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

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 turns out to play a key role in job-worker matching: when existing managers are replaced by better ones, the quality of rank-and-file workers’ job matches improves. JAQ can be constructed for any employer-employee data including workers’ occupations, and used to explore research questions in corporate finance and organization economics.