Abstract

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 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.

Highlights

  • It has become established knowledge within complexity economics (Arthur, 2021) that network structure affects economic dynamics over the short, medium, and long-term

  • The spectral bipartivity of the network of trade relationships in Zeeland is substantially larger than random expectation; statistical comparison yields a onetailed Kolmogorov-Smirnov statistic of 1.0 (N1 = 1, N2 = 1000, p < 0.001)

  • This paper has advanced the hypothesis that company-level production networks are so-called “functional” networks, with a distinctive local connectivity structure first identified in network biology (Kovács et al, 2019; Kitsak, 2020)

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Summary

Introduction

It has become established knowledge within complexity economics (Arthur, 2021) that network structure affects economic dynamics over the short-, medium-, and long-term. Trade linkages have been used to explain aggregate fluctuations in business activity that play out over months or years (Acemoglu et al, 2012; Carvalho and Tahbaz-Salehi, 2018). Structural changes happen over decades and we know that national growth trajectories are strongly affected by the network structure of economic activity (Hausmann and Hidalgo, 2013; McNerney et al, 2018). The mechanisms underlying dynamics on and of production networks are thought to operate at the level of trade relationships among individual companies (see Hazama and Uesugi, 2017; Carvalho and Tahbaz-Salehi, 2018; Inoue and Todo, 2019). Trade relationships are more often considered either in detailed, local case studies or as aggregated trade linkages among sectors, industries, and countries based in officiallyprepared macroeconomic statistics (Uzzi, 1997; Coenen et al, 2010; Acemoglu et al, 2012; Miller and Temurshoev, 2017; McNerney et al, 2018)

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