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

Luca Coraggio (), Marco Pagano, Annalisa Scognamiglio () and Joacim Tåg

No 1427, Working Paper Series from Research Institute of Industrial Economics

Abstract: We present a novel measure of job-worker allocation quality (JAQ) by exploiting employer-employee data with machine learning techniques and validate it in various ways. Our measure correlates positively with earnings and negatively with separations over individual workers’ careers. At firm level, it increases with competition, non-family firm status, workers’ human capital and has a robust correlation with productivity. The quality of rank-and-file workers’ job matches responds positively to improvements in management quality. JAQ can be constructed for any employer-employee data including workers’ occupations, and used to explore research questions in organization and labor economics, as well as in corporate finance.

Keywords: Jobs; Workers; Matching; Mismatch; Machine Learning; Productivity; Management (search for similar items in EconPapers)
JEL-codes: D22 D23 D24 G34 J24 J31 J62 L22 L23 M12 M54 (search for similar items in EconPapers)
Pages: 53 pages
Date: 2022-04-01, Revised 2023-04-25
New Economics Papers: this item is included in nep-bec, nep-big, nep-eff, nep-hrm and nep-lma
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Working Paper: JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality (2022) Downloads
Working Paper: JAQ of All Trades: Job Mismatch, Firm Productivity and Managerial Quality (2022) Downloads
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