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Cumulative (Dis)Advantage and the Matthew Effect in Life-Course Analysis

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  • Miia Bask
  • Mikael Bask

Abstract

To foster a deeper understanding of the mechanisms behind inequality in society, it is crucial to work with well-defined concepts associated with such mechanisms. The aim of this paper is to define cumulative (dis)advantage and the Matthew effect. We argue that cumulative (dis)advantage is an intra-individual micro-level phenomenon, that the Matthew effect is an inter-individual macro-level phenomenon and that an appropriate measure of the Matthew effect focuses on the mechanism or dynamic process that generates inequality. The Matthew mechanism is, therefore, a better name for the phenomenon, where we provide a novel measure of the mechanism, including a proof-of-principle analysis using disposable personal income data. Finally, because socio-economic theory should be able to explain cumulative (dis)advantage and the Matthew mechanism when they are detected in data, we discuss the types of models that may explain the phenomena. We argue that interactions-based models in the literature traditions of analytical sociology and statistical mechanics serve this purpose.

Suggested Citation

  • Miia Bask & Mikael Bask, 2015. "Cumulative (Dis)Advantage and the Matthew Effect in Life-Course Analysis," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0142447
    DOI: 10.1371/journal.pone.0142447
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    Cited by:

    1. Frenger, Monika & Emrich, Eike & Geber, Sebastian & Follert, Florian & Pierdzioch, Christian, 2019. "The influence of performance parameters on market value," Working Papers of the European Institute for Socioeconomics 30, European Institute for Socioeconomics (EIS), Saarbrücken.
    2. Robin Maialeh, 2024. "Success-breeds-success distributional dynamics in stochastic competitive systems," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(2), pages 1901-1916, April.
    3. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    4. Sarah Shandera & Jes L Matsick & David R Hunter & Louis Leblond, 2021. "RASE: Modeling cumulative disadvantage due to marginalized group status in academia," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-20, December.
    5. Mikael Bask, 2024. "Skill, status and the Matthew effect: a theoretical framework," Journal of Computational Social Science, Springer, vol. 7(3), pages 2221-2253, December.
    6. Liao, Chien Hsiang, 2021. "The Matthew effect and the halo effect in research funding," Journal of Informetrics, Elsevier, vol. 15(1).
    7. Yegorov, Yury & Wirl, Franz & Grass, Dieter & Eigruber, Markus & Feichtinger, Gustav, 2022. "On the matthew effect on individual investments in skills in arts, sports and science," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 178-199.
    8. Feichtinger, Gustav & Grass, Dieter & Kort, Peter M. & Seidl, Andrea, 2021. "On the Matthew effect in research careers," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).

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