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Scale effect in Turkish manufacturing industry: stochastic metafrontier analysis

Author

Listed:
  • Saeid Hajihassaniasl

    (Gaziantep University)

  • Recep Kök

    (Dokuz Eylul University)

Abstract

Economic theories explain the economic growth affected by accumulation of production factors and increase in productivity and efficiency. Traditional growth theories focus on the first factor where in developing countries, and especially due to the low input of capital, serious problems arise in the growth process. Accordingly, in these countries, increase in the productivity and efficiency and use of the excess capacity has focused. Therefore, the efficiency analysis of economic sectors of these countries, and especially the manufacturing sector and the factors that affect it, is very important to study. The main purpose of this study with respect to the indicators of efficiency of firms operating in Turkey manufacturing industry is to analyze the impact of scale differences on firm performance. The database used in this study is provided from the survey results (2006) belongs to Istanbul OSB, from the balance sheets and income statements of firms registered in IMKB, which operate in Turkey manufacturing industry for the 2006. Furthermore, the database for descriptive analyses was obtained from Statistics Department of Turkey (TUIK) and Turkey’s Development Bank. As the analyzing method, the stochastic frontier is used as well as the metafrontier. According to the frontier function scores in the subsectors, in small-scale firms MP, FDT and MEMSAS subsectors and in medium- and large-scale firms OCP, FDT and TSL subsectors are the most efficient subsectors. Also, according to the metafrontier function scores in the subsectors, in small-scale firms MP, MMR and OCP subsectors and in medium- and large-scale firms MP, TSL and OCP subsectors are the most efficient subsectors. Some of the results of this study reveal that, except of food stuffs and drinks (FDT) oil, chemistry, petrochemical and its derivatives (OCP) subsectors, the production inefficiency which occurs in other subsectors due to conditions of increasing return to scale is significantly caused by the operation carried out below the optimal production scale. In addition, except BMI subsector, in all other subsectors, it is seen that production scale has large impact on the efficiency of the firm and also the average efficiency of medium- and large-scale firms in each subsector is higher than the average efficiency of small-scale firms of same subsector.

Suggested Citation

  • Saeid Hajihassaniasl & Recep Kök, 2016. "Scale effect in Turkish manufacturing industry: stochastic metafrontier analysis," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 5(1), pages 1-17, December.
    • Handle: RePEc:spr:jecstr:v:5:y:2016:i:1:d:10.1186_s40008-016-0044-9
    DOI: 10.1186/s40008-016-0044-9
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    References listed on IDEAS

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    1. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    2. Barnes, Andrew Peter & Revoredo-Giha, Cesar, 2010. "A Metafrontier Analysis of Technical Efficiency of Selected European Agricultures," Working Papers 109412, Scotland's Rural College (formerly Scottish Agricultural College), Land Economy & Environment Research Group.
    3. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    4. Repkine, Alexandre, 2009. "Telecommunications Capital Intensity and Aggregate Production Efficiency: a Meta-Frontier Analysis," MPRA Paper 13059, University Library of Munich, Germany.
    5. D.S. Prasada Rao & Christopher J. O'Donnell & George E. Battese, 2003. "Metafrontier Functions for the Study of Inter-regional Productivity Differences," CEPA Working Papers Series WP012003, School of Economics, University of Queensland, Australia.
    6. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    7. Hossein Mehrabi Boshrabadi & Renato Villano & Euan Fleming, 2008. "Technical efficiency and environmental‐technological gaps in wheat production in Kerman province of Iran," Agricultural Economics, International Association of Agricultural Economists, vol. 38(1), pages 67-76, January.
    8. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    9. Wang, Xiaobing & Hockmann, Heinrich, 2012. "Technical Efficiency Under Producer’S Individual Technology: A Metafrontier Analysis," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126755, International Association of Agricultural Economists.
    10. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    11. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    12. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    13. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Technical efficiency; Technology gap ratio; Stochastic frontier analysis; Turkey’s manufacturing industry;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • 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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