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

Luca Coraggio, Marco Pagano, Annalisa Scognamiglio, Joacim Tåg

Research output: Book/ReportCommissioned report


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.
Original languageEnglish
Place of PublicationStockholm
PublisherIFN - Research Institute of Industrial Economics
Number of pages53
Publication statusPublished - 2022
MoE publication typeD4 Published development or research report or study

Publication series

NameIFN Working Paper


  • 511 Economics
  • jobs
  • workers
  • matching
  • mismatch
  • machine learning
  • productivity
  • management
  • managerial quality


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