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

Abstract Recent 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 resilien... Mehr ...

Verfasser: Ialongo, Leonardo Niccolò
de Valk, Camille
Marchese, Emiliano
Jansen, Fabian
Zmarrou, Hicham
Squartini, Tiziano
Garlaschelli, Diego
Dokumenttyp: Artikel
Erscheinungsdatum: 2022
Reihe/Periodikum: Scientific Reports ; volume 12, issue 1 ; ISSN 2045-2322
Verlag/Hrsg.: Springer Science and Business Media LLC
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29044324
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : http://dx.doi.org/10.1038/s41598-022-13996-3

Abstract Recent 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.