Synergistic small worlds that drive technological sophistication

Area of Science:

• Economics
• Network Science
• Information Theory

Background:

• Economic growth is linked to technological advancement, but production process interactions remain unclear in large systems.
• Understanding input-output relationships is crucial for analyzing industry and economy-wide production technologies.

Purpose of the Study:

• To develop a data-driven method for quantifying input complementarity in production processes using input-output data.
• To link information theory's synergistic information to economic complementarity without assuming production functions.

Main Methods:

• Utilized input-output data to quantify input complementarity.
• Applied the concept of synergistic information from information theory as an economic analog.
• Developed a data-driven approach to identify and quantify input-input interactions and their contribution to output.

Main Results:

• Revealed input-input synergistic interaction networks characterizing industry productive technology.
• Found that sophisticated industries exhibit modular small-world network topologies, with the tertiary sector as a core.
• Observed that well-established core networks and specialized modules correlate with higher economic complexity, output, and lower emissions.

Conclusions:

• The proposed method offers a framework to identify key economic relationships for enhancing economic performance.
• Industry structure, characterized by network topology, is a significant determinant of economic complexity and environmental impact.
• Synergistic information provides a novel lens for understanding economic complementarity and production networks.