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Semiparametric stochastic metafrontier efficiency of European manufacturing firms

Author

Listed:
  • Marijn Verschelde

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Michel Dumont

    (Federal Planning Bureau Government of Belgium - Federal Planning Bureau Government of Belgium)

  • Glenn Rayp

    (Department of Economics and SHERPPA - UGent - Universiteit Gent = Ghent University = Université de Gand)

  • Bruno Merlevede

Abstract

In this paper a semiparametric stochastic metafrontier approach is used to obtain insight into the performance of manufacturing firms in Europe. We differ from standard TFP studies at the firm level as we simultaneously allow for inefficiency , noise and do not impose a functional form on the input–output relation. Using AMADEUS firm-level data covering ten manufacturing sectors from seven EU15 countries, (1) we document substantial and persistent differences in performance (with Belgium and Germany as benchmark countries and Spain lagging behind) and a wide technology gap, (2) we confirm the absence of convergence in TFP between the seven selected countries, (3) we highlight a more pronounced technology gap for smaller firms.

Suggested Citation

  • Marijn Verschelde & Michel Dumont & Glenn Rayp & Bruno Merlevede, 2015. "Semiparametric stochastic metafrontier efficiency of European manufacturing firms," Post-Print hal-01563023, HAL.
    • Handle: RePEc:hal:journl:hal-01563023
    DOI: 10.1007/s11123-015-0458-7
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    Cited by:

    1. Margareta Gardijan & Zrinka Lukač, 2018. "Measuring the relative efficiency of the food and drink industry in the chosen EU countries using the data envelopment analysis with missing data," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 695-713, September.
    2. Jin, Qianying & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2020. "Metafrontier productivity indices: Questioning the common convexification strategy," European Journal of Operational Research, Elsevier, vol. 283(2), pages 737-747.
    3. Walheer, Barnabé, 2018. "Aggregation of metafrontier technology gap ratios: the case of European sectors in 1995–2015," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1013-1026.
    4. Michel Dumont & Chantal Kegels, 2016. "Working Paper 06-16 - Young Firms and Industry Dynamics in Belgium," Working Papers 1606, Federal Planning Bureau, Belgium.
    5. Cherchye, Laurens & De Rock, Bram & Kerstens, Pieter Jan, 2018. "Production with storable and durable inputs: Nonparametric analysis of intertemporal efficiency," European Journal of Operational Research, Elsevier, vol. 270(2), pages 498-513.
    6. Pierluigi Toma, 2020. "Size and productivity: a conditional approach for Italian pharmaceutical sector," Journal of Productivity Analysis, Springer, vol. 54(1), pages 1-12, August.
    7. Themann, Michael & Koch, Nicolas, 2021. "Catching up and falling behind: Cross-country evidence on the impact of the EU ETS on firm productivity," Ruhr Economic Papers 904, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    8. 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.
    9. Kerstens, Kristiaan & O’Donnell, Christopher & Van de Woestyne, Ignace, 2019. "Metatechnology frontier and convexity: A restatement," European Journal of Operational Research, Elsevier, vol. 275(2), pages 780-792.
    10. See, Kok Fong & Rashid, Azwan Abdul & Yu, Ming-Miin, 2024. "Measuring the network capacity utilization, energy consumption and environmental inefficiency of global airlines," Energy Economics, Elsevier, vol. 132(C).
    11. Frederic Ang & Pieter Jan Kerstens, 2018. "Superlative approximation of the Luenberger-Hicks-Moorsteen productivity indicator: Theory and application," IFRO Working Paper 2018/10, University of Copenhagen, Department of Food and Resource Economics.
    12. Le, Viet & Vu, Xuan-Binh (Benjamin) & Nghiem, Son, 2018. "Technical efficiency of small and medium manufacturing firms in Vietnam: A stochastic meta-frontier analysis," Economic Analysis and Policy, Elsevier, vol. 59(C), pages 84-91.
    13. Zdeňka Náglová & Marie Šimpachová Pechrová, 2019. "Are Wine Producers With Subsidies More Technically Efficient?," Central European Business Review, Prague University of Economics and Business, vol. 2019(1), pages 1-14.
    14. Ching-Ren Chiu & Ming-Chung Chang & Jin-Li Hu, 2022. "Energy intensity improvement and energy productivity changes: an analysis of BRICS and G7 countries," Journal of Productivity Analysis, Springer, vol. 57(3), pages 297-311, June.
    15. Koch, Nicolas & Themann, Michael, 2022. "Catching up and falling behind: Cross-country evidence on the impact of the EU ETS on firm productivity," Resource and Energy Economics, Elsevier, vol. 69(C).
    16. Roets, Bart & Verschelde, Marijn & Christiaens, Johan, 2018. "Multi-output efficiency and operational safety: An analysis of railway traffic control centre performance," European Journal of Operational Research, Elsevier, vol. 271(1), pages 224-237.
    17. Ang, Frederic & Kerstens, Pieter Jan, 2020. "A superlative indicator for the Luenberger-Hicks-Moorsteen productivity indicator: Theory and application," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1161-1173.
    18. Michel Dumont & Glenn Rayp & Marijn Verschelde & Bruno Merlevede, 2016. "The contribution of start-ups and young firms to industry-level efficiency growth," Applied Economics, Taylor & Francis Journals, vol. 48(59), pages 5786-5801, December.
    19. Lee, Chi-Chuan & Huang, Tai-Hsin, 2017. "Cost efficiency and technological gap in Western European banks: A stochastic metafrontier analysis," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 161-178.
    20. Sabri Boubaker & Tu D. Q. Le & Thanh Ngo & Riadh Manita, 2025. "Predicting the performance of MSMEs: a hybrid DEA-machine learning approach," Annals of Operations Research, Springer, vol. 350(2), pages 555-577, July.

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    Keywords

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance

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