Supply chain disruptions and labor shortages: COVID in perspective

Stefan Pitschner

Economics Letters, 2022, vol. 221, issue C

Abstract: We use a neural network language model and dependency parsing to extract textual information about shortages from US corporate filings. This allows us to study the COVID pandemic in the broader context of other macroeconomic environments over the past 25 years. Shortages are much more prevalent during the pandemic than they are during other times, especially in the goods sector and for intermediate inputs. Firms also report significantly more increases in costs. The largest number of shortages occurs at the very end of our sample, in the first half of 2022. Our data are consistent with several known economic facts that are unrelated to the pandemic.

Keywords: COVID; Supply chain disruptions; Supply shortages; Labor shortages; Natural language processing; Dependency parsing (search for similar items in EconPapers)
JEL-codes: E00 E23 E30 E65 F14 F62 G01 J20 M20 M21 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:221:y:2022:i:c:s016517652200369x

DOI: 10.1016/j.econlet.2022.110895

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