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Optimisation of the relationship between structural parameters of the processing industry as a way to increase its efficiency

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Industry, which on average accounts for about 60% of commodity exports in the EU-28, with over 58% resulting from the processing industry, plays a key role in ensuring the competitiveness of EU countries. The article aims to simulate the influence of structural processing industry parameters on the industry’s efficiency. Correlation methods and the regression analysis were used to substantiate the hypotheses regarding the effect that the share comprised of high-tech and medium-high-tech industries has on the output structure, and the impact made by the share of imports in the intermediate consumption of those industries on the efficiency (the share of gross value added (GVA) in output) of the processing industry. Based on the criteria indicating the increased technological level and reduced import dependence, economic and mathematical models of optimisation were created for the output structure and intermediate consumption of the processing industry, which were then solved using the linear programming method. The authors present the mathematical proof of the relationship between the change in structural parameters (shares of high-tech and medium-tech industries and the share of imports in the structure of their intermediate consumption) of the processing industry and the ratio of the gross value added/output. The results of the simulation, which were based on data from the European Statistical Office and the Organization for Economic Cooperation and Development, provide an analytical basis for selecting industrial policy benchmarks.
Rocznik
Strony
7--20
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
  • Institute of Regional Research named after M.I. Dolishniy of the NAS of Ukraine, Ukraine
  • Institute of Regional Research named after M.I. Dolishniy of the NAS of Ukraine, Ukraine
  • Bronislaw Markiewicz State Higher School of Technology and Economics, Poland
Bibliografia
  • Altan, Ş., Dogan, S., & İloğlu, H. (2016). Maximization of National Income with Linear Programming and Input-Output Analysis: An Application for Turkey. Journal of Business & Economic Policy, 3(2), 29-37.
  • Can, T. (2012). Input-Output Analysis with Linear Programming: The Case of Turkey. International Research Journal of Finance and Economics, 89, 38-145.
  • Čapek, J. (2016). Structural Changes in the Czech Economy: A DSGE Model Approach. Prague Economic Papers, 25(1), 37-52. doi: 10.18267/j.pep.535
  • Ishchuk, S.O. (Ed.), & Sozanskyy, L.Jo. (2018). Competitive Advantages of the Industrial Sector of Ukraine’s Economy: Regional Dimension. Lviv, Ukraine: Institute of Regional Research named after M.I. Dolishniy of the NAS of Ukraine.
  • Olczyk, M., & Kordalska, A. (2017). International Competitiveness of Czech Manufacturing – A Sectoral Approach with Error Correction Model. Prague Economic Papers, 26(2), 213-226. doi: 10.18267/j. pep.605
  • Organisation for Economic Cooperation and Development (OECD). Input-Output Tables 2018 edition. Retrieved from https://stats.oecd.org/index. aspx?r=705558
  • Sharify, N. (2018). A nonlinear supply-driven input-output model. Prague Economic Papers, 27(4), 494-502. doi: 10.18267/j.pep.657
  • Sozanskyy, L. (2018a). Models of optimization of the structure of industry in Ukraine. Economy and Forecasting, 1, 79-97. doi: 10.15407/eip2018.01.079
  • Sozanskyy, L. (2018b). Structurale assessment of the Industry of Ukraine and Poland. Transborder Economics International Journal on Transborder Economics, Politics and Statistics, 3(1), 83-93.
  • State Statistics Service of Eurostat (2016). Manufacturing groups are formed in accordance with Eurostat indicators on High-tech industry and Knowledge – intensive services: Aggregations of manufacturing based on NACE Rev.2. Retrieved from http://ec.europa.eu/eurostat/cache/metadata/Annexes/ htec_esms_an3.pdf
  • State Statistics Service of Eurostat (2019). National accounts aggregates by industry (up to NACE A*64). Retrieved from https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10_a64&lang=en
  • Tan, R.R., Aviso, K.B., Promentilla, M.A.B., Yu, K.D.S., & Santos, J.R. (2019). Input-output models for sustainable industrial systems: implementation using LINGO. Singapore: Springer Nature Singapore Pte Ltd. doi: 10.1007/978-981-13-1873-3_5
  • Taušer, Jo., Arltová M., & Žamberský, P. (2015). Czech Exports and German Gdp: A Closer Look. Prague Economic Papers, 24(1), 17-37. doi: 10.18267/j.pep.498
  • United Nations Industrial Development Organization (UNIDO) (2019). Manufacturing industries at the 2-digit level of ISIC Rev 4 by technological intensity. Classification of manufacturing sectors by technological intensity (ISIC Revision 4). Retrieved from https://stat.unido.org/content/focus/classification-ofmanufacturing-sectors-by-technological-intensity- %2528isic-revision-4%2529;jsessionid=4DB1A3A5812144CACC956F4B8137C1CF
  • Vogstad, K.-O. (2009). Input-Output Analysis and Linear Programming. In S. Suh (Eds.), Handbook of Input-Output Economics in Industrial Ecology. Eco-Efficiency in Industry and Science, 23, Springer. doi: 10.1007/978-1-4020-5737-3_36
  • Wlodarczyk, A. (2013). The optimization of the structure of the factors of production in the food industry in Poland – the approach based on the production function methodology. C. B. Illes, F. Bylok, A. Dunay, L. Cichobłaziński (Eds.), People, Knowledge and Modern Technologies in the Management of Contemporary Organizations. Theoretical and Practical Approaches (pp. 225-239). Częstowchowa, Poland: Czestochowa University of Technology.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5b21a02f-2593-418b-a575-91185dfc45b4
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