IDEAS home Printed from https://ideas.repec.org/a/spr/jecstr/v5y2016i1d10.1186_s40008-016-0044-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40008-016-0044-9
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1186/s40008-016-0044-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Repkine, Alexandre, 2009. "Telecommunications Capital Intensity and Aggregate Production Efficiency: a Meta-Frontier Analysis," MPRA Paper 13059, University Library of Munich, Germany.
    2. 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.
    3. 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.
    4. 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.
    5. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Joachim Nyemeck BINAM & Jim GOCKOWSKI & Guy Blaise NKAMLEU, 2008. "Technical Efficiency And Productivity Potential Of Cocoa Farmers In West African Countries," The Developing Economies, Institute of Developing Economies, vol. 46(3), pages 242-263, September.
    3. Bahta, Sirak & Baker, Derek & Malope, Patrick & Katijuongua, Hikuepi, 2015. "A metafronteir analysis of determinants of technical efficiency in beef farm types: an application to Botswana," 2015 Conference, August 9-14, 2015, Milan, Italy 211194, International Association of Agricultural Economists.
    4. Roengchai Tansuchat, 2023. "A Copula-Based Meta-Stochastic Frontier Analysis for Comparing Traditional and HDPE Geomembranes Technology in Sea Salt Farming among Farmers in Phetchaburi, Thailand," Agriculture, MDPI, vol. 13(4), pages 1-23, March.
    5. Otieno, David Jakinda & Hubbard, Lionel J. & Ruto, Eric, 2012. "Determinants of technical efficiency in beef cattle production in Kenya," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 125853, International Association of Agricultural Economists.
    6. Otieno, David Jakinda & Hubbard, Lionel J. & Ruto, Eric, 2011. "Technical efficiency and technology gaps in beef cattle production systems in Kenya: A stochastic metafrontier analysis," 85th Annual Conference, April 18-20, 2011, Warwick University, Coventry, UK 108947, Agricultural Economics Society.
    7. Dhehibi, Boubaker & Lachaal, Lassaad & Elloumi, Mohamed & Messaoud, Emna B., 2007. "Measurement and Sources of Technical Inefficiency in the Tunisian Citrus Growing Sector," 103rd Seminar, April 23-25, 2007, Barcelona, Spain 9391, European Association of Agricultural Economists.
    8. MAIMOUNA DIAKITE & Jean-François BRUN, 2016. "Tax Potential and Tax Effort: An Empirical Estimation for Non-Resource Tax Revenue and VAT’s Revenue," EcoMod2016 9537, EcoMod.
    9. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.
    10. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    11. Gralka, Sabine, 2018. "Stochastic frontier analysis in higher education: A systematic review," CEPIE Working Papers 05/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    12. Young Hoon Lee, 2009. "Frontier Models and their Application to the Sports Industry," Working Papers 0903, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised 2009.
    13. Tanko, Mohammed & Ismaila, Salifu, 2021. "How culture and religion influence the agriculture technology gap in Northern Ghana," World Development Perspectives, Elsevier, vol. 22(C).
    14. Manlagnit, Maria Chelo V., 2015. "Basel regulations and banks’ efficiency: The case of the Philippines," Journal of Asian Economics, Elsevier, vol. 39(C), pages 72-85.
    15. Belotti, Federico & Ilardi, Giuseppe, 2018. "Consistent inference in fixed-effects stochastic frontier models," Journal of Econometrics, Elsevier, vol. 202(2), pages 161-177.
    16. Gian Carlo Scarsi, 1999. "Local Electricity Distribution in Italy: Comparative Efficiency Analysis and Methodological Cross-Checking," Working Papers 1999.16, Fondazione Eni Enrico Mattei.
    17. Cazals Catherine & Dudley Paul & Florens Jean-Pierre & Jones Michael, 2011. "The Effect of Unobserved Heterogeneity in Stochastic Frontier Estimation: Comparison of Cross Section and Panel with Simulated Data for the Postal Sector," Review of Network Economics, De Gruyter, vol. 10(3), pages 1-22, September.
    18. Guy Nkamleu & Joachim Nyemeck & Jim Gockowsk, 2010. "Working Paper 104 - Technology Gap and Efficiency in Cocoa Production in West and Central Africa: Implications for Cocoa Sector Development," Working Paper Series 241, African Development Bank.
    19. Phu Nguyen-Van & The Nguyen To, 2014. "Agricultural extension and technical efficiency of tea production in northeastern Vietnam," Working Papers hal-01725580, HAL.
    20. Althaler, Karl S. & Slavova, Tatjana, 2000. "DEA Problems under Geometrical or Probability Uncertainties of Sample Data," Economics Series 89, Institute for Advanced Studies.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jecstr:v:5:y:2016:i:1:d:10.1186_s40008-016-0044-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.