A Distributional Analysis of Earnings Losses of Displaced Workers in an Economic Depression and Recovery

2013 ◽  
Vol 76 (4) ◽  
pp. 565-588 ◽  
Author(s):  
Ossi korkeamäki ◽  
Tomi kyyrä
2017 ◽  
Vol 9 (2) ◽  
pp. 1-31 ◽  
Author(s):  
Pawel Krolikowski

Workers who suffer job displacement experience surprisingly large and persistent earnings losses. This paper proposes an explanation for this robust empirical puzzle in a model of search with a significant job ladder and increased separation rates for the recently hired. In addition to capturing the depth and persistence of displaced worker earnings losses, the model matches: employment-to-nonemployment and employer-to-employer probabilities by tenure; the empirical decomposition of earnings losses into reduced wages and employment; observed wage dispersion; and the distribution of wage changes around a nonemployment event. (JEL J31, J63, J64)


ILR Review ◽  
2001 ◽  
Vol 54 (3) ◽  
pp. 559 ◽  
Author(s):  
Kenneth A. Couch

2015 ◽  
Vol 15 (4) ◽  
pp. 1793-1829 ◽  
Author(s):  
Nicholas A. Jolly

Abstract This paper uses data from the 1968 through 1997 survey waves of the Panel Study of Income Dynamics to analyze how the long-term costs of job loss vary by a worker’s post-displacement migration status. Results from the analysis show that those individuals who move within the first 2 years after a job loss experience lower earnings losses, lower reductions in hours worked, and smaller increases in time unemployed when compared to a group of displaced workers who are not geographically mobile during the early years following this life event. Workers who move within the first 2 years after displacement face a lower probability of homeownership when compared to their non-mobile counterparts. However, this lower probability is short-lived.


1992 ◽  
Author(s):  
Louis S. Jacobson ◽  
Robert John LaLonde ◽  
Daniel Gerard Sullivan

2010 ◽  
Vol 100 (1) ◽  
pp. 572-589 ◽  
Author(s):  
Kenneth A Couch ◽  
Dana W Placzek

Earnings losses of Connecticut workers affected by mass layoff are calculated using administrative data. Estimated reductions are initially more than 30 percent and six years later, as much as 15 percent. The Connecticut estimates are smaller than comparable ones from Pennsylvania administrative data but similar to those from the Panel Study of Income Dynamics (PSID) and Department of Workforce Services (DWS). Earnings reductions in Connecticut and Pennsylvania are concentrated among Unemployment Insurance recipients. An unusually high proportion of Unemployment Insurance beneficiaries in Pennsylvania explains the larger estimated losses relative to other studies. Fixed-effects, random growth, and matching estimators produced similar earnings loss estimates suggesting each is relatively unbiased in this context.


Sign in / Sign up

Export Citation Format

Share Document