Abstract
The problem of inferring the technological structure of production processes and, hence, quantifying technological sophistication, remains largely unsolved; at least in a generalizable way. This reflects in empirical literature that either focuses on outputs instead of transformative processes, or preemptively assumes the nature of technology. Building on recent advances in information theory, we develop a method to quantify technological sophistication. Our approach explicitly addresses the transformative process where inputs interact to generate outputs; providing a more direct inference about the nature of technology. More specifically, we measure the degree to which an industry's inputs interact in a synergistic fashion. Synergies create novel outputs, so we conjecture that synergistic technologies are more sophisticated. We test this conjecture by estimating synergy scores for industries across nearly 150 countries using input-output datasets. We find that these scores predict popular export-based measures of economic complexity that are commonly used as proxies for economic sophistication. The method yields synergistic interaction networks that provide further insights on the structure of industrial processes. For example, they reveal that industries from the tertiary sector tend to be disassortative sector-wise. To the extent of our knowledge, our findings are the first data-driven inference of technological sophistication within production processes (on an industrial scale). Thus, they provide the technological foundations of economic complexity and represent an important step toward its empirical microfoundations.
Turing affiliated authors
