日本都道府县的经济复杂度
Economic complexity of prefectures in Japan
摘要：
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，我们注意到，经济活动在空间上是聚集的，这导致不同地区之间的人均收入存在差距。Hidalgo和Hausmann提出了一种基于复杂性的方法[PNAS 106，10570-10575（2009）]来解释各国之间人均收入的巨大差距。虽然利用国际出口数据对各国的经济复杂度进行了广泛的研究，但对区域一级的经济复杂度的研究相对较少。在此，我们以100多万家企业的基本信息为基础，对日本都道府县的产业部门复杂度进行研究。我们将数据汇总为一个由都道府县和工业部门组成的双向网络。我们将二分网络分解为县-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度量标准来衡量的。根据这些相似性矩阵，利用最小生成树技术对各地区和部门进行聚类，根据二分网络结构计算出的经济复杂度指数与各地区人均生产总值和人均收入等宏观经济指标相关性很高。我们认为，这一指数反映了目前的经济表现和隐藏的潜力，为未来的增长县。
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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