TEKNİK ETKİNLİĞİN ÖLÇÜMÜNDE MEKÂNSAL BAĞIMLILIĞIN ETKİSİ: İMALAT SANAYİ İÇİN MEKÂNSAL STOKASTİK SINIR ANALİZİ BULGULARI

Yıl 2020, Cilt: 22 Sayı: 3, 995 - 1021, 29.09.2020

Ramazan Ekinci

https://doi.org/10.16953/deusosbil.733488

Cited By: 3

Öz

Bu çalışmanın amacı 2019 dönemi için farklı bölgelerde kurulu üretim tesisi bulunan 76 imalat sanayi firmasının mekansal bağımlılığı dikkate alarak teknik etkinlik değerlerini tahmin etmektir. Mekansal bağımlılığın ekonomik birimlerin performası üzerinde etkisinin olabileceğine yönelik literatürde genel kabul olmasına rağmen, etkinlik üzerine yapılan çok az çalışma bu etkiye dikkate almaktadır. Bu amaçla çalışmada Fusco ve Vidoli (2013) tarafından önerilen ve henüz güncel bir yaklaşım olan Mekansal Stokastik Sınır Analizi (SSFA) kullanılarak, mekansal bağımlılığın firmaların etkinlik skorları üzerindeki etkisi analiz edilmiştir. Elde edilen sonuçlar teknik etkinlik skorları arasında mekansal otokorelasyonun olduğunu göstermektedir. Mekansal bağımlılık altında tahmin edilen teknik etkinlik skorları ortalaması 0.444’tür. Dolayısıyla, teknik etkinliğin iyileştirilmesinde firmalar için geniş bir çıktı boşluğu bulunmaktadır. Ayrıca, etkinsizliğin belirleyenleri içerisinde mekansal bağımlılıktan kaynaklanan etkinin sınırlı kaldığı sonucuna ulaşılmıştır. Buna göre, etkinsizlik farklılıklarının mekansal etkilerden daha çok firmaların bireysel özelliklerinden kaynaklandığı söylenebilir. Öte yandan, mekansal bağımlılığın sonucu olarak firmaların teknik etkinlik skorlarının birbirine yakınsadığı görülmektedir.

Anahtar Kelimeler

Mekansal Stokastik Sınır Analizi, Mekansal Bağımlılık, Teknik Etkinlik, Heterojenlik

THE EFFECT OF SPATIAL DEPENDENCE ON MEASUREMENT OF TECHNICAL EFFICIENCY: SPATIAL STOCHASTIC FRONTIER ANALYSIS FINDINGS FOR MANUFACTURING INDUSTRY

Öz

The aim of this study is to estimate the technical efficiency of 76 manufacturing firms with production plants installed in different regions, taking into account the spatial dependency for the 2019 period. Despite the general acceptance in the literature that spatial dependence may have an impact on the performance of economic units, only a few studies on efficiency consider this effect. For this purpose, the effect of spatial dependence on firms' efficiency scores is analyzed by using Spatial Stochastic Frontier Analysis (SSFA), which is a current approach proposed by Fusco and Vidoli (2013). The results show that there is a spatial autocorrelation between the technical efficiency scores. Also, the average technical efficiency scores, estimated under spatial dependency, is 0.444. This means that, there is a large output gap for firms in improving technical efficiency. Besides, it is concluded that the impact resulting from spatial dependence is limited among the determinants of inefficiency. According to this, it can be said that differences in inefficiency arise from the individual characteristics of firms rather than spatial effects. On the other hand, as a result of spatial dependency, it has been concluded that the technical efficiency scores of firm converge.

Anahtar Kelimeler

Spatial Stochastic Frontier Analysis, Spatial Dependence, Technical Efficiency, Heterogeneity

Kaynakça

• Acemoglu, D. (2009). Introduction to modern economic growth. Princeton University Press, Oxford.
• Adetutu, M., Glass, A.J., Kenjegalieva, K. & Sickles, R.C. (2015). The effects of efficiency and TFP growth on pollution in Europe: a multistage spatial analysis. Journal of Productivity Analysis, 43 (3), 307–326.
• Aigner, D., Lovell, C. & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6 , 21-37.
• Affuso, E. (2010). Spatial autoregressive stochastic frontier analysis: an application to an impact evaluation study. Auburn University, Working Papers.
• Anselin, L. (1988). Spatial econometrics: Methods and models. Kluwer Academic Publishers, Dordrecht, The Netherlands.
• Anselin, L. (2010). Thirty years of spatial econometrics. Papers in Regional Science, 89 (1), 3-25.
• Areal, F. J., Balcombe, K. & Tiffin, R. (2012). Integrating spatial dependence into stochastic frontier analysis. Australian Journal of Agricultural and Resource Economics, 56 (4), 521-541,
• Baltagi, B. H. (2011). Spatial panels. A. Ullah. & D.E.A. Giles (Der.), Handbook of Empirical Economics and Finance içinde. Chapman & Hall/CRC, Florida.
• Baltagi, B. H. (2013). Econometric analysis of panel data. Wiley, Chichester.
• Barrios, E. B. & Lavado, R. F. (2010). Spatial Stochastic Frontier Models. Discussion Paper Series, No. 2010-08, Philippine Institute for Development Studies, Philippines.
• Battese, G. E. & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. Journal of Productivity Analysis, 3, 153–169.
• Battese, G. E. & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20, 325–332.
• Besag, J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, 36, 192–236.
• Brehm, S. (2013). Fiscal incentives, public spending, and productivity – County-level evidence from a Chinese province. World Development, 46, 92-103.
• Carvalho, A. (2018). Efficiency spillovers in bayesian stochastic frontier models: Application to electricity distribution in New Zealand. Spatial Economic Analysis, 13 (2) , 171-190.
• Caves, R. E. (1989). Mergers, takeovers, and economic efficiency: Foresight vs. hindsight. International Journal of Industrial Organization, 7 (1), 151-174.
• Cliff, A. D. & Ord, J. K. (1973). Spatial autocorrelation - monographs in spatial and environmental systems analysis, London: Pion Ltd.
• Cobb, C. W. & Douglas, P. H. (1928). A theory of production. The American Economic Review, 18 (1), 139-165.
• Coelli, T. J., Rao, D. S. P. & Battese, G. E. (1998). An introduction to efficiency and productivity analysis. Kluwer Academic Publishers, Boston.
• Coelli, T. (1995). Estimators and hypothesis tests for a stochastic frontier function: a Monte Carlo analysis. Journal of Productivity Analysis, 6, 247- 268.
• Crespo-Cuaresma, J., Doppelhofer, G. & Feldkircher, M. (2014). The determinants of economic growth in European regions. Regional Studies, 48 (1), 44-67.
• Çokgezen, M. & Balcılar, M. (2003). Comparative technical efficiencies of state and privately owned sugar plants in Turkey. Manas Universitesi Sosyal Bilimler Dergisi, 4 (8), 167-179.
• Deliktaş, E. (2002). Türkiye özel sektör imalat sanayiinde etkinlik ve toplam faktör verimliliği analizi. ODTÜ Gelişme Dergisi, 29, 247–284.
• Deliktaş, E. (2006). İzmir küçük, orta ve büyük ölçekli imalat sanayinde üretim etkinliği ve toplam faktör verimliliği analizi. Ege University Working Papers in Economics, 6 (3), İzmir.
• Druska, V. & Horrace, W. (2004). Generalized moments estimation for spatial panel data: Indonesian rice farming. American Journal of Agricultural Economics, 86, 185–198.
• Elhorst, J. P. (2009). Spatial panel data models. M. M. Fischer. & A. Getis. (Der.), Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications içinde. Springer-Verlag, Berlin.
• Elhorst, J. P. (2014). Spatial econometrics: from cross-sectional data to spatial panels. Springer, Heidelberg.
• Färe, R., Grosskopf, S., Norris, M. & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84: 66–83.
• Fusco, E. & Vidoli, F. (2013). Spatial stochastic frontier models: controlling spatial global and local heterogeneity. International Review of Applied Economics, 27 (5), 679-694.
• Glass, A., Kenjegalieva, K. & Paez-Farrell, J. (2013). Productivity growth decomposition using a spatial autoregressive frontier model. Economics Letters, 119, 291–295.
• Glass, A. J., Kenjegalieva, K. & Sickles, R. C. (2014). Estimating efficiency spillovers with state level evidence for manufacturing in the US. Economics Letters, 123 (2), 154–159.
• Glass, A. J., Kenjegalieva, K. & Sickles, R. C. (2016). A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers. Journal of Econometrics, 190, 289– 300.
• Greene, W. H. (2005). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Productivity Analysis, 126 (2), 269-303.
• Hadley, D. (2006). Patterns in technical efficiency and technical change at the farm-level in England and Wales, 1982–2002. Journal of Agricultural Economics, 57, 81–100.
• Hajihassaniasl, S. & Kök, R. (2016). Scale effect in Turkish manufacturing industry: stochastic metafrontier analysis. Journal of Economic Structures, 5, 1-17.
• Han, J., Ryu, D. & Sickles, R. C. (2016). Spillover effects of public capital stock using spatial frontier analyses: A first look at the data. W. H. Greene., L. Khalaf., R. C. Sickles., M. Veall. & M. C. Voia. (Der.), Productivity and Efficiency Analysis. Springer içinde (ss. 83-97). Switzerland: Springer Proceedings in Business and Economics.
• Herwartz, H., & Strumann, C. (2012). On the effect of prospective payment on local hospital competition in Germany. Health Care Management Science, 15 (1), 48-62.
• Hollingsworth, B. (2012). Revolution, evolution, or status quo? Guidelines for efficiency measurement in health care. Journal of Productivity Analysis, 37 (1), 1-5.
• Kumbhaker, S. C., Lovell, C. A. K. (2000). Stochastic frontier analysis. UK: Cambridge University Press, Cambridge.
• Kumar, S. & Russell, R. (2002). Technological change, technological catch-up, and capital deepening: relative contributions to growth and convergence. American Economic Review, 92, 527–548.
• Kök, R. & Yeşilyurt, M. E. (2006). İlk beş yüz imalat sanayi kuruluşunun etkinlik analizi ve sigma yakınsaması-Türkiye örneği: 1993- 2000. İktisat İşletme ve Finans, 249, 46-60
• LeSage, J. P. (1999). The Theory and Practice of Spatial Econometrics. http://www.spatial-econometrics.com/html/sbook.pdf (Erişim Tarihi: 11.04.2020).
• Lovell, C.A.K. (1993). Production frontiers and productive efficiency. H. O. Fried., C. A. K. Lovell. & S. S. Schmidt (Der.), The Measurement of Productive Efficiency içinde (ss. 3-67). New York: Oxford University Press.
• Mastromarco, C., Serlenga, L. & Shin, Y. (2016). Modelling technical efficiency in cross sectionally dependent stochastic frontier panels. Journal of Applied Econometrics, 31, 281–297.
• Mead, R. (1967). A mathematical model for the estimation of inter-plant competition, Boimetrics, 23, 189–205.
• Meeusen, W. & Van den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production function with composed errors. International Economic Review, 18 (2), 435-444.
• Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37, 17-23.
• Mutter, R. L., Rosko, M. D., Greene, W. H. & Wilson, P. W. (2011). Translating frontiers into practice: Taking the next steps toward improving hospital efficiency. Medical Care Research and Review, 68 (1), 3S-19S.
• Taymaz, E. & G. Saatçi. (1997). Technical change and efficiency in Turkish manufacturing industries. Journal of Productivity Analysis, 8 (4), 461–475.
• Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234-240.
• Tsukamoto, T. (2019). A spatial autoregressive stochastic frontier model for panel data incorporating a model of technical inefficiency, Japan & The World Economy, 50, 66-77.
• Pavlyuk, D. (2011). Application of the spatial stochastic frontier model for analysis of a regional tourism sector. Transport and Telecommunication, 12 (2), 28–38.
• Pavlyuk, D. (2013). Distinguishing between spatial heterogeneity and inefficiency: Spatial stochastic frontier analysis of European airports. Transport and Telecommunication, 14 (1), 29– 38.
• Pede, V. O., Areal, F. J., Singbo, A., McKinley, J. & Kajisa, K. (2018). Spatial dependency and technical efficiency: An application of a Bayesian stochastic frontier model to irrigated and rainfed rice farmers in Bohol, Philippines. Agricultural Economics, 49 (3), 301-312.
• Ord, K. (1975). Methods for models of spatial interaction. Journal of the American Statistical Association, 70, 120–126.
• Önder, A. Ö., Deliktaş, E. & Lenger, A. (2003). Efficiency in the manufacturing industry of selected provinces in Turkey: A stochastic frontier analysis. Emerging Markets Finance and Trade, 39 (2), 98–113.
• Ramajo, J. & Hewings, G.J. (2018). Modelling regional productivity performance across Western Europe. Regional. Studies, 52 (10), 1372–1387.
• Rosko, M. D. & Mutter, R. L. (2011). What have we learned from the application of stochastic frontier analysis to U.S. hospitals? Medical Care Research and Review, 68, 75S-100S.
• Schmidt, A. M., Moreira, A. R. B., Helfand, S. M. & Fonseca, T. C. O. (2009). Spatial stochastic frontiers: accounting for unobserved local determinants of inefficiency. Journal of Productivity Analysis, 31, 101–112.
• Schmidt, P. & Sickles, R. C. (1984). Production frontiers and panel data. Journal of Business and Economic Statistics, 2, 367-374.
• Tsionas, E. G. & Michaelides, P. G. (2016). A spatial stochastic frontier model with spillovers: Evidence for italian regions. Scottish Journal of Political Economy, 63 (3), 243-257.
• Varian, H. R. (1992). Microeconomic analysis. 3rd edition. New York: W. W. Norton & Company Inc
• Vidoli, F., Cardillo, C., Fusco, E. & Canello, J. (2016). Spatial nonstationarity in the stochastic frontier model: An application to the Italian wine industry. Regional Science and Urban Economics, 61, 153-164.
• Wang, H.J. & Schmidt, P. (2002). One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. Journal of Productivity Analysis, 18, 129–144.
• Zhu, X., Karagiannis, G. & Lansink, A. O. (2011). The impact of direct income transfers of cap on Greek olive farms performance: using a non-monotonic inefficiency effects model. Journal of Agricultural Economics, 62 (3), 630-638.