Luca Coraggio (), Marco Pagano (), Annalisa Scognamiglio () and Joacim Tåg ()
Additional contact information
Luca Coraggio: University of Naples Federico II
Marco Pagano: University of Naples Federico II, and, Postal: Research Institute of Industrial Economics, Stockholm, Sweden
Annalisa Scognamiglio: University of Naples Federico II, and, Postal: Research Institute of Industrial Economics, Stockholm, Sweden
Joacim Tåg: Research Institute of Industrial Economics (IFN), Postal: Research Institute of Industrial Economics, Box 55665, SE-102 15 Stockholm, Sweden
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.
Keywords: Jobs; Workers; Matching; Mismatch; Machine Learning; Productivity; Management
JEL-codes: D22; D23; D24; G34; J24; J31; J62; L22; L23; M12; M54
Language: English
45 pages, First version: April 1, 2022. Revised: October 24, 2022.
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