A New Interpretation of the Economic Complexity Index

P. Mealy, J. Farmer, A. Teytelboym
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引用次数: 25

Abstract

Analysis of properties of the global trade network has generated new insights into the patterns of economic development across countries. The Economic Complexity Index (ECI), in particular, has been successful at explaining cross-country differences in GDP/capita and economic growth. The ECI aims to infer information about countries’ productive capabilities by making relative comparisons across countries' export baskets. However, there has been some confusion about how the ECI works: previous studies compared the ECI to the number of exports that a country has revealed comparative advantage in (`diversity') and to eigenvector centrality. We show that the ECI is, in fact, equivalent to a spectral clustering algorithm, which partitions a similarity graph into two parts. When applied to country-export data, the ECI represents a ranking of countries that places countries with similar exports close together in the ordering. More generally, the ECI is a dimension reduction tool, which gives the optimal one-dimensional ordering that minimizes the distance between nodes in a similarity graph. We discuss this new interpretation of the ECI with reference to the economic development literature. Finally, we illustrate stark differences between the ECI and diversity with two empirical examples based on regional data.
经济复杂性指数的新解读
对全球贸易网络特性的分析使我们对各国经济发展模式有了新的认识。经济复杂性指数(ECI)尤其成功地解释了国内生产总值/人均和经济增长的跨国差异。ECI旨在通过对各国出口篮子进行相对比较,推断有关各国生产能力的信息。然而,对于ECI的工作方式存在一些困惑:以前的研究将ECI与一个国家在(“多样性”)方面显示出比较优势的出口数量和特征向量中心性进行了比较。我们表明,ECI实际上相当于一个谱聚类算法,它将相似图分成两部分。当应用于国家出口数据时,ECI代表了一个国家的排名,将出口相似的国家按顺序排列在一起。更一般地说,ECI是一种降维工具,它给出了最小化相似图中节点之间距离的最佳一维排序。我们将参考经济发展文献来讨论ECI的这种新解释。最后,我们用两个基于区域数据的实证例子说明了ECI和多样性之间的明显差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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