Disequilibrium Propagation of Quantity Constraints: Application to the COVID Lockdowns

24 Pages Posted: 9 Jul 2020 Last revised: 11 Oct 2021

See all articles by Antoine Mandel

Antoine Mandel

Université Paris I Panthéon-Sorbonne; Climate Finance Alpha

Vipin P. Veetil

Indian Institute of Managment Kozhikode

Date Written: October 2021

Abstract

This paper develops a network economy model to study the propagation of the COVID lockdown shock. Firms are related to each other through buyer-seller relations in the market for intermediate inputs. Firms choose production levels and input combinations using prices that emerge from local interactions. Nothing forbids trade at out-of-equilibrium prices. In such a setting, disequilibrium spills over from one market to another due to their interconnections. These disequilibrium dynamics are capable of generating unemployment when labor released by contracting firms are not frictionlessly absorbed by expanding firms. We calibrate the model to the US economy using a data set with more than 200,000 buyer-seller relations between about 70,000 firms. Computational experiments on the calibrated economy suggest that the COVID lockdown will cost around 15-30% of annual GDP. The rate at which unemployed workers find new jobs is the primary determinant of the cost of the lockdown.

Keywords: Economic Network, Disequilibrium, Unemployment, COVID Lockdown, Agent-based Model

JEL Classification: J60, D50, D57

Suggested Citation

Mandel, Antoine and Veetil, Vipin P., Disequilibrium Propagation of Quantity Constraints: Application to the COVID Lockdowns (October 2021). Available at SSRN: https://ssrn.com/abstract=3631014 or http://dx.doi.org/10.2139/ssrn.3631014

Antoine Mandel

Université Paris I Panthéon-Sorbonne ( email )

17, rue de la Sorbonne
Paris, IL 75005
France

Climate Finance Alpha ( email )

20 rue de Vintimille
PARIS 9E ARRONDISSEMENT, 75009

Vipin P. Veetil (Contact Author)

Indian Institute of Managment Kozhikode ( email )

Kerala
Kunnamangalam
Kozhikode, KS Kerala 673570
India

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