Morgan R. Frank (University of Pittsburgh): Network constraints on worker mobility: How skills shape careers and cities
Abstract | Policy makers aim to reduce income inequality by promoting worker mobility. However, the gap between macroscopic labor statistics and microscopic workplace requirements masks the features that shape workers’ career paths. In this talk, I explore new analysis of polarization, urban economics, and other labor trends from the perspective of workers' skills. I model career transitions as a network of occupations connected by detailed skill similarity. I will show how this approach predicts transition rates between occupations using both a nationally-representative survey and two resume data sets each representing over 100 million individual workers. Workers decrease their network embeddedness over their careers---a tendency associated with increased wages due to specialization. Further, this behavior predicts spatial worker flows between cities. The methods in this study directly connect specific workplace skills to workers’ career mobility and spatial mobility, and enable targeted investigations into the types of workplace skills that promote economic well-being for workers and workforces.
Bio | Morgan Frank is an Assistant Professor at the School of Computing and Information at the University of Pittsburgh. Morgan is interested in the complexity of AI, the future of work, and the socio-economic consequences of technological change. While many studies focus on phenotypic labor trends, Morgan’s recent research examines how genotypic skill-level processes around AI impact individuals and society. Combining labor research with investigations into the nature of AI research and the social or societal implications of AI adoption, Morgan hopes to inform our understanding of AI’s impact. Morgan has a PhD from MIT’s Media Lab, was a postdoc at MIT IDSS and the IDE, and has a masters degree in applied mathematics from the University of Vermont where he was a member of the Computational Story Lab.