Skill Loss, Job Mismatch and Slow Recoveries from Large Recessions
Title: Skill Loss, Job Mismatch and Slow Recoveries from Large Recessions
DNr: SNIC 2022/5-262
Project Type: SNIC Medium Compute
Principal Investigator: Karl Walentin <karl.walentin@nek.uu.se>
Affiliation: Uppsala universitet
Duration: 2022-06-01 – 2023-06-01
Classification: 50201
Homepage: https://sites.google.com/view/karlwalentin
Keywords:

Abstract

In this paper we ask to what degree i) human capital dynamics induced by skill loss during unemployment and ii) decrease in match quality contributed to the slow recovery from the Great Recession, in particular the low post-2009 growth in GDP, employment, labor productivity and real wages. Match quality has decreased because of the sullying effect of the recession that follows from reduced hiring activity and the resulting collapse of the job ladder. We find that the increase in unemployment during the initial phase of the Great Recession had long-lasting effects through the skill loss it induced, mainly in terms of increased unemployment and reduced GDP. This model also has important policy implications regarding the recession induced by the Corona crisis; we will quantify the lasting effects of this recession and how they depend on both size and persistence of the Corona-recession. As part of the project we will add a second related paper with the title, "Frictional and structural unemployment over the business cycle". In this paper, we explore the role that structural unemployment plays and how it interacts with frictional unemployment over the business cycle. Implications for, for example, productivity, wage formation and job creation are also documented. Methodologically, we build on the dynamic model of frictional unemployment and structural unemployment that we developed in Olovsson, Walentin and Westermark (2021). The hypothesis is that there is a large gap in the literature as it has abstracted from structural unemployment in the analysis of stabilization policy and business cycles more generally.