Arbeitspapier

JAQ of all trades: Job mismatch, firm productivity and managerial quality

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.

Sprache
Englisch

Erschienen in
Series: IFN Working Paper ; No. 1427

Klassifikation
Wirtschaft
Firm Behavior: Empirical Analysis
Organizational Behavior; Transaction Costs; Property Rights
Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
Mergers; Acquisitions; Restructuring; Voting; Proxy Contests; Corporate Governance
Human Capital; Skills; Occupational Choice; Labor Productivity
Wage Level and Structure; Wage Differentials
Job, Occupational, and Intergenerational Mobility; Promotion
Firm Organization and Market Structure
Organization of Production
Personnel Management; Executives; Executive Compensation
Personnel Economics: Labor Management
Thema
jobs
workers
matching
mismatch
machine learning
productivity
management

Ereignis
Geistige Schöpfung
(wer)
Corragio, Luca
Pagano, Marco
Scognamiglio, Annalisa
Tåg, Joacim
Ereignis
Veröffentlichung
(wer)
Research Institute of Industrial Economics (IFN)
(wo)
Stockholm
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Corragio, Luca
  • Pagano, Marco
  • Scognamiglio, Annalisa
  • Tåg, Joacim
  • Research Institute of Industrial Economics (IFN)

Entstanden

  • 2022

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