DebtStreamness: An Ecological Approach to Credit Flows in Inter-Firm Networks

2025-05-02

AI Summary

This study addresses the structural characteristics of credit flow and systemic risk assessment in corporate credit networks. Methodologically, it constructs a directed weighted network using central bank enterprise credit survey data, integrating maximum-entropy reconstruction, graph-theoretic centrality measures, and motif analysis to uncover the nonlinear influence of implicit financial intermediation and local cyclic structures on credit hierarchy. The paper introduces DebtStreamness—a novel metric inspired by ecological trophic levels—treating credit as the economy’s “primary energy source” to quantify firms’ hierarchical positions and propagation distances within credit chains. Empirically applied to Uruguay, the analysis reveals short, strongly stratified credit chains; DebtStreamness identifies financially meaningful structural patterns invisible to input-output data, demonstrating robustness across specifications and strong policy interpretability for macroprudential monitoring and network-based risk management.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Causality
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Understanding how credit flows through inter-firm networks is critical for assessing financial stability and systemic risk. In this study, we introduce DebtStreamness, a novel metric inspired by trophic levels in ecological food webs, to quantify the position of firms within credit chains. By viewing credit as the ``primary energy source'' of the economy, we measure how far credit travels through inter-firm relationships before reaching its final borrowers. Applying this framework to Uruguay's inter-firm credit network, using survey data from the Central Bank, we find that credit chains are generally short, with a tiered structure in which some firms act as intermediaries, lending to others further along the chain. We also find that local network motifs such as loops can substantially increase a firm's DebtStreamness, even when its direct borrowing from banks remains the same. Comparing our results with standard economic classifications based on input-output linkages, we find that DebtStreamness captures distinct financial structures not visible through production data. We further validate our approach using two maximum-entropy network reconstruction methods, demonstrating the robustness of DebtStreamness in capturing systemic credit structures. These results suggest that DebtStreamness offers a complementary ecological perspective on systemic credit risk and highlights the role of hidden financial intermediation in firm networks.

Problem

• Quantify firm positions in credit chains using ecological metrics
• Analyze credit flow distance and structure in inter-firm networks
• Identify hidden financial intermediation via network motifs

Innovation

• DebtStreamness metric inspired by ecological food webs
• Measures credit flow distance in inter-firm networks
• Validated using maximum-entropy network reconstruction methods