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

Luca Coraggio, Marco Pagano, Annalisa Scognamiglio and Joacim Tåg

Journal of Financial Economics, 2025, vol. 164, issue C

Abstract: We develop a novel measure of job-worker allocation quality (JAQ) by exploiting employer-employee data with machine learning techniques. Based on our measure, the quality of job-worker matching correlates positively with individual labor earnings and firm productivity, as well as with market competition, non-family firm status, and employees’ human capital. Management plays a key role in job-worker matching: when managerial hirings and firings persistently raise management quality, the matching of rank-and-file workers to their jobs improves. JAQ can be constructed from any employer–employee data set including workers’ occupations, and used to explore research questions in corporate finance and organization economics.

Keywords: Jobs; Workers; Matching; Mismatch; Machine learning; Productivity; Management (search for similar items in EconPapers)
JEL-codes: D22 J24 J31 L22 L23 M12 M54 (search for similar items in EconPapers)
Date: 2025
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Related works:
Working Paper: JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality (2024)
Working Paper: JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality (2022)
Working Paper: JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:164:y:2025:i:c:s0304405x24002150

DOI: 10.1016/j.jfineco.2024.103992

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