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

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  • Cantner, Uwe
  • Savin, Ivan
  • Vannuccini, Simone

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

  • Cantner, Uwe & Savin, Ivan & Vannuccini, Simone, 2016. "Replicator dynamics in value chains: Explaining some puzzles of market selection," Working Paper Series in Economics 83, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    • Handle: RePEc:zbw:kitwps:83
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Uwe Cantner & Holger Graf & Ekaterina Prytkova & Simone Vannuccini, 2018. "The Compositional Nature of Productivity and Innovation Slowdown," Jena Economic Research Papers 2018-006, Friedrich-Schiller-University Jena.
    5. 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.
    6. 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.

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    More about this item

    Keywords

    innovation; replicator dynamics; returns to scale; value chain;
    All these keywords.

    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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