Predicting Labor Market Competition: Leveraging Interfirm Network and Employee Skills
Information Systems Research, Forthcoming
51 Pages Posted: 23 Jun 2020
Date Written: May 30, 2020
Human capital is a key component of the knowledge economy that firms compete for in the labor market. Compared to the product market competition, the identification and prediction of labor market competitors have garnered little attention in the literature. In this study, we perform an interfirm labor market competitor analysis with a unique longitudinal employer-employee matched dataset derived from online profiles of 89,943 employees, tracking their career over 3,467 public firms from the years 2000 to 2014. Using employee migrations across firms, we derive and analyze a human capital flow network. We leverage this network to extract global cues about interfirm human capital overlap through structural equivalence and community classification. The online employee profiles also provide rich data on the explicit knowledge base of firms. In particular, they allow us to represent firms in the space of the skills possessed by its employees and measure the interfirm human capital overlap in terms of similarity in their employees' skills. We validate our proposed human capital overlap metrics in a predictive analytics framework using future employee migrations as an indicator of labor market competition. The results show that our proposed metrics have superior predictive power over conventional firm-level economic and human resource measures. We also demonstrate how our proposed metrics and the prediction framework can be incorporated into a comprehensive competitor analysis that includes both product and labor overlap between firms.
Keywords: competitor analysis, human capital, text mining, network analysis, machine learning
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