Applying data analytics for talent acquisition and retention has been identified as one of the most urgent challenges facing HR leaders around the world; however, it is also one of the challenges that firms are least prepared to tackle. Our research strives to narrow such a capability gap between the urgency and readiness of data-driven human resource management.
First, we predict interfirm competitors for human capital in the labor market utilizing the rich information contained in over 89,000 LinkedIn users' profiles. 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 and allow us to 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.
Second, we estimate the effect of skilled immigrants on the native U.S. workers' turnover probability. We apply unsupervised machine learning to categorize employees' self-reported skills and find that skilled immigrants disproportionately specialize in IT. In contrast, the native workers predominantly focus on management and analyst skills. Utilizing the randomness in the H-1B visa lottery system and a 2SLS design, we find that a 1 percentage point increase in a firm's proportion of skilled immigrant employees leads to a decrease of 0.69 percentage points in a native employee's turnover risk. However, this beneficial crowding-in effect varies for native workers with different skills. Our methodology highlights the need to account for a multifaceted view of the skilled immigration's effect on native workers. Finally, we also propose a set of features and models that are able to effectively predict future employee turnover outcomes. Our predictive models can provide significant utility to managers by identifying individuals with the highest turnover risks.