{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T05:38:21Z","timestamp":1775367501049,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Defense, Network Science Division, Army Research Office","award":["W911NF2010187"],"award-info":[{"award-number":["W911NF2010187"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb\u2013Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data.<\/jats:p>","DOI":"10.3390\/e23060673","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T11:07:02Z","timestamp":1622113622000},"page":"673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Socio-Economic Impact of the Covid-19 Pandemic in the U.S."],"prefix":"10.3390","volume":"23","author":[{"given":"Jonathan","family":"Barlow","sequence":"first","affiliation":[{"name":"Department of Physics, Graduate School of Arts and Sciences, Boston University, Boston, MA 02215, USA"},{"name":"Laboratory for Interdisciplinary Finance and Economics (LIFE) Research, Metropolitan College, Boston University, Boston, MA 02215, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1183-7941","authenticated-orcid":false,"given":"Irena","family":"Vodenska","sequence":"additional","affiliation":[{"name":"Department of Physics, Graduate School of Arts and Sciences, Boston University, Boston, MA 02215, USA"},{"name":"Laboratory for Interdisciplinary Finance and Economics (LIFE) Research, Metropolitan College, Boston University, Boston, MA 02215, USA"},{"name":"Department of Administrative Sciences, Metropolitan College, Boston University, Boston, MA 02215, USA"},{"name":"Global Development Policy Center, Boston University, Boston, MA 02215, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.2307\/1913386","article-title":"Time to Build and Aggregate Fluctuations","volume":"50","author":"Kydland","year":"1982","journal-title":"Econometrica"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Farhi, E., and Werning, I. (2013). A Theory of Macroprudential Policies in the Presence of Nominal Rigidities, Available online: http:\/\/www.nber.org\/papers\/w19313.","DOI":"10.3386\/w19313"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/0167-2231(77)90002-1","article-title":"Understanding Business Cycles","volume":"5","author":"Lucas","year":"1977","journal-title":"Carnegie-Rochester Ser. 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Rep.","DOI":"10.1038\/srep01219"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Garas, A., Argyrakis, P., Rozenblat, C., Tomassini, M., and Havlin, S. (2010). Worldwide spreading of economic crisis. New J. Phys.","DOI":"10.1088\/1367-2630\/12\/11\/113043"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100803","DOI":"10.1016\/j.jfs.2020.100803","article-title":"From stress testing to systemic stress testing: The importance of macroprudential regulation","volume":"52","author":"Vodenska","year":"2021","journal-title":"J. Financ. Stab."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Battiston, S., Puliga, M., Kaushik, R., Tasca, P., and Caldarelli, G. (2012). DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk. Nature.","DOI":"10.1038\/srep00541"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-82904-y","article-title":"Systemic stress test model for shared portfolio networks","volume":"11","author":"Vodenska","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_12","unstructured":"Page, L., Brin, S., Motwani, R., and Winograd, T. (1999). The PageRank Citation Ranking: Bringing Order to the Web, Stanford InfoLab. Technical Report."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Brinca, P., Duarte, J., and Castro, M. (2020). Measuring Labor Supply and Demand Shocks during COVID-19, Federal Reserve Bank of St. Louis. Working Paper 2020-011D.","DOI":"10.1016\/j.euroecorev.2021.103901"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guerrieri, V., Lorenzoni, G., Straub, L., and Werning, I. (2020). Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages? Working Paper 26918, National Bureau of Economic Research.","DOI":"10.3386\/w26918"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ijsu.2020.04.018","article-title":"The socio-economic implications of the coronavirus pandemic (COVID-19): A review","volume":"78","author":"Nicola","year":"2020","journal-title":"Int. J. Surg."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, S.Y.T., Park, M., and Shin, Y. (2021). Hit Harder, Recover Slower? Unequal Employment Effects of the Covid-19 Shock, National Bureau of Economic Research. Technical Report.","DOI":"10.3386\/w28354"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103436","DOI":"10.1016\/j.jvb.2020.103436","article-title":"Unemployment in the time of COVID-19: A research agenda","volume":"119","author":"Blustein","year":"2020","journal-title":"J. Voc. Behav."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s00148-020-00792-4","article-title":"COVID-19 outbreak, social response, and early economic effects: A global VAR analysis of cross-country interdependencies","volume":"34","author":"Milani","year":"2021","journal-title":"J. Popul. Econ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Farrell, D., Ganong, P., Greig, F., Liebeskind, M., Noel, P., and Vavra, J. (2020). Consumption effects of unemployment insurance during the covid-19 pandemic. Soc. Sci. Electron. Publ.","DOI":"10.2139\/ssrn.3654274"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1257\/pandp.20211108","article-title":"Sectoral effects of social distancing","volume":"111","author":"Barrot","year":"2021","journal-title":"AEA Pap. Proc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1146\/annurev-economics-080218-030212","article-title":"Production networks: A primer","volume":"11","author":"Carvalho","year":"2019","journal-title":"Annu. Rev. Econ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Luo, S., Tsang, K.P., and Yang, Z. (2020). The impact of stay-at-Home orders on U.S. output: A network perspective. Soc. Sci. Electron. Publ.","DOI":"10.2139\/ssrn.3571866"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Makridis, C., and Hartley, J. (2020). The Cost of Covid-19: A Rough Estimate of the 2020 US GDP Impact. Spec. Ed. Policy Brief, Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3570731.","DOI":"10.2139\/ssrn.3570731"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Acemoglu, D., Ozdaglar, A., and Tahbaz-Salehi, A. (2010). Cascades in Networks and Aggregate Volatility. Soc. Sci. Electron. Publ., 1\u201348.","DOI":"10.3386\/w16516"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1257\/jep.28.4.23","article-title":"From Micro to Macro via Production Networks","volume":"28","author":"Carvalho","year":"2014","journal-title":"J. Econ. Perspect."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1086\/261128","article-title":"Real Business Cycles","volume":"91","author":"Plosser","year":"1983","journal-title":"J. Political Econ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ilyasov, R.H. (2014). About the Method of Analysis of Economic Correlations by Differentiation of Spline Models. Mod. Appl. 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