Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/250691 
Year of Publication: 
2022
Series/Report no.: 
IZA Discussion Papers No. 15030
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
The rate at which workers switch employers without experiencing a spell of unemployment is one of the most important labor market indicators. However, Employer-to-Employer (EE) transitions are hard to measure in widely used matched employer-employee datasets such as those available in the US. We investigate how the lack of the exact start and end dates for job spells affect the level and cyclicality of EE transitions using Danish data containing daily information on employment relationships. Defining EE transitions based on quarterly data overestimates the EE transition rate by approximately 30% compared to daily data. The bias is procyclical and is reduced by more than 10% in recessions. We propose an algorithm that uses earnings and not just start and end dates of jobs to redefine EE transitions. Our definition performs better than definitions used in the literature.
Subjects: 
measurement problems
employer-to-employer transitions
labor market flows
time aggregation bias
JEL: 
E24
E32
J63
Document Type: 
Working Paper

Files in This Item:
File
Size
2.26 MB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.