). Based on the OECD-ICIO database, this paper uses the value-added accounting model of Wang et al. (2017) to measure and compare the domestic value-added rate of China's OEM (Original Equipment Manufacturer) embedded in GVC and NVC from 1995 to 2011, and analyzes the driving factors of the domestic value-added rate under the GVC and NVC. The results show that in the observation period, the OEM in China showed an obvious development trend of “export to domestic sales”, but the pulling effect of embedded NVC on domestic value added rate is weaker than that of GVC. The rapid value escalation of processing trade is mainly backstopped by GVC; the relative lag in value escalation by NVC is mainly due to the plight of "lack of domestic supporting policies→sluggish domestic sales→more foreign elements embedded". Therefore, the future transformation and upgrading should be strengthened with the domestic industrial supporting measures to reverse the extensive business models, in order to practically use NVC to promote the value escalation of the processing trade in China.

Key words: Processing trade, Domestic Value Added Ratio, Value Chain Climbing, Structural Decomposition Analysis