Tag Archives | topic modeling

Are Occupations “Bundles of Skills”? Identifying Latent Skill Profiles in the Labor Market Using Topic Modeling

Marie Labussière, Thijs Bol

Sociological Science April 13, 2026
0.15195/v13.a16


Skills are considered a key determinant of workers’ labor market opportunities, especially in times of rapid technological change. However, existing research rarely conceptualizes and measures skills in their own right, instead relying on occupations as a proxy. How does this limit our understanding of the labor market structure and of wage inequality? In this article, we leverage a unique dataset of millions of online job postings in the United Kingdom to measure the skill profiles of jobs and analyze their similarity within and between occupational categories. Our data-driven approach reveals substantial discrepancies between occupational classifications and the actual skill content of jobs. We further demonstrate that job-level variation in skill content constitutes an independent source of wage inequality—one that is obscured by analyses at the occupational level. These findings challenge the conventional view of occupations as coherent bundles of skills, offering new avenues for analyzing labor market stratification.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.


Marie Labussière: Sciences Po, Centre for Research on Social Inequalities (CRIS).
E-mail: marie.labussiere@sciencespo.fr.

Thijs Bol: University of Amsterdam, Department of Sociology.
E-mail: t.bol@uva.nl.

Acknowledgments: We are grateful to Luisa Burchartz, Viktor Decker, Thomas A. DiPrete, Fenella Fleischmann, Andreas Haupt, and Wouter Schakel for their helpful feedback on earlier drafts of this manuscript. This research was presented at the 2024 ISA RC28 Spring Meeting, the 2025 TASKS VII Conference, and workshops of the Institutions, Inequalities and Life Courses (IIL) research group at the University of Amsterdam, the Sciences Po Center for Research on Social Inequalities (CRIS), the Center for Research in Economics and Statistics (CREST), and the Federal Institute for Vocational Education and Training (BIBB); we thank participants for their constructive discussions. Marie Labussière gratefully acknowledges Pierre Alquier and Matteo Amestoy for their technical advice.

Funding: This work was supported by the ERC starting grant from School to Career: Towards a Career Perspective on the Labor Market Returns to Education (CAREER) (ID: 950189).


Supplemental Materials

Reproducibility Package: All code necessary to reproduce the results reported in this article is publicly available in a replication package hosted on GitHub (https://github.com/mlabussiere/Occupations-bundles-of-skills.git). The online supplement also contains additional information on the data, methods, and robustness checks. The data are subject to access restrictions and cannot be shared publicly.


  • Citation: Labussière, Marie, Thijs Bol. 2026. “Are Occupations “Bundles of Skills”? Identifying Latent Skill Profiles in the Labor Market Using Topic Modeling” Sociological Science 13: 362-407.
  • Received: December 9, 2025
  • Accepted: March 2, 2026
  • Editors: Ari Adut, Vincent Buskens
  • DOI: 10.15195/v13.a16


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Wide Social Influence and the Emergence of the Unexpected: An Empirical Test Using Spotify Data

Martin Arvidsson, Peter Hedström, Marc Keuschnigg

Sociological Science October 23, 2025
10.15195/v12.a29


Social-influence processes not only affect the rate at which behaviors spread but can also decouple adoption behavior from individual preferences, and thereby bring about unexpected collective outcomes that cannot be predicted on the basis of the initial likes and dislikes of the individuals involved. However, the conditions under which social influence can lead to such decoupling are not well understood. We identify a social-influence mechanism that widens individuals’ behavioral repertoires and breaks the link between individuals’ initial preferences and the collective outcomes they jointly bring about. We test the micro-level assumptions of the mechanism in the context of cultural choices on Spotify, combining topic modeling with traditional statistical matching to cultural change. agent-based simulation estimate peer-to-peer influence effects from digital trace data. We then use agent-based simulations to examine the macro-level consequences of “wide” social influence and its importance for explaining cultural change.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Martin Arvidsson: The Institute for Analytical Sociology, Linköping University. E-mail: martin.arvidsson@liu.se.
Peter Hedström: The Institute for Analytical Sociology, Linköping University. E-mail: peter.hedstrom@liu.se.
Marc Keuschnigg: The Institute for Analytical Sociology, Linköping University and Institute of Sociology, Leipzig University. E-mail: marc.keuschnigg@liu.se.

Acknowledgments: For helpful comments, we thank James Evans, Jacob Habinek, Mark Lutter, Arnout van de Rijt, and Duncan Watts. We are grateful for financial support from Riksbankens Jubileumsfond (M12-0301:1) and the Swedish Research Council (2013-7681, 2018-05170, 2019-00245, and 2024-01861). This research was carried out at the Swedish Excellence Center for Computational Social Science, which is also funded by the Swedish Research Council (2022-06611). Resources provided by the Swedish National Infrastructure for Computing (2024/22-1012) enabled computations.

Supplemental Materials

Reproducibility Package: A replication package has been deposited to OSF (https://osf.io/grsyt/?view_only=133867f728644ba596eb104890cb018f ) that contains code and data required to reproduce the results presented in the article.

  • Citation: Arvidsson, Martin, Peter Hedström, Marc Keuschnigg. 2025. “Wide Social Influence and the Emergence of the Unexpected: An Empirical Test Using Spotify Data.” Sociological Science 12: 715-742.
  • Received: December 16, 2024
  • Accepted: September 10, 2025
  • Editors: Ari Adut, Peter Bearman
  • DOI: 10.15195/v12.a29

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