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Replicator dynamics in value chains: explaining some puzzles of market selection

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  • Uwe Cantner
  • Ivan Savin
  • Simone Vannuccini

Abstract

The pure model of replicator dynamics though providing important insights in the evolution of markets has not found much of empirical support. This paper extends the model to the case of firms vertically integrated in value chains. We show that i) by taking value chains into account, the replicator dynamics may revert its effect. In these regressive developments of market selection, firms with low fitness expand because of being integrated with highly fit partners, and the other way around; ii) allowing partner’s switching within a value chain illustrates that periods of instability in the early stage of industry life-cycle may be the result of an ’optimization’ of partners within a value chain providing a novel and simple explanation to the evidence discussed by Mazzucato (1998); iii) there are distinct differences in the contribution to market selection between the layers of a value chain, causing strategic advantages to firms in partnering.

Suggested Citation

  • Uwe Cantner & Ivan Savin & Simone Vannuccini, 2016. "Replicator dynamics in value chains: explaining some puzzles of market selection," Working Papers of BETA 2016-09, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    • Handle: RePEc:ulp:sbbeta:2016-09
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    Cited by:

    1. Ivan Savin & Maria Novitskaya, 2023. "Data-driven definitions of gazelle companies that rule out chance: application for Russia and Spain," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 13(3), pages 507-542, September.
    2. Ivan Savin & Kristina Chukavina & Andrey Pushkarev, 2023. "Topic-based classification and identification of global trends for startup companies," Small Business Economics, Springer, vol. 60(2), pages 659-689, February.
    3. Grodzicki, Maciej J. & Skrzypek, Jurand, 2020. "Cost-competitiveness and structural change in value chains – vertically-integrated analysis of the European automotive sector," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 276-287.
    4. Eric Kemp-Benedict, 2022. "A classical-evolutionary model of technological change," Journal of Evolutionary Economics, Springer, vol. 32(4), pages 1303-1343, September.
    5. Uwe Cantner & Holger Graf & Ekaterina Prytkova & Simone Vannuccini, 2018. "The Compositional Nature of Productivity and Innovation Slowdown," Jena Economics Research Papers 2018-006, Friedrich-Schiller-University Jena.
    6. Jan Schulz & Daniel M. Mayerhoffer, 2021. "Equal chances, unequal outcomes? Network-based evolutionary learning and the industrial dynamics of superstar firms," Journal of Business Economics, Springer, vol. 91(9), pages 1357-1385, November.
    7. Shungo Sakaki, 2019. "Equality in Income and Sustainability in Economic Growth: Agent-Based Simulations on OECD Data," Sustainability, MDPI, vol. 11(20), pages 1-32, October.
    8. Luca Fontanelli, 2023. "Theories of market selection: a survey," LEM Papers Series 2023/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    9. Savin, Ivan & Mundt, Philipp, 2022. "Drivers of productivity change in global value chains: Reallocation vs. innovation," Economics Letters, Elsevier, vol. 220(C).
    10. Shungo Sakaki, 2017. "Income distribution management to sustain long-term economic growth: does the equalization of income distribution contribute to long-term economic growth?," Evolutionary and Institutional Economics Review, Springer, vol. 14(2), pages 363-395, December.
    11. Savin, I., 2020. "Studying market selection in Russia and abroad: Measurement problems, national specificity and stimulating methods," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 197-204.
    12. Diego d’Andria, 2025. "Insider imitation with product differentiation," Journal of Evolutionary Economics, Springer, vol. 35(1), pages 95-122, January.
    13. Uwe Cantner & Simone Vannuccini, 2017. "Innovation and lock-in," Chapters, in: Harald Bathelt & Patrick Cohendet & Sebastian Henn & Laurent Simon (ed.), The Elgar Companion to Innovation and Knowledge Creation, chapter 11, pages 165-181, Edward Elgar Publishing.
    14. Shungo Sakaki, 2023. "The rationality of adaptive decision-making and the feasibility of optimal growth planning," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    15. Shungo Sakaki, 2018. "A method of building simulation model for organizational decision-making and inter-organizational control," Evolutionary and Institutional Economics Review, Springer, vol. 15(2), pages 289-313, December.
    16. Cantner, Uwe & Vannuccini, Simone, 2021. "Pervasive technologies and industrial linkages: Modeling acquired purposes," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 386-399.

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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