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Efficiency and Productivity Change in the Greek Dairy Industry

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  • Vlontzos, George
  • Theodoridis, Alexandros

Abstract

The objective of this paper is to measure the efficiency and the productivity change of Greek dairy firms, using non parametric approaches. This assessment is being achieved by the computation of the CRS and the VRS DEA models, the context dependent DEA approach and finally, the evolution of the Malmquist productivity index. These empirical analyses are based on data from 29 Greek dairy firms. This implementation provided helpful information regarding the efficiency ranking of the firms that operate in the Greek dairy industry. Findings that inefficient firms are over-invested and overexposed to high risk operation practices provide suggestions for future reparative actions in order to improve efficiency. This goal does not require radial effort from firms to achieve intermediate targets. Finally, stagnated mean efficiency change does not imply stagnated efficiency change for individual firms, but it is the outcome of a large variance of efficiency change scores being achieved by dairy firms from period to period.

Suggested Citation

  • Vlontzos, George & Theodoridis, Alexandros, 2013. "Efficiency and Productivity Change in the Greek Dairy Industry," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 14(2), pages 1-15.
    • Handle: RePEc:ags:aergaa:253542
    DOI: 10.22004/ag.econ.253542
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    References listed on IDEAS

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    9. A. M. Theodoridis & A. Psychoudakis, 2008. "Efficiency Measurement in Greek Dairy Farms: Stochastic Frontier vs. Data Envelopment Analysis," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 1(2), pages 53-67, December.
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    Cited by:

    1. Apostolos Christopoulos & Ioannis Dokas & Sofia Katsimardou & Eleftherios Spyromitros, 2022. "The Malmquist Productivity measure for UK-listed firms in the aftermath of the global financial crisis," Operational Research, Springer, vol. 22(2), pages 1617-1634, April.
    2. Chieh-Wen Chang & Kun-Shan Wu & Bao-Guang Chang, 2019. "Productivity Change and Decomposition in Taiwan Bakery Enterprise―Evidence from 85 °C Company," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    3. Pinar Celikkol Geylani & Magdalena Kapelko & Spiro E. Stefanou, 2021. "Dynamic productivity change differences between global and non-global firms: a firm-level application to the U.S. food and beverage industries," Operational Research, Springer, vol. 21(2), pages 901-923, June.
    4. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    5. Magdalena Kapelko & Alfons Oude Lansink & Spiro E. Stefanou, 2017. "Input-Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(2), pages 579-599, June.
    6. Magdalena Kapelko, 2019. "Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union," Annals of Operations Research, Springer, vol. 278(1), pages 215-234, July.

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