Abstract
We use social network analysis to model the trade networks that connect each of the United States to the rest of the world in an effort to capture trade shocks and supply chain disruptions resulting from the COVID-19 pandemic and, more specifically, to capture how such disruptions propagate through those networks. The results show that disruptions will noticeably move along industry connections, spreading in specific patterns. Our results are also consistent with past work that shows that non-pharmaceutical policy interventions have had limited impact on trade flows.
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Notes
- 1.
It is common in network analysis literature to collapse bipartite graphs due to failed convergence in bipartite inferential models and for additional model features not available in bipartite models. Past work has shown that collapsing into a monopartite project still preserves important information about the network [15].
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Schoeneman, J., Brienen, M. (2022). The COVID-19 Pandemic and Export Disruptions in the United States. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_59
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