Skills-Approximate Occupations: Using Networks to Guide Jobs Retraining

Abstract

An issue often confronting economic development agencies is how to minimize unemployment due to disruptions like technological change, trade wars, recessions, or other economic shocks. Decision makers are left to craft policies that can absorb surplus labor with as little pain to workers as possible. The key question, in terms of skills and occupations, is: how can we fill labor gaps with labor surplus efficiently? To address this question, we develop a policy-oriented method to measure the skills proximity of occupations. Using network analysis, we identify key missing skills and determine what occupations are “skills proximate” to one another. Inspired by techniques from ecology, our skills proximities are derived from occupational patterns of geographical co-occurrence. To demonstrate the potential of this method as a policy tool, we provide a case study of a possible worker retraining pathway for Northern Virginia, which was simultaneously impacted by the COVID-19 pandemic and the arrival of a second headquarters for Amazon.