JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality

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, managerial quality

JEL Classification: D22, D23, D24, G34, J24, J31, J62, L22, L23, M12, M54