The Lockdown Impact on Unemployment for Heterogeneous Workers

Malak Kandoussi () and Francois Langot
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Malak Kandoussi: University of Evry

No 13439, IZA Discussion Papers from IZA Network @ LISER

Abstract: We develop a multi-sectoral matching model to predict the impact of the lockdown on the US unemployment, considering the heterogeneity of workers to account for the contrasted impacts across various types of jobs. We show that separations and business closures that hit the workers with the first level of education explains the abruptness of the unemployment rise. The existence of significant congestion externalities in the hiring process suggests that a comeback to the pre-crisis unemployment level could be reached in 2024 in a scenario with a double wave. In the same scenario, a calibration on French data leads to more pessimistic forecasts with a comeback to the pre-crisis unemployment level expected until 2027.

Keywords: COVID-19; unemployment dynamics; search and matching; worker heterogeneity (search for similar items in EconPapers)
JEL-codes: E24 E32 J64 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2020-07
New Economics Papers: this item is included in nep-dge, nep-lab and nep-mac
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Citations: View citations in EconPapers (1)

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