Reconstructing firm-level interactions in the Dutch input–output network from production constraints

Author:

Ialongo Leonardo NiccolòORCID,de Valk Camille,Marchese Emiliano,Jansen Fabian,Zmarrou Hicham,Squartini Tiziano,Garlaschelli Diego

Abstract

AbstractRecent crises have shown that the knowledge of the structure of input–output networks, at the firm level, is crucial when studying economic resilience from the microscopic point of view of firms that try to rewire their connections under supply and demand constraints. Unfortunately, empirical inter-firm network data are protected by confidentiality, hence rarely accessible. The available methods for network reconstruction from partial information treat all pairs of nodes as potentially interacting, thereby overestimating the rewiring capabilities of the system and the implied resilience. Here, we use two big data sets of transactions in the Netherlands to represent a large portion of the Dutch inter-firm network and document its properties. We, then, introduce a generalized maximum-entropy reconstruction method that preserves the production function of each firm in the data, i.e. the input and output flows of each node for each product type. We confirm that the new method becomes increasingly more reliable in reconstructing the empirical network as a finer product resolution is considered and can, therefore, be used as a realistic generative model of inter-firm networks with fine production constraints. Moreover, the likelihood of the model directly enumerates the number of alternative network configurations that leave each firm in its current production state, thereby estimating the reduction in the rewiring capability of the system implied by the observed input–output constraints.

Funder

EC | Horizon 2020 Framework Programme

Artes 4.0 - Advanced Robotics and enabling digital Technologies & Systems

Stichting Econophysics, Leiden, the Netherlands

This work was supported by the Open Access Publishing Fund of the Scuola Normale Superiore

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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