Year: 2025
Type: article
Abstract: It is a well-known fact that economic growth goes hand in hand with improvements in technological sophistication. While critical to such sophistication, the nature and underlying structure of the input interactions taking place inside production processes remain opaque, at least in the study of large systems such as industries and entire economies. We develop a method to quantify the degree of input complementarity in production processes form input-output data. We propose that the information-theoretic concept of synergistic information is analog to economic complementarity and exploit this link to create a data-driven approach that does not require the ex ante assumption of production functions. In contrast to alternative empirical approaches, our method is able identify input-input interactions and to quantify their contribution to output, revealing an input-input synergistic interaction network that characterizes an industry's productive technology. We find that more sophisticated industries tend to exhibit highly modular small-world topologies; with the tertiary sector as its central connective core. Overall, countries and industries that have a well-established connective core and specialized modules exhibit higher economic complexity, higher output, and lower emissions. The proposed method provides a framework to identify key relationships in the economy that can enhance economic performance.
Source: PNAS Nexus
Authors: Hardik Rajpal, Omar Guerrero
Institutions: The Alan Turing Institute, Imperial College London
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $4,202
Awards: G17555345, Statistical physics of cognition, Agent Computing and AI to Achieve the 2030 Agenda: New Methods to Infer Policy Priorities from Open Fiscal Data and Sustainable Development Indicators, G3004361074, G3495160753, G5147728456, G6098645062, G6308133171, The Alan Turing Institute 22/23 - Core and Additional Funding
OpenAlex ID: W4409504182