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Network Structure in UK Payment Flows: Evidence on Economic Interdependencies and Implications for Real-Time Measurement

Aditya Humnabadkar

Abstract

Network analysis of inter-industry payment flows reveals structural economic relationships invisible to traditional bilateral measurement approaches, with significant implications for real-time economic monitoring. Analysing 532,346 UK payment records (2017--2024) across 89 industry sectors, we demonstrate that graph-theoretic features which include centrality measures and clustering coefficients improve payment flow forecasting by 8.8 percentage points beyond traditional time-series methods. Critically, network features prove most valuable during economic disruptions: during the COVID-19 pandemic, when traditional forecasting accuracy collapsed (R2} falling from 0.38 to 0.19), network-enhanced models maintained substantially better performance, with network contributions reaching +13.8 percentage points. The analysis identifies Financial Services, Wholesale Trade, and Professional Services as structurally central industries whose network positions indicate systemic importance beyond their transaction volumes. Network density increased 12.5\% over the sample period, with visible disruption during 2020 followed by recovery exceeding pre-pandemic integration levels. These findings suggest payment network monitoring could enhance official statistics production by providing leading indicators of structural economic change and improving nowcasting accuracy during periods when traditional temporal patterns prove unreliable.

Network Structure in UK Payment Flows: Evidence on Economic Interdependencies and Implications for Real-Time Measurement

Abstract

Network analysis of inter-industry payment flows reveals structural economic relationships invisible to traditional bilateral measurement approaches, with significant implications for real-time economic monitoring. Analysing 532,346 UK payment records (2017--2024) across 89 industry sectors, we demonstrate that graph-theoretic features which include centrality measures and clustering coefficients improve payment flow forecasting by 8.8 percentage points beyond traditional time-series methods. Critically, network features prove most valuable during economic disruptions: during the COVID-19 pandemic, when traditional forecasting accuracy collapsed (R2} falling from 0.38 to 0.19), network-enhanced models maintained substantially better performance, with network contributions reaching +13.8 percentage points. The analysis identifies Financial Services, Wholesale Trade, and Professional Services as structurally central industries whose network positions indicate systemic importance beyond their transaction volumes. Network density increased 12.5\% over the sample period, with visible disruption during 2020 followed by recovery exceeding pre-pandemic integration levels. These findings suggest payment network monitoring could enhance official statistics production by providing leading indicators of structural economic change and improving nowcasting accuracy during periods when traditional temporal patterns prove unreliable.

Paper Structure

This paper contains 11 sections, 10 equations, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Data and Network Construction
  4. Graph-Theoretic Feature Engineering
  5. Prediction Framework
  6. Results
  7. Network Structure of UK Payment Flows
  8. Forecasting Performance
  9. Network Value During Economic Disruption
  10. Network Evolution
  11. Conclusion

Figures (1)

  • Figure 1: Network Topology Evolution of UK Inter-Industry Payment Flows (2017-2024). Each panel displays the payment network structure for a given year, with nodes representing industry sectors coloured by economic category: Financial & Business (red), Manufacturing (blue), Trade & Distribution (green), Public & Social (orange), Primary Industries (purple), and Other Services (grey). Network density increased systematically from 0.689 (2017) to 0.775 (2024), indicating growing economic integration. The COVID-19 disruption is visible in 2020, where density temporarily fell to 0.693 before recovery exceeded pre-pandemic levels. Average path length decreased from 1.31 to 1.22, suggesting increasingly direct inter-industry relationships.