Spatial constraints on economic interactions: a complexity approach to the Japanese inter-firm trade network
File(s)mathematics-12-01244-v2.pdf (6.75 MB)
Published version
Author(s)
Viegas, Eduardo
Levy, Orr
Havlin, Shlomo
Takayasu, Hideki
Takayasu, Misako
Type
Journal Article
Abstract
The trade distance is an important constraining factor underpinning the emergence of social and economic interactions of complex systems. However, agent-based studies supported by the granular analysis of distances are limited. Here, we present a complexity method that places the actual geographical locations of individual firms in Japan at the epicentre of our research. By combining methods derived from network science together with information theory measures, and by using a comprehensive dataset of Japanese inter-firm business transactions, we evaluate the effects of spatial features on the structural patterns of the economy. We find that the normalised probability distributions of the distances between interacting firms obey a power law like decay concomitant with the sizes of firms and regions. Furthermore, small firms would reach large distances to become customers of large firms, while trading between either only small firms or only large firms tends to be at smaller distances. Furthermore, a time evolution analysis suggests a reduction in the overall average trading distances in last 20 years. Lastly, our analysis concerning the trading dynamics among prefectures indicates that the preference to trade with neighbouring prefectures tends to be more pronounced at rural regions as opposed to the larger central conurbations.
Date Issued
2024-04
Date Acceptance
2024-04-16
Citation
Mathematics, 2024, 12 (8)
ISSN
2227-7390
Publisher
MDPI AG
Journal / Book Title
Mathematics
Volume
12
Issue
8
Copyright Statement
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
http://dx.doi.org/10.3390/math12081244
Publication Status
Published
Article Number
1244
Date Publish Online
2024-04-19