Working Paper: CEPR ID: DP17167
Authors: Marco Pagano; Luca Coraggio; Annalisa Scognamiglio; Joacim Tag
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
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Managerial Quality (L15) | Job-Worker Allocation Quality (JAQ) (J29) |
| Managerial Turnover (J63) | Job-Worker Allocation Quality (JAQ) (J29) |
| Poor Managerial Changes (M54) | Job-Worker Allocation Quality (JAQ) (J29) |
| Job-Worker Allocation Quality (JAQ) (J29) | Firm Performance (L25) |
| Job-Worker Allocation Quality (JAQ) (J29) | Labor Earnings (J31) |
| Job-Worker Allocation Quality (JAQ) (J29) | Firm Productivity (D21) |