Using network science to quantify economic disruptions in regional input-output networks

TL;DR

This paper introduces a network science approach to analyze regional input-output networks, comparing traditional and new measures to assess economic disruptions and the impact of spatial aggregation.

Contribution

It develops a network-based framework for regional IO analysis, demonstrating the effectiveness of betweenness and eigenvector centralities in quantifying economic disruptions.

Findings

Betweenness centrality indicates flow disruptions effectively.

Eigenvector centrality correlates with traditional multipliers.

Spatial aggregation affects network measure outcomes.

Abstract

Input Output (IO) tables provide a standardised way of looking at monetary flows between all industries in an economy. IO tables can be thought of as networks - with the nodes being different industries and the edges being the flows between them. We develop a network-based analysis to consider a multi-regional IO network at regional and subregional level within a country. We calculate both traditional matrix-based IO measures (e.g. 'multipliers') and new network theory-based measures at this higher spatial resolution. We contrast these methods with the results of a disruption model applied to the same IO data in order to demonstrate that betweenness centrality gives a good indication of flow on economic disruption, while eigenvector-type centrality measures give results comparable to traditional IO multipliers.We also show the effects of treating IO networks at different levels of spatial aggregation.