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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.
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.
W. W. Powell and K. Snellman, The knowledge economy, Annual Review Sociology, vol. 30, pp. 199–220, 2004.
B. Godin, The linear model of innovation: The historical construction of an analytical framework, Science,Technology,&Human Values, vol. 31, no. 6, pp. 639–667, 2006.
T. J. Pinch and W. E. Bijker, The social construction of facts and artefacts: Or how the sociology of science and the sociology of technology might benefit each other, Social Studies of Science, vol. 14, no. 3, pp. 399–441, 1984.
K. Börner, O. Scrivner, M. Gallant, S. Ma, X. Liu, K. Chewning, L. Wu, and J. A. Evans, Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy, Proceedings of the National Academy of Sciences, vol. 115, no. 50, pp. 12630–12637, 2018.
P. -A. Balland, C. Jara-Figueroa, S. G. Petralia, M. P. A. Steijn, D. L. Rigby, and C. A. Hidalgo, Complex economic activities concentrate in large cities, Nature Human Behaviour, vol. 4, no. 3, pp. 248–254, 2020.
S. Y. Lee, R. Florida, and G. Gates, Innovation, human capital, and creativity, International Review of Public Administration, vol. 14, no. 3, pp. 13–24, 2010.
R. Shearmur, Are cities the font of innovation? A critical review of the literature on cities and innovation, Cities, vol. 29, pp. S9–S18, 2012.
G. Duranton and D. Puga, Micro-foundations of urban agglomeration economies, Handbook of Regional and Urban Economics, vol. 4, pp. 2063–2117, 2004.
S. Arbesman, J. M. Kleinberg, and S. H. Strogatz, Superlinear scaling for innovation in cities, Physical Review E, vol. 79, no. 1, p. 016115, 2009.
L. M. A. Bettencourt, J. Lobo, D. Helbing, C. Kuhnert, and G. B. West, Growth, innovation, scaling, and the pace of life in cities, Proceedings of the National Academy of Sciences, vol. 104, no. 17, pp. 7301–7306, 2007.
L. M. A. Bettencourt, J. Lobo, and D. Strumsky, Invention in the city: Increasing returns to patenting as a scaling function of metropolitan size, Research Policy, vol. 36, no. 1, pp. 107–120, 2007.
L. M. A. Bettencourt, J. Lobo, D. Strumsky, and G. B. West, Urban scaling and its deviations: Revealing the structure of wealth, innovation and crime across cities, PloS One, vol. 5, no. 11, p. e13541, 2010.
M. Gentzkow, B. Kelly, and M. Taddy, Text as data, Journal of Economic Literature, vol. 57, no. 3, pp. 535–574, 2019.
P. Hoffman, M. A. L. Ralph, and T. T. Roger, Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words, Behavior Research Methods, vol. 45, no. 3, pp. 718–730, 2013.
S. T. Dumais, A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge, Psychological Review, vol. 104, no. 2, pp. 211–240, 1997.
J. A. Evans and P. Aceves, Machine translation: Mining text for social theory, Annual Review of Sociology, vol. 42, pp. 21–50, 2016.
C. Kemp and J. B. Tenenbaum, The discovery of structural form, Proceedings of the National Academy of Sciences, vol. 105, no. 31, pp. 10687–10692, 2008.
A. T. J. Barron, J. Huang, R. L. Spang, and S. DeDeo, Individuals, institutions, and innovation in the debates of the French Revolution, Proceedings of the National Academy of Sciences, vol. 115, no. 18, pp. 4607–4612, 2018.
Q. Zhou, P. Tang, S. Liu, J. Pan, Q. Yan, and S. -C. Zhang, Learning atoms for materials discovery, Proceedings of the National Academy of Sciences, vol. 115, no. 28, pp. E6411–E6417, 2018.
V. Tshitoyan, J. Dagdelen, L. Weston, A. Dunn, Z. Rong, O. Kononova, K. A. Persson, G. Ceder, and A. Jain, Unsupervised word embeddings capture latent knowledge from materials science literature, Nature, vol. 571, no. 7763, pp. 95–98, 2019.
A. C. Kozlowski, M. Taddy, and J. A. Evans, The geometry of culture: Analyzing the meanings of class through word embeddings, American Sociological Review, vol. 84, no. 5, pp. 905–949, 2019.
N. Garg, L. Schiebinger, D. Jurafsky, and J. Zou, Word embeddings quantify 100 years of gender and ethnic stereotypes, Proceedings of the National Academy of Sciences, vol. 115, no. 16, pp. E3635–E3644, 2018.
A. Caliskan, J. J. Bryson, and A. Narayanan, Semantics derived automatically from language corpora contain human-like biases, Science, vol. 356, no. 6334, pp. 183–186, 2017.
G. Grand, I. A. Blank, F. Pereira, and E. Fedorenko, Semantic projection recovers rich human knowledge of multiple object features from word embeddings, Nature Human Behaviour, vol. 6, no. 7, pp. 975–987, 2022.
A. R. Andrés, A. Otero, and V. H. Amavilah, Using deep learning neural networks to predict the knowledge economy index for developing and emerging economies, Expert Systems with Applications, vol. 184, p. 115514, 2021.
F. Iandolo, F. Loia, I. Fulco, C. Nespoli, and F. Caputo, Combining big data and artificial intelligence for managing collective knowledge in unpredictable environment—Insights from the Chinese case in facing COVID-19, Journal of the Knowledge Economy, vol. 12, no. 4, pp. 1982–1996, 2021.
S. V. D. Walt, S. C. Colbert, and G. Varoquaux, The NumPy array: A structure for efficient numerical computation, Computing in Science and Engineering, vol. 13, no. 2, pp. 22–30, 2011.
J. D. Hunter, Matplotlib: A 2D graphics environment, Computing in Science and Engineering, vol. 9, no. 3, pp. 90–95, 2007.
F. Perez and B. E. Granger, IPython: A system for interactive scientific computing, Computing in Science and Engineering, vol. 9, no. 3, pp. 21–29, 2007.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
B. Hershbein and L. B. Kahn, Do recessions accelerate routine-biased technological change? Evidence from vacancy postings, American Economic Review, vol. 108, no. 7, pp. 1737–1772, 2018.
L. V. D. Maaten and G. Hinton, Visualizing data using t-SNE, Journal of Machine Learning Research, vol. 9, no. 11, pp. 2579–2605, 2008.
D. N. Politis and J. P. Romano, The stationary bootstrap, Journal of the American Statistical Association, vol. 89, no. 428, pp. 1303–1313, 1994.
C. A. Hidalgo, Economic complexity theory and applications, Nature Reviews Physics, vol. 3, no. 2, pp. 92–113, 2021.
L. Bromham, R. Dinnage, and X. Hua, Interdisciplinary research has consistently lower funding success, Nature, vol. 534, no. 7609, pp. 684–687, 2016.
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