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Semiparametric stochastic metafrontier efficiency of European manufacturing firms

Marijn Verschelde (UGent) , Michel Dumont (UGent) , Glenn Rayp (UGent) and Bruno Merlevede (UGent)
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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 simul- taneously 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.
Keywords
LOCAL LIKELIHOOD ESTIMATION, NONPARAMETRIC FRONTIER ESTIMATION, REGRESSION, TRADE, COEFFICIENT, ESTIMATORS, Manufacturing, Stochastic frontier, Semiparametric frontier, Kernel estimation, Metafrontier estimation, Productive efficiency, TURNOVER, PRODUCTIVITY, GENERALIZED LEONTIEF, MINFLEX-LAURENT

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MLA
Verschelde, Marijn, et al. “Semiparametric Stochastic Metafrontier Efficiency of European Manufacturing Firms.” JOURNAL OF PRODUCTIVITY ANALYSIS, vol. 45, no. 1, 2016, pp. 53–69, doi:10.1007/s11123-015-0458-7.
APA
Verschelde, M., Dumont, M., Rayp, G., & Merlevede, B. (2016). Semiparametric stochastic metafrontier efficiency of European manufacturing firms. JOURNAL OF PRODUCTIVITY ANALYSIS, 45(1), 53–69. https://doi.org/10.1007/s11123-015-0458-7
Chicago author-date
Verschelde, Marijn, Michel Dumont, Glenn Rayp, and Bruno Merlevede. 2016. “Semiparametric Stochastic Metafrontier Efficiency of European Manufacturing Firms.” JOURNAL OF PRODUCTIVITY ANALYSIS 45 (1): 53–69. https://doi.org/10.1007/s11123-015-0458-7.
Chicago author-date (all authors)
Verschelde, Marijn, Michel Dumont, Glenn Rayp, and Bruno Merlevede. 2016. “Semiparametric Stochastic Metafrontier Efficiency of European Manufacturing Firms.” JOURNAL OF PRODUCTIVITY ANALYSIS 45 (1): 53–69. doi:10.1007/s11123-015-0458-7.
Vancouver
1.
Verschelde M, Dumont M, Rayp G, Merlevede B. Semiparametric stochastic metafrontier efficiency of European manufacturing firms. JOURNAL OF PRODUCTIVITY ANALYSIS. 2016;45(1):53–69.
IEEE
[1]
M. Verschelde, M. Dumont, G. Rayp, and B. Merlevede, “Semiparametric stochastic metafrontier efficiency of European manufacturing firms,” JOURNAL OF PRODUCTIVITY ANALYSIS, vol. 45, no. 1, pp. 53–69, 2016.
@article{6983725,
  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 simul- taneously 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.}},
  author       = {{Verschelde, Marijn and Dumont, Michel and Rayp, Glenn and Merlevede, Bruno}},
  issn         = {{0895-562X}},
  journal      = {{JOURNAL OF PRODUCTIVITY ANALYSIS}},
  keywords     = {{LOCAL LIKELIHOOD ESTIMATION,NONPARAMETRIC FRONTIER ESTIMATION,REGRESSION,TRADE,COEFFICIENT,ESTIMATORS,Manufacturing,Stochastic frontier,Semiparametric frontier,Kernel estimation,Metafrontier estimation,Productive efficiency,TURNOVER,PRODUCTIVITY,GENERALIZED LEONTIEF,MINFLEX-LAURENT}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{53--69}},
  title        = {{Semiparametric stochastic metafrontier efficiency of European manufacturing firms}},
  url          = {{http://doi.org/10.1007/s11123-015-0458-7}},
  volume       = {{45}},
  year         = {{2016}},
}

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