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Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union

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  • Magdalena Kapelko

    (Wroclaw University of Economics)

Abstract

This paper extends the measurement of dynamic productivity change over time to provide its full decomposition into economically meaningful components in the Data Envelopment Analysis framework. The dynamic approach accounts for dynamics of production decisions via adjustment costs and is visualized as a dynamic Luenberger productivity change indicator. The paper also estimates the dynamic productivity change and its components for a large dataset of European food companies from 2004 till 2012, grouped into Eastern, Southern, and Western regions. The study reveals three main results. First, the overall trend of dynamic technical regress and positive dynamic technical inefficiency change across almost all regions and sectors was found. Second, some differences for this general pattern were found for the bakery industry and for Eastern European firms. Thirdly, there are also some remarkable changes in indicators observed during the periods related to the financial crisis and the volatility of agricultural commodity prices.

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  • 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.
    • Handle: RePEc:spr:annopr:v:278:y:2019:i:1:d:10.1007_s10479-017-2497-0
    DOI: 10.1007/s10479-017-2497-0
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    Cited by:

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    3. Ali, Beshir M. & de Mey, Yann & Oude Lansink, Alfons G.J.M., 2021. "The effect of farm genetics expenses on dynamic productivity growth," European Journal of Operational Research, Elsevier, vol. 290(2), pages 701-717.
    4. Boussemart, Jean-Philippe & Ferrier, Gary D. & Leleu, Hervé & Shen, Zhiyang, 2020. "An expanded decomposition of the Luenberger productivity indicator with an application to the Chinese healthcare sector," Omega, Elsevier, vol. 91(C).
    5. Michael L. Polemis & Mike G. Tsionas, 2022. "Endogenous productivity: a new Bayesian perspective," Annals of Operations Research, Springer, vol. 318(1), pages 425-451, November.
    6. Gholam R. Amin & Mustapha Ibn Boamah, 2020. "A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks," Annals of Operations Research, Springer, vol. 295(1), pages 21-36, December.
    7. Chieh-Wen Chang & Kun-Shan Wu & Bao-Guang Chang & Kuo-Ren Lou, 2019. "Measuring Technical Efficiency and Returns to Scale in Taiwan’s Baking Industry―A Case Study of the 85 °C Company," Sustainability, MDPI, vol. 11(5), pages 1-14, February.

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