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Trends in automation and the digital reorganization of work pose challenges for those seeking to make strategic skilling and hiring decisions. Existing scholarship offers limited guidance for these decisions as it tends to model sectors of the knowledge economy as modules, rather than parts of a connected, adaptive system. This project will leverage unprecedented access to the complete LinkedIn dataset to identify mixtures of knowledge, skills, and relationships that maximize income and future potential for individual participants in the knowledge economy. Coupled with enterprise and regional data, the researchers will perform natural experiments to evaluate predictions about marginal skill development and optimal skill sourcing to yield a wealth of intellectual, technological, and commercial innovations that will have a direct and positive impact on society.

Team

James Evans

Professor of Sociology

Eamon Duede

PhD student, Departments of Philosophy and the Committee on the Conceptual and Historical Studies of Science

Lingfei Wu

Postdoctoral Scholar, Knowledge Lab

Matthew Gee

Senior Research Fellow, Center for Data Science and Public Policy