The knowledge economy is a complex and dynamical system, where knowledge and skills are discovered through research, diffused via education, and deployed by industry. Dynamically aligning the supply of new knowledge with the demand for practical skills through education is critical for developing national innovation systems that maximize human flourishing. In this paper, we evaluate the complex alignment of skills across the knowledge economy by creating an integrated semantic model that neurally encodes invented, instructed, and instituted skills across three major datasets: research abstracts from the Web of Science, teaching syllabi from the Open Syllabus Project, and job advertisements from Burning Glass. Analyzing the high dimensional knowledge and skills space inscribed by these data, we draw critical insight about systemic misalignment between the diversity of skills supplied and demanded in the knowledge economy. Consistent with insights from economic geography, demand for skills from industry exhibits high entropy (diversity) at local, regional, and national levels, demonstrating dense complementarities between them at all levels of the economy. Consistent with the economics and sociology of innovation, we find low entropy in the invention of new knowledge and skills through research, as specialist researchers cluster within universities. We provide new evidence, however, for the low entropy of skills taught at local, regional, and national levels, illustrating a massive mismatch between diversity in skills supplied versus demanded. This misalignment is sustained by the spatial and institutional mismatch in the organization of education by researchers at the site of skill invention over use. Our findings suggestively trace the societal costs of tethering education to researchers with narrow knowledge rather than students with broad skill needs.
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Bhargav Srinivasa Desikan
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