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Open Access

Misalignment Between Skills Discovered, Disseminated, and Deployed in the Knowledge Economy

Department of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
Knowledge Lab, the Department of Sociology, University of Chicago, Chicago, IL 60615, USA, and also with the Santa Fe Institute, Santa Fe, NM 87501, USA
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Abstract

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.

References

[1]

W. W. Powell and K. Snellman, The knowledge economy, Annual Review Sociology, vol. 30, pp. 199–220, 2004.

[2]
D. C. Mowery and N. Rosenberg, The US national innovation system, in National Innovation Systems: A Comparative Analysis, R. R. Nelson, ed. New York, NY, USA: Oxford University Press, 1993, pp. 29–75.
[3]

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.

[4]

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.

[5]

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.

[6]
A. Marshall, Industry and Trade. New York, NY, USA: MacMillan and Co., 1919.
[7]
E. Moretti, The New Geography of Jobs. New York, NY, USA: Houghton Mifflin Harcourt, 2012.
[8]

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.

[9]
R. Florida, Cities and the Creative Class. New York, NY, USA: Routledge, 2005.
DOI
[10]

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.

[11]

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.

[12]

G. Duranton and D. Puga, Micro-foundations of urban agglomeration economies, Handbook of Regional and Urban Economics, vol. 4, pp. 2063–2117, 2004.

[13]

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.

[14]

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.

[15]

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.

[16]

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.

[17]
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality, in Proc. 27th Annual Conference on Neural Information Processing Systems 2013, Lake Tahoe, NV, USA, 2013, pp. 3111–3119.
[18]

M. Gentzkow, B. Kelly, and M. Taddy, Text as data, Journal of Economic Literature, vol. 57, no. 3, pp. 535–574, 2019.

[19]

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.

[20]

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.

[21]
Q. Le and T. Mikolov, Distributed representations of sentences and documents, in Proc. 31st International Conference on Machine Learning, Beijing, China, 2014, pp. 1188–1196.
[22]

J. A. Evans and P. Aceves, Machine translation: Mining text for social theory, Annual Review of Sociology, vol. 42, pp. 21–50, 2016.

[23]

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.

[24]
D. M. Blei and J. D. Lafferty, Dynamic topic models, in Proc. of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, 2006, pp. 113–120.
DOI
[25]
W. L. Hamilton, J. Leskovec, and D. Jurafsky, Diachronic word embeddings reveal statistical laws of semantic change, in Proc. 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 2016, pp. 1489–1501.
[26]

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.

[27]
C. Danescu-Niculescu-Mizil, R. West, D. Jurafsky, J. Leskovec, and C. Potts, No country for old members: User lifecycle and linguistic change in online communities, in Proc. 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 307–318.
[28]

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.

[29]

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.

[30]

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.

[31]

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.

[32]

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.

[33]

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.

[34]

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.

[35]

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.

[36]
K. Lix, A. Goldberg, S. B. Srivastava, and M. A. Valentine, Aligning differences: Discursive diversity and team performance, Management Science, doi: 10.31235/osf.io/8pjga.
[37]
R. Yu, S. Das, S. Gurajada, K. Varshney, H. Raghavan, and C. Lastra-Anadon, A research framework for understanding education-occupation alignment with NLP techniques, in Proc. 1st Workshop on NLP for Positive Impact, Online, 2021, pp. 100–106.
[38]
B. Biasi, D. J. Deming, and P. Moser, Education and innovation, Technical report, National Bureau of Economic Research, Cambridge, MA, USA, 2021.
[39]
Thomson Reuters, Web of Science, https://www.webofknowledge.com/, 2012.
[40]
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, Deep contextualized word representations, arXiv preprint arXiv: 1802.05365, 2018.
[41]
J. Devlin, M. -W. Chang, K. Lee, and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv: 1810.04805, 2018.
[42]
S. Gururangan, A. Marasović, S. Swayamdipta, K. Lo, I. Beltagy, D. Downey, and N. A. Smith, Don’t stop pretraining: Adapt language models to domains and tasks, in Proc. 58th Annual Meeting of the Association for Computational Linguistics, Online, 2020, pp. 8342–8360.
[43]

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.

[44]
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., SciPy 1.0: Fundamental algorithms for scientific computing in Python, https://scipy.org/, 2020.
[45]

J. D. Hunter, Matplotlib: A 2D graphics environment, Computing in Science and Engineering, vol. 9, no. 3, pp. 90–95, 2007.

[46]

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.

[47]
R. Řehřek and P. Sojka, Gensim—Statistical semantics in python, https://radimrehurek.com/gensim/, 2011.
[48]
M. Honnibal, SpaCy: Industrial-strength natural language processing (NLP) with Python and Cython, https://spacy.io/, 2015.
[49]
B. S. Desikan, Natural Language Processing and Computational Linguistics: A Practical Guide to Text Analysis with Python, Gensim, spaCy, and Keras. Birmingham, UK: Packt Publishing Ltd, 2018
[50]

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.

[51]
Keras, https://keras.io/, 2015.
[52]
M. Polanyi, Personal Knowledge. New York, NY, USA: Routledge, 2012.
[53]
P. Bourdieu, Habitus and Field: General Sociology. Hoboken, NJ, USA: Wiley, 1983.
[54]
H. Collins, Tacit and Explicit Knowledge. Chicago, IL, USA: University of Chicago Press, 2010.
[55]

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.

[56]
A. P. Carnevale, T. Jayasundera, and D. Repnikov, Understanding online job ads data, Tech. Rep., Georgetown University, Center on Education and the Workforce, Washington, DC, USA, 2014.
[57]
V. Lancaster, D. Mahoney-Nair, and N. J. Ratcliff, Technology report review of burning glass job-ad data, Technical report, Biocomplexity Institute and Initiative Social and Decision Analytics Division, University of Virginia, Charlottesville, VA, USA, 2019.
[58]
D. Tong, L. Wu, and J. A. Evans, Low-skilled occupations face the highest re-skilling pressure, arXiv preprint arXiv: 2101.11505, 2021.
[59]

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.

[60]
D. N. Politis and J. P. Romano, A circular block-resampling procedure for stationary data, Technical report, Department of Statistics, Purdue University, West Lafayette, IN, USA, 1991.
[61]

D. N. Politis and J. P. Romano, The stationary bootstrap, Journal of the American Statistical Association, vol. 89, no. 428, pp. 1303–1313, 1994.

[62]

C. A. Hidalgo, Economic complexity theory and applications, Nature Reviews Physics, vol. 3, no. 2, pp. 92–113, 2021.

[63]
F. Shi and J. Evans, Science and technology advance through surprise, arXiv preprint arXiv: 1910.09370, 2020.
[64]

L. Bromham, R. Dinnage, and X. Hua, Interdisciplinary research has consistently lower funding success, Nature, vol. 534, no. 7609, pp. 684–687, 2016.

[65]
W. James, Talks to Teachers on Psychology and to Students on Some of Life’s Ideals. Cambridge, MA, USA: Harvard University Press, 1983.
Journal of Social Computing
Pages 191-205
Cite this article:
Desikan BS, Evans J. Misalignment Between Skills Discovered, Disseminated, and Deployed in the Knowledge Economy. Journal of Social Computing, 2022, 3(3): 191-205. https://doi.org/10.23919/JSC.2022.0013

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Received: 19 August 2022
Revised: 19 October 2022
Accepted: 20 October 2022
Published: 30 September 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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