Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/262041 
Year of Publication: 
2022
Series/Report no.: 
IFN Working Paper No. 1427
Publisher: 
Research Institute of Industrial Economics (IFN), Stockholm
Abstract: 
Does the matching between workers and jobs help explain productivity differentials across firms? To address this question we develop a job-worker allocation quality measure (JAQ) by combining employer-employee administrative data with machine learning techniques. The proposed measure is positively and significantly associated with labor earnings over workers' careers. At firm level, it features a robust positive correlation with firm productivity, and with managerial turnover leading to an improvement in the quality and experience of management. JAQ can be constructed for any employer-employee data including workers' occupations, and used to explore the effect of corporate restructuring on workers' allocation and careers.
Subjects: 
jobs
workers
matching
mismatch
machine learning
productivity
management
JEL: 
D22
D23
D24
G34
J24
J31
J62
L22
L23
M12
M54
Document Type: 
Working Paper

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