Economic complexity at subnational level: A consistency analysis

2026-06-25

AI Summary

Existing measures of subnational economic complexity yield inconsistent results due to variations in geographic scale and methodological approaches. This study proposes an exogenous, scalable computational framework that integrates network analysis with multiscale geographic data to construct a unified index of subnational economic complexity. The approach substantially improves cross-regional consistency in estimation outcomes and demonstrates strong correlations with conventional economic indicators such as per capita GDP and employment levels. By harmonizing measurement across heterogeneous administrative units, the framework enhances both the explanatory power and practical applicability of economic complexity metrics at the regional level, offering a more robust foundation for comparative economic analysis and policy design.

Abstract

Several network-based measures have been proposed to assess the economic complexity of countries. These measures have provided important insights into national economic development, and they are now widely applied at the subnational level as well. Here, we show that such applications lead to inconsistent results, in the sense that the estimated complexity of the same product appears to depend on methodological details such as the geographical scale of analysis. Building on these findings, we propose a measure of territorial economic complexity based on an exogenous and extensive computation. We show that these methodological choices yield estimates that are more consistent and more strongly aligned with standard economic indicators, such as GDP per capita and employment.

Problem

• economic complexity
• subnational level
• consistency
• geographical scale
• network-based measures

Innovation

• economic complexity
• subnational analysis
• consistency
• network-based measures
• territorial economic complexity