论文标题
关于贸易网络复杂性与全球国内生产总值之间的经验关联
On the Empirical Association between Trade Network Complexity and Global Gross Domestic Product
论文作者
论文摘要
近几十年来，国家之间的贸易构成了全球国内生产总值（GDP）的重要组成部分，官方估计表明，它可能占全球总产量的四分之一。尽管贸易量和GDP增长之间的宏观经济数据已经存在，但在单个颗粒部门（例如车辆或矿物质）水平上是否存在相关的工作，贸易网络的复杂性与全球GDP之间存在关联。在本文中，我们通过使用经济复杂性项目地图集的公开数据来探讨这个问题，以严格构建多个部门的国家之间的全球贸易网络，并研究这些网络计算的网络理论措施之间的相关性（例如平均聚类系数和密度和密度）和全球GDP。我们发现，几乎每个领域的贸易网络的复杂性与全球GDP之间确实存在显着关联，并且该网络指标也与商业周期现象（例如2007 - 2008年的大衰退）相关。我们的结果表明，仅贸易量无法解释全球GDP的增长，并且网络科学可能被证明是研究宏观经济现象（例如贸易）复杂性的宝贵经验途径。
In recent decades, trade between nations has constituted an important component of global Gross Domestic Product (GDP), with official estimates showing that it likely accounted for a quarter of total global production. While evidence of association already exists in macro-economic data between trade volume and GDP growth, there is considerably less work on whether, at the level of individual granular sectors (such as vehicles or minerals), associations exist between the complexity of trading networks and global GDP. In this paper, we explore this question by using publicly available data from the Atlas of Economic Complexity project to rigorously construct global trade networks between nations across multiple sectors, and studying the correlation between network-theoretic measures computed on these networks (such as average clustering coefficient and density) and global GDP. We find that there is indeed significant association between trade networks' complexity and global GDP across almost every sector, and that network metrics also correlate with business cycle phenomena such as the Great Recession of 2007-2008. Our results show that trade volume alone cannot explain global GDP growth, and that network science may prove to be a valuable empirical avenue for studying complexity in macro-economic phenomena such as trade.
