摘要翻译：
每个国家都优先考虑所有区域的包容性经济增长和发展。然而，我们观察到，经济活动在空间上是聚集的，这导致了不同地区之间的人均收入差距。Hidalgo和Hausmann提出了一种基于复杂性的方法[PNAS 106,10570-10575(2009)]来解释各国人均收入的巨大差距。虽然利用国际出口数据对各国经济复杂性进行了广泛的研究，但对区域一级经济复杂性的研究相对较少。本文以日本100多万家都道府县的基本信息为基础，研究了各都道府县工业部门的复杂性。我们将数据汇总为县和工业部门的两部分网络。本文将二部网络分解为县-县网络和部门-部门网络，揭示了它们之间的关系。各都道府县之间和各部门之间的相似性是用一个指标衡量的。从这些相似矩阵中，我们利用最小生成树技术对县和部门进行聚类，从二部网络结构中计算出的经济复杂度指数与人均生产总值和人均人均收入等宏观经济指标具有较高的相关性。我们认为，这一指数反映了目前的经济表现和潜在的潜力，为未来的增长都道府县。
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英文标题：
《Economic complexity of prefectures in Japan》
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作者：
Abhijit Chakraborty, Hiroyasu Inoue, Yoshi Fujiwara
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  Every nation prioritizes the inclusive economic growth and development of all regions. However, we observe that economic activities are clustered in space, which results in a disparity in per-capita income among different regions. A complexity-based method was proposed by Hidalgo and Hausmann [PNAS 106, 10570-10575 (2009)] to explain the large gaps in per-capita income across countries. Although there have been extensive studies on countries' economic complexity using international export data, studies on economic complexity at the regional level are relatively less studied. Here, we study the industrial sector complexity of prefectures in Japan based on the basic information of more than one million firms. We aggregate the data as a bipartite network of prefectures and industrial sectors. We decompose the bipartite network as a prefecture-prefecture network and sector-sector network, which reveals the relationships among them. Similarities among the prefectures and among the sectors are measured using a metric. From these similarity matrices, we cluster the prefectures and sectors using the minimal spanning tree technique.The computed economic complexity index from the structure of the bipartite network shows a high correlation with macroeconomic indicators, such as per-capita gross prefectural product and prefectural income per person. We argue that this index reflects the present economic performance and hidden potential of the prefectures for future growth.
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PDF链接：
https://arxiv.org/pdf/2002.05785