Mapping the disaggregated economy in real-time: Using granular payment network data to complement national accounts

TL;DR

This paper demonstrates how granular, real-time payment network data can effectively complement traditional national accounts by providing timely, detailed insights into economic activity at the industry level.

Contribution

It introduces a novel approach using anonymized payment data to enhance real-time economic measurement and compares it systematically with established indicators.

Findings

Strong correlation with GDP

Qualitative consistency with input-output tables

Payment network structures align with economic network literature

Abstract

In an era of rapid change, timely and disaggregated economic insights are crucial for effective policymaking. This study explores the potential of real-time payment data to complement traditional economic measurement. Using anonmysed UK business payments from 2015-2023, we analysed inter-industry financial flows at a granular 5-digit SIC level and compared them systematically with established economic indicators such as GDP and input-output tables (IOTs). Our findings show strong correlations with GDP and qualitative consistency with official IOTs, highlighting the value of the novel high-frequency data for real-time economic monitoring. We also benchmarked network statistics at the 5-digit level, showing how industry-specific payment structures align with stylised facts from the empirical economic network literature. While outlining methodological and interpretative challenges, we discuss the integration of such bottom-up data into national accounts. This work contributes to ongoing efforts to advance economic measurement and offers additional tools for tracking economic dynamics in real time.