A stylized map of China and Japan connected by a glowing network, symbolizing economic connections.

Decoding Economic Giants: How China and Japan's Industries Stack Up

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Samir D’Costa in Business & Economy June 2025 • 5 min read.

"A network perspective reveals surprising similarities and key differences in the sectoral structures of China and Japan's economies."


China and Japan stand as titans in the global economic arena, yet their internal engines operate with distinct nuances. While China boasts a rapid growth rate and expansive economic scale, Japan showcases an advanced industrial structure coupled with high labor productivity. Understanding the sectoral structures of these nations – the intricate web of industries and their interactions – is crucial for grasping their economic behavior and future trajectories.

Economic comparisons between China and Japan have long been a subject of fascination for economists. To understand the difference, a deep dive into their respective industries is needed. From 1995 to 2018, data from the annual input-output tables (IOTs) of both nations to construct weighted and directed input-output networks (IONs) was collected. This approach facilitates deeper network analyses, revealing the unique characteristics of each economy.

Network analysis offers a powerful lens to dissect these complex systems. By mapping the flows of goods and services between sectors, researchers can uncover key interdependencies, assess the influence of individual industries, and identify clusters of related activities. This approach moves beyond traditional economic indicators, providing a more granular and dynamic understanding of economic structure.

What Can Sector Analysis Tell Us About China and Japan?

A stylized map of China and Japan connected by a glowing network, symbolizing economic connections.

To dissect the economic anatomy of China and Japan, an examination of node strength distribution, a measure reflecting the magnitude of interactions within each sector is needed. The economic structure comparisons between China and Japan have captivated development economists. To delve deeper into their sectoral differences from 1995 to 2018, the annual input-output tables (IOTs) of both nations to construct weighted and directed input-output networks (IONs) was used. This approach facilitates deeper network analyses, revealing the unique characteristics of each economy. From these models, the results underscore variations in inter-sector economic interactions. Weighted, directed assortativity coefficients encapsulated the homophily among connecting sectors' features.

Community detection reveals clustering tendencies among the sectors. The analysis pinpointed manufacturing as China's central sector, while Japan favored services. Yet, at a finer level of the specific sectors, both nations exhibited varied structural evolutions. Sectoral communities in both China and Japan demonstrated commendable stability over the examined duration.

  • Manufacturing vs. Services: China's economic strength is rooted in manufacturing, while Japan's economy is focused in the service sector.
  • Evolving Structures: The specific sectors within each nation have experienced varied structural evolutions.
  • Community Stability: China and Japan have demonstrated stability of sectoral communities over time.
Assortativity measures the homophily of a network, that is, the tendency of nodes to connect with similar partners in a network. In the context of IONs, assortativity assesses the preference of one sector with certain sector-level feature channels products to another sector with another sector-level feature. The features of the supplying sector and the receiving sector do not have to be the same feature. Positive assortativity coefficients signify assortative-mixing, suggesting that nodes with higher strength tend to connect with similarly strong nodes. In contrast, negative values indicate disassortative-mixing, where high-strength nodes connect with weaker ones. For instance, a positive out-in assortativity coefficient means sectors with significant out-strength tend to channel their products to sectors with high in-strength. Conversely, a negative out-out assortativity coefficient suggests sectors with large out-strength are more inclined to direct their products to sectors with low out-strength.

What Does it All Mean?

This comparative study of China and Japan's sectoral structures has applied several network analysis techniques, including some that were recently developed. The study began with an examination of node strength distributions and illustrated sectoral connections. More nuanced understanding of the homophily of the supplying sectors and receiving sectors has been employed by weighted, directed assortativity coefficients, considering uncertainties through the jackknife method. Without uncertainty quantifications, some conclusions in existing works could be misleading.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

Everything You Need To Know

1

In comparing the economies of China and Japan, what are some key high-level distinctions between them?

China is characterized by its rapid growth rate and expansive economic scale. Japan, on the other hand, showcases an advanced industrial structure and high labor productivity. These differences highlight the distinct operational nuances within each nation's economic engine, further emphasizing the importance of understanding their sectoral structures.

2

How does network analysis enhance our understanding of the economic structures of China and Japan, beyond traditional economic indicators?

Network analysis allows for a deeper understanding of China and Japan by mapping the flows of goods and services between sectors, uncovering key interdependencies, and assessing the influence of individual industries. Unlike traditional economic indicators, this approach provides a more granular and dynamic view of economic structure. This method uses input-output networks (IONs) constructed from annual input-output tables (IOTs).

3

What do assortativity coefficients reveal about the relationships between sectors in China and Japan's economies?

Assortativity coefficients measure the homophily within a network, indicating the tendency of sectors to connect with similar partners. Positive assortativity coefficients suggest that sectors with high strength tend to connect with similarly strong sectors, while negative values indicate connections between high-strength and weaker sectors. Out-in assortativity and out-out assortativity further specify how sectors channel products based on their respective strengths. Analyzing these coefficients helps reveal the characteristics of inter-sector relationships.

4

What are the most significant sectors in China and Japan, and how has their stability been over time?

China's economic strength is rooted in manufacturing, while Japan's economy is focused in the service sector. Despite these differences, sectoral communities in both China and Japan have demonstrated commendable stability over the period examined. The specific sectors within each nation have experienced varied structural evolutions. Community detection within network analysis illuminates these sector-specific trends.

5

What are weighted, directed assortativity coefficients and how do they improve upon existing methods for understanding economic networks?

Weighted, directed assortativity coefficients provide a nuanced understanding of the homophily between supplying and receiving sectors in economic networks. Unlike simpler measures, these coefficients consider the magnitude and direction of interactions, as well as quantifying uncertainties through methods like the jackknife method. By accounting for these uncertainties, the coefficients ensure more reliable conclusions about economic structures, addressing potential misleading results from analyses that overlook such quantifications.

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