在产品层面绘制全球价值链的地图
Lea Karbevska1,2, César A Hidalgo1,3
1Center for Collective Learning, IAST, Toulouse School of Economics, 1 Esplanade de l'Universite, Toulouse, 31080 France.
概括
本研究引入了一种新的机器学习方法,用于创建产品级全球价值链数据. 这种方法从国际贸易模式中推断价值链关系,解决了经济分析中的一个关键差距.
科学领域:
- 经济学 经济学 经济学
- 计算机科学 计算机科学
- 国际贸易 国际贸易 国际贸易
背景情况:
- 现有的价值链数据集汇总了工业部门,缺乏产品特定细节.
- 这限制了对经济中断和供应链漏洞的理解.
- 公开可用的数据往往缺乏详细的产品级价值链信息.
研究的目的:
- 开发一种用于推断产品水平价值链关系的新方法.
- 为分析全球价值链创建一个更细致的数据集.
- 帮助研究人员和政策制定者了解复杂的贸易网络.
主要方法:
- 利用机器学习和贸易理论原则.
- 分析了1200多种产品和250个地区的细粒度国际贸易数据 (出口和进口).
- 开发一个比例分配模型来估计产品级别的贸易流.
主要成果:
- 成功地从贸易模式中推断出产品级价值链信息.
- 创建了一种方法,在产品层面近似估计价值链数据.
- 证明了贸易数据在揭示隐藏的价值链结构方面的实用性.
结论:
- 开发的方法为近似产品级价值链数据提供了有价值的工具.
- 这种方法增强了对全球价值链,贸易和可持续发展的分析.
- 解决了对精细数据的关键需求,以应对经济复杂性.
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