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Choosing the Production Function Model for an Optimal Measurement of the Restructuring Efficiency of the Polish Metallurgical Sector in Years 2000–2015

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  • Gajdzik, Bożena
  • Gawlik, Remigiusz

Abstract

Between 2000 and 2015, the Polish metallurgical sector was subject to serious restructuring. Presented research aimed at providing a framework for possibly most accurate measurement of efficiency of this process. The study employed: (I) Quantitative research for elaboration of production function models: power regression Cobb-Douglas function with its developments; (II) Qualitative research: Analytic Hierarchy Process for assessment of relevance of efficiency evaluation criteria in reference to various production function models in metallurgy sector: (i) sectoral added value (net production); (ii) production sold; and, (iii) steel production volume. Criteria relevance has been assessed by scientists and practitioners with specialization in metallurgy. As a result the sectoral added value function has been chosen as the one that optimally reflects sector’s restructuring efficiency. This, in turn, constitutes a qualitative confirmation of previous research result, which has been verified with a quantitative method. Practical outcome is a more precise modelling of efficiency of restructuring processes in the metallurgical sector, both for scientific and business needs. The main research limitations originate from the sector itself—in order to make our tool more universal, further research should be led in parallel branches of industry.

Suggested Citation

  • Gajdzik, Bożena & Gawlik, Remigiusz, 2018. "Choosing the Production Function Model for an Optimal Measurement of the Restructuring Efficiency of the Polish Metallurgical Sector in Years 2000–2015," MPRA Paper 83618, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:83618
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    References listed on IDEAS

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    Cited by:

    1. Tatyana V. Alferova & Yelena A. Tretyakova, 2018. "Production Function of Regional Economies: The Case of the Ural Economic Region," Journal of New Economy, Ural State University of Economics, vol. 19(5), pages 72-83, October.
    2. Min-Sung Kim & Eul-Bum Lee & In-Hye Jung & Douglas Alleman, 2018. "Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    3. Martina Novotná & Ivana Faltová Leitmanová & Jiří Alina & Tomáš Volek, 2020. "Capital Intensity and Labour Productivity in Waste Companies," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
    4. Bożena Gajdzik & Włodzimierz Sroka, 2021. "Resource Intensity vs. Investment in Production Installations—The Case of the Steel Industry in Poland," Energies, MDPI, vol. 14(2), pages 1-16, January.

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    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • L61 - Industrial Organization - - Industry Studies: Manufacturing - - - Metals and Metal Products; Cement; Glass; Ceramics
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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