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Aggregate fluctuations in adaptive production networks
To counteract the adverse effects of shocks, such as the global pandemic, on the economy, governments have discussed policies to improve the resilience of supply chains by reducing dependence on foreign suppliers. In this paper, we develop and quantify an adaptive production network model to study n...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499558/ https://www.ncbi.nlm.nih.gov/pubmed/36095207 http://dx.doi.org/10.1073/pnas.2203730119 |
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author | König, Michael D. Levchenko, Andrei Rogers, Tim Zilibotti, Fabrizio |
author_facet | König, Michael D. Levchenko, Andrei Rogers, Tim Zilibotti, Fabrizio |
author_sort | König, Michael D. |
collection | PubMed |
description | To counteract the adverse effects of shocks, such as the global pandemic, on the economy, governments have discussed policies to improve the resilience of supply chains by reducing dependence on foreign suppliers. In this paper, we develop and quantify an adaptive production network model to study network resilience and the consequences of reshoring of supply chains. In our model, firms exit due to exogenous shocks or the propagation of shocks through the network, while firms can replace suppliers they have lost due to exit subject to switching costs and search frictions. Applying our model to a large international firm-level production network dataset, we find that restricting buyer–supplier links via reshoring policies reduces output and increases volatility and that volatility can be amplified through network adaptivity. |
format | Online Article Text |
id | pubmed-9499558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-94995582023-03-12 Aggregate fluctuations in adaptive production networks König, Michael D. Levchenko, Andrei Rogers, Tim Zilibotti, Fabrizio Proc Natl Acad Sci U S A Social Sciences To counteract the adverse effects of shocks, such as the global pandemic, on the economy, governments have discussed policies to improve the resilience of supply chains by reducing dependence on foreign suppliers. In this paper, we develop and quantify an adaptive production network model to study network resilience and the consequences of reshoring of supply chains. In our model, firms exit due to exogenous shocks or the propagation of shocks through the network, while firms can replace suppliers they have lost due to exit subject to switching costs and search frictions. Applying our model to a large international firm-level production network dataset, we find that restricting buyer–supplier links via reshoring policies reduces output and increases volatility and that volatility can be amplified through network adaptivity. National Academy of Sciences 2022-09-12 2022-09-20 /pmc/articles/PMC9499558/ /pubmed/36095207 http://dx.doi.org/10.1073/pnas.2203730119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Social Sciences König, Michael D. Levchenko, Andrei Rogers, Tim Zilibotti, Fabrizio Aggregate fluctuations in adaptive production networks |
title | Aggregate fluctuations in adaptive production networks |
title_full | Aggregate fluctuations in adaptive production networks |
title_fullStr | Aggregate fluctuations in adaptive production networks |
title_full_unstemmed | Aggregate fluctuations in adaptive production networks |
title_short | Aggregate fluctuations in adaptive production networks |
title_sort | aggregate fluctuations in adaptive production networks |
topic | Social Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499558/ https://www.ncbi.nlm.nih.gov/pubmed/36095207 http://dx.doi.org/10.1073/pnas.2203730119 |
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