Integrating Academic Trajectories Into Career Path Analysis: Evidence From Linkedin

Evaluating the impact of academic training on career paths remains an ongoing challenge for educational institutions, public agencies, and organizations. Traditional methods, based on surveys and censuses, have limitations in terms of cost, granularity, and timeliness. This article formalizes a set of variables and metrics related to academic trajectories and career paths, which can be computed from data extracted from professional social networks such as LinkedIn. These metrics facilitate various types of descriptive, temporal, and correlational analyses, potentially serving as decision-support tools. A case study involving 8614 anonymized LinkedIn profiles of Chilean professionals, with career paths spanning from 1969 to 2024, demonstrates the replicability, scalability, and data-source agnosticism of the proposed metrics, in addition to revealing interesting temporal trends in job mobility and tenure.