Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks within a wider space of networks that are different in nature, but similar in local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional local connectivity structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and give us precise terms for the local structural features that may be key to understanding their routine function, failure, and growth.
This seminar is the first one in the seminar series organized within he PRO3 project "Network Analysis of Economic and Financial Resilience", a joint project between the IMT School of Advanced Studies (Lucca), the Sant'Anna School of Advanced Studies (Pisa) and the Scuola Normale Superiore (Pisa). Recent global crises have highlighted the systemic dimension of risk in financial, economic and health systems, largely due to the interconnections among the underlying elements. Network Theory offers quantitative tools to reconstruct those interconnections from (big) data, analyse the properties of the resulting complex structures, and study their impact on systemic risk. The "Network Analysis of Economic and Financial Resilience" project aims on the one hand at developing novel quantitative tools for the data-driven analysis of systemic risk on complex networks, and on the other hand at using those methods for the study of financial stability and economic resilience. The main domains of application will be the structural impact of COVID-19 on networks of international and inter-firm trade and the propagation of shocks in financial and inter-bank networks. The project will require a combination of design of theoretical models of complex networks within the framework of statistical physics, empirical data analysis with econometric and machine learning methods, and derivation of implications for policymaking and financial risk management.
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