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Technical efficiency in firm games with constant returns to scale and $$\alpha $$-returns to scale

Author

Listed:
  • Walter Briec

    (LAMPS - LAboratoire de Modélisation Pluridisciplinaire et Simulations - UPVD - Université de Perpignan Via Domitia)

  • Marc Dubois

    (UNIMES - Université de Nîmes, CUFR - Centre Universitaire de Formation et de Recherche de Mayotte (CUFR))

  • Stéphane Mussard

    (CHROME - Détection, évaluation, gestion des risques CHROniques et éMErgents (CHROME) / Université de Nîmes - UNIMES - Université de Nîmes)

Abstract

Under a technology based on the generalized mean of inputs and outputs with constant returns to scale (CRS), the firms have incentive to merge (in a firm game) in order to improve their technical efficiency. A directional complementarity property in inputs and in outputs is introduced. It is shown that the core of the firm game is non-void whenever the aggregate technology of each coalition exhibits complementarity in outputs and CRS. In the case of $$\alpha $$ α -returns to scale, the firms have incentive to merge (improvement of technical efficiency) when there are both directional complementarity in inputs and in outputs.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Walter Briec & Marc Dubois & Stéphane Mussard, 2021. "Technical efficiency in firm games with constant returns to scale and $$\alpha $$-returns to scale," Post-Print hal-03264193, HAL.
    • Handle: RePEc:hal:journl:hal-03264193
    DOI: 10.1007/s10479-021-04056-6
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    References listed on IDEAS

    as
    1. Briec, Walter & Mussard, Stéphane, 2014. "Efficient firm groups: Allocative efficiency in cooperative games," European Journal of Operational Research, Elsevier, vol. 239(1), pages 286-296.
    2. Färe, Rolf & Mizobuchi, Hideyuki & Zelenyuk, Valentin, 2021. "Hicks neutrality and homotheticity in technologies with multiple inputs and multiple outputs," Omega, Elsevier, vol. 101(C).
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    7. Briec, Walter & Mussard, Stéphane, 2020. "Improvement of technical efficiency of firm groups," European Journal of Operational Research, Elsevier, vol. 283(3), pages 991-1001.
    8. Boussemart, Jean-Philippe & Briec, Walter & Peypoch, Nicolas & Tavéra, Christophe, 2009. "[alpha]-Returns to scale and multi-output production technologies," European Journal of Operational Research, Elsevier, vol. 197(1), pages 332-339, August.
    9. Walter Briec & Benoit Dervaux & Hervé Leleu, 2003. "Aggregation of Directional Distance Functions and Industrial Efficiency," Journal of Economics, Springer, vol. 79(3), pages 237-261, July.
    10. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
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    13. Louisa Andriamasy & Walter Briec & Stephane Mussard, 2016. "On Some Relations between Several Generalized Convex DEA Models," Cahiers de recherche 16-02, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    14. Ravelojaona, Paola, 2019. "On constant elasticity of substitution – Constant elasticity of transformation Directional Distance Functions," European Journal of Operational Research, Elsevier, vol. 272(2), pages 780-791.
    15. Briec, Walter & Liang, Qi Bin, 2011. "On some semilattice structures for production technologies," European Journal of Operational Research, Elsevier, vol. 215(3), pages 740-749, December.
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    20. Walter Briec, 2015. "Some Remarks on an Idempotent and Non-Associative Convex Structure," Post-Print hal-01416352, HAL.
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    1. Tsionas, Mike & Parmeter, Christopher F. & Zelenyuk, Valentin, 2023. "Bayesian Artificial Neural Networks for frontier efficiency analysis," Journal of Econometrics, Elsevier, vol. 236(2).

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