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ETKİNLİĞİN ÖLÇÜMÜNDE BAYEZGİL YAKLAŞIM: DİNAMİK STOKASTİK SINIR MODELİ BULGULARI

Year 2020, Volume: 16 Issue: 2, 389 - 412, 31.12.2020

Abstract

Literatürde yapılan etkinlik analizlerinde genellikle üretim sürecinde kullanılan girdilerin gecikmeli uyarlama süreçleri dikkate alınmamaktadır. Dinamik uyarlama sürecini gözardı eden bu yaklaşımlarla tahmin edilen etkinlik skorları bu nedenle sapmalı olabilmektedir. Bu çalışmada 2005 – 2017 dönemi için Türkiye imalat sanayinde faaliyet gösteren 106 firmanın maliyet etkinlik düzeyleri dinamik stokastik sınır analizi ile tahmin edilmiştir. Elde edilen sonuçlara göre, kalıcılık parametresinin değeri bire yakın ve oldukça yüksektir. Buna göre firmaların uyarlama maliyetlerinden kaynaklanan etkinsizlikteki kalıcılığın yüksek olduğu söylenebilir. Firmaların dinamik etkinlik skorları ortalamasının statik etkinlik skorları ortalamasından düşük olması bu sonucu destekleyen diğer bir bulgudur.

References

  • Ahn, S. C., Good, D. H., Sickles, R. C. (2000). Estimation of Long-Run Inefficiency Levels: A Dynamic Frontier Approach. Econometric Reviews, 19(4), 461–492
  • Ahn, S. C., Good, D. H., Sickles, R. C. (1998). The relative efficiency and rate of technology adoption of Asian and North
  • American Airline Firms. In: Fu, T.T., Huang, C. J., Lovell, C.A.K (eds). Economic efficiency and productivity growth in the Asia Pacific Region. Edward Elgar Publishing Limited, Cheltenham, pp 65–89
  • Aigner, D. J., Lovell, C. A. K., Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Function Models. Journal of Econometrics, 6, 21–37
  • Akan, Y., & Çalmaşur, G. (2011). Etkinliğin Hesaplanmasında Veri Zarflama Analizi ve Stokastik Sınır Yaklaşımı Yöntemlerinin Karşılaştırılması (Tra1 Alt Bölgesi Üzerine Bir Uygulama), Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 25 (0), 13-32
  • Asche, F., Kumbhakar, S. C., Tveteras, R. (2008). A Dynamic Profit Function with Adjustment Costs for Outputs. Empirical Econ, 35, 379–393.
  • Atan, M., Yaşar, Z. R., Unvan, Ö., Uzun, C. B. (2009). Türkiye’de İktisadi Faaliyet Kollarında Verimlilik ve Etkinliğin Üretim Fonksiyonları ile İncelenmesi (2004-2006). Ekonomik Yaklaşım, 20 (72), 43-58
  • Ayed-Mouelhi, R.B., & Goaied, M. (2003). Efficiency Measure from Dynamic Stochastic Production Frontier: Application to Tunisian Textile, Clothing, and Leather İndustries. Journal of Econometrics Reviews, 22(1), 93–111
  • Avcı, T., & Çağlar, A. (2016). Stokastik Sınır Analizi: İstanbul Sanayi Odası’na Kayıtlı Firmalara Yönelik Bir Uygulama. Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi, 4(2), 17-57
  • Battese, G., & Coelli, T. (1992). Frontier Production, Function, 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
  • Battese, G., & G. Corra (1977). Estimation of a Production Frontier Model: With Application to the Pastoral Zone of Eastern Australia. Australian Journal of Agricultural Economics, 21, 169–179
  • Berger, A.N., & Mester, L. J. (1997). Inside the Black Box: What Explains Differences in the Efficiencies of Financial Institutions. Journal of Banking and Finance, 21, 895-947
  • Bilik, M., Aydın, Ü., Kahyaoğlu, H. (2016). Türkiye Gıda Sanayinde Kısa ve Uzun Dönemli Etkinlik: Stokastik Sınır Analizi. Çankırı Karatekin Üniveristesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(2), 67-84
  • Coelli, T., D. S., Prasada, R., Battese, G. E. (1997). An Introduction to Efficiency and Productivity Analysis. Boston: Springer.
  • Cornwell, C., Schmidt, P., Sickles, R. C. (1990). Production Frontiers with Cross-Sectional and Time-Series Variations in Efficiency Levels. Journal of Econometrics, 46, 185–200
  • 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 İmalat Sanayiinde Etkinlik ve Toplam Faktör Verimliliği Analizi. ODTÜ Gelişme Dergisi, 29, 247–284
  • Deliktaş, E. (2006). İzmir Küçük, Orta ve Büyük Ölçekli İmalat Sanayinde Üretim Etkinliği ve Toplam Faktör Verimliliği Analizi. Ege University Working Papers in Economics, Sayı 06/03, İzmir.
  • Desli, E., Ray, S. C., Kumbhakar, S. C. (2003). A Dynamic Stochastic Frontier Production Model With Time-Varying Efficiency. Applied Economics Letters, 10, 623-626
  • Düzakın, E., Düzakın, H. (2007), Measuring the Performance of Manufacturing Firms with Super Slacks Based Model of Data Envelopment Analysis: An Application of 500 Major Industrial Enterprises in Turkey. European Journal of Operational Research, 182, 1412-1432
  • Emvalomatis, G., Stefanou, S., & Lansink, A. (2011). A Reduced-Form Model for Dynamic Efficiency Measurement: Application to Dairy Farms in Germany and the Netherlands. American Journal of Agricultural Economics, 93, 131–174
  • Emrouznejad, A. & Thanassoulis, E. (2005). A Mathematical Model for Dynamic Efficiency Using Data Envelopment Analysis. Applied Mathematics and Computation, 160, 363–378
  • Galán, J. E., Veiga, H., Wiper, M.P. (2015). Dynamic effects in inefficiency: Evidence from the Colombian banking sector. European Journal of Operational Research, 240 (2), 562-571
  • Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-Based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association, 85, 398–409.
  • Jondrow, J., Lovell, C., Materov, I. S., Schmidt, P. (1982). Technical Inefficiency in the Stochastic Frontier Production Function Model. Journal of Econometrics, 19, 233–238
  • Hajihassaniasl, S., & Kök, R. (2016). Scale Effect in Turkish Manufacturing Industry: Stochastic Metafrontier Analysis. Journal of Economic Structures, 5(13): 1-17
  • Hamermesh, D. S., & Pfann, G. A. (1996). Adjustment Costs in Factor Demand. Journal of Economic Literature, 34(3), 1264–1292
  • Hultberg, P. T., Nadiri, M. I., Sickles, R. C. (1999). An International Comparison of Technology Adoption and Efficiency: A Dynamic Panel Model. Annals of Economics and Statistics, (55/56), 449–474
  • Kodde, D., & Palm, F. C. (1986). Wald Criteria for Jointly Testing Equality and Inequality Restrictions. Econometrica, 54(5), 1243–1248
  • Kök, R., & Yeşilyurt, M. E. (2006). İlk Beş Yüz İmalat Sanayi Kuruluşunun Etkinlik Analizi ve Sigma Yakınsaması-Türkiye Örneği: 1993- 2000. İktisat İşletme ve Finans, 249, 46-60
  • Kumbhakar, S. (1990). Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency. Journal of Econometrics, 46, 201–211
  • Kumbhakar, S., Ghosh, S., McGuckin, T. (1991). A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms. Journal of Business and Economic Statistics, 9(3), 279–286
  • Kumbhakar, S. C., Heshmati, A., Hjalmarsson, L. (2002). How Fast Do Banks Adjusts? A Dynamic Model of Labor Use with an Application to Swedish Banks. Journal of Productivity Analysis, 18, 79–102
  • Kumbhakar, S. C., Lien, G., Hardaker, J. B. (2014). Technical Efficiency in Competing Panel Data Models: A Study of Norwegian Grain Farming. Journal of Productivity Analysis, 41(2), 321–337
  • Lang, G., & Welzel, P. (1996). Efficiency and Technical Progress in Banking: Empirical Results for a Panel of German Cooperative Banks. Journal of Banking & Finance, 20, 1003–23
  • Lucas, R. E. (1967b). Adjustment Costs and the Theory of Supply. Journal of Political Economy, 75 (4), 321–334
  • Meeusen, W., & Van den Broek, J. (1977). Efficiency Estimation from Cobb- Douglas Production Functions with Composite Errors. International Economic Review, 18, 435–44
  • Mayes, D., Harris, C., Lansbury, M. (1994). Inefficiency in Industry. Harvester Wheatsheaf, New York, London
  • Nemoto, J. ve Goto, M. (2003). Measuring Dynamic Efficiency in Production: An Application of Data Envelopment Analysis to Japanese Electric Utilities. Journal of Productivity Analysis. 19, 191–210.Sengupta, 1995: 127-128
  • Ouellette, P. ve Yan, L. (2008). Investment and Dynamic DEA. Journal of Productivity Analysis, 29: 235–247
  • 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
  • Ramanathan, T. V., Rohan, N., Bovas, A. (2015). A Stochastic Frontier Regression Model with Dynamic Frontier, Communications in Statistics - Simulation and Computation
  • Sala-i-Martin, X. X. (1996). Regional Cohesion: Evidence and Theories of Regional Growth and Convergence. European Economic Review, 40, 1325–1352.
  • Sengupta, J.K. (1995). Dynamics of Data Envelopment Analysis: Theory of Systems Efficiency. Dordrecht: Kluwer Academic Publishers.
  • Schmidt, P, & Sickles, R. C. (1984). Production Frontiers and Panel Data. Journal of Business & Economic Statistics, 2(4), 367–374
  • Tanner, M. A., & Wong, W. H. (1987). The Calculation of Posterior Distributions by Data Augmentation (with discussion). Journal of the American Statistical Association, 82, 528–550
  • Taymaz, E., & G. Saatçi. (1997). Technical Change and Efficiency in Turkish Manufacturing Industries. Journal of Productivity Analysis, 8(4), 461–475
  • Treadway, A. B. (1971). The Rational Multivariate Flexible Accelerator. Econometrica, 35(5), 845–856
  • Tsionas, E. (2006). Inference in Dynamic Stochastic Frontier Models. Journal of Applied Econometrics, 21, 669–676

BAYESIAN APPROACH IN THE MEASUREMENT OF THE EFFICIENCY: DYNAMIC STOCHASTIC FRONTIER MODEL FINDINGS

Year 2020, Volume: 16 Issue: 2, 389 - 412, 31.12.2020

Abstract

In the efficiency analysis conducted in the literature, the lagged adjustment process of inputs used in the production process is usually not taken into account. Therefore, the efficiency scores estimated by these approaches, which ignore the dynamic adjustment costs, can be biased. In this study, the level of cost efficiency for 106 firms operating in Turkey's manufacturing industry over the period 2005 – 2017, is estimated by dynamic stochastic frontier analysis. The results indicate that, the persistence parameter is fairly close to unity. Accordingly, one can say that the persistence in inefficiency caused by the adjustment costs of the firms is high. The fact that the average of dynamic efficiency scores of the firms is lower than the average of static efficiency scores is another finding that supports this result.

References

  • Ahn, S. C., Good, D. H., Sickles, R. C. (2000). Estimation of Long-Run Inefficiency Levels: A Dynamic Frontier Approach. Econometric Reviews, 19(4), 461–492
  • Ahn, S. C., Good, D. H., Sickles, R. C. (1998). The relative efficiency and rate of technology adoption of Asian and North
  • American Airline Firms. In: Fu, T.T., Huang, C. J., Lovell, C.A.K (eds). Economic efficiency and productivity growth in the Asia Pacific Region. Edward Elgar Publishing Limited, Cheltenham, pp 65–89
  • Aigner, D. J., Lovell, C. A. K., Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Function Models. Journal of Econometrics, 6, 21–37
  • Akan, Y., & Çalmaşur, G. (2011). Etkinliğin Hesaplanmasında Veri Zarflama Analizi ve Stokastik Sınır Yaklaşımı Yöntemlerinin Karşılaştırılması (Tra1 Alt Bölgesi Üzerine Bir Uygulama), Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 25 (0), 13-32
  • Asche, F., Kumbhakar, S. C., Tveteras, R. (2008). A Dynamic Profit Function with Adjustment Costs for Outputs. Empirical Econ, 35, 379–393.
  • Atan, M., Yaşar, Z. R., Unvan, Ö., Uzun, C. B. (2009). Türkiye’de İktisadi Faaliyet Kollarında Verimlilik ve Etkinliğin Üretim Fonksiyonları ile İncelenmesi (2004-2006). Ekonomik Yaklaşım, 20 (72), 43-58
  • Ayed-Mouelhi, R.B., & Goaied, M. (2003). Efficiency Measure from Dynamic Stochastic Production Frontier: Application to Tunisian Textile, Clothing, and Leather İndustries. Journal of Econometrics Reviews, 22(1), 93–111
  • Avcı, T., & Çağlar, A. (2016). Stokastik Sınır Analizi: İstanbul Sanayi Odası’na Kayıtlı Firmalara Yönelik Bir Uygulama. Siyaset, Ekonomi ve Yönetim Araştırmaları Dergisi, 4(2), 17-57
  • Battese, G., & Coelli, T. (1992). Frontier Production, Function, 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
  • Battese, G., & G. Corra (1977). Estimation of a Production Frontier Model: With Application to the Pastoral Zone of Eastern Australia. Australian Journal of Agricultural Economics, 21, 169–179
  • Berger, A.N., & Mester, L. J. (1997). Inside the Black Box: What Explains Differences in the Efficiencies of Financial Institutions. Journal of Banking and Finance, 21, 895-947
  • Bilik, M., Aydın, Ü., Kahyaoğlu, H. (2016). Türkiye Gıda Sanayinde Kısa ve Uzun Dönemli Etkinlik: Stokastik Sınır Analizi. Çankırı Karatekin Üniveristesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 6(2), 67-84
  • Coelli, T., D. S., Prasada, R., Battese, G. E. (1997). An Introduction to Efficiency and Productivity Analysis. Boston: Springer.
  • Cornwell, C., Schmidt, P., Sickles, R. C. (1990). Production Frontiers with Cross-Sectional and Time-Series Variations in Efficiency Levels. Journal of Econometrics, 46, 185–200
  • 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 İmalat Sanayiinde Etkinlik ve Toplam Faktör Verimliliği Analizi. ODTÜ Gelişme Dergisi, 29, 247–284
  • Deliktaş, E. (2006). İzmir Küçük, Orta ve Büyük Ölçekli İmalat Sanayinde Üretim Etkinliği ve Toplam Faktör Verimliliği Analizi. Ege University Working Papers in Economics, Sayı 06/03, İzmir.
  • Desli, E., Ray, S. C., Kumbhakar, S. C. (2003). A Dynamic Stochastic Frontier Production Model With Time-Varying Efficiency. Applied Economics Letters, 10, 623-626
  • Düzakın, E., Düzakın, H. (2007), Measuring the Performance of Manufacturing Firms with Super Slacks Based Model of Data Envelopment Analysis: An Application of 500 Major Industrial Enterprises in Turkey. European Journal of Operational Research, 182, 1412-1432
  • Emvalomatis, G., Stefanou, S., & Lansink, A. (2011). A Reduced-Form Model for Dynamic Efficiency Measurement: Application to Dairy Farms in Germany and the Netherlands. American Journal of Agricultural Economics, 93, 131–174
  • Emrouznejad, A. & Thanassoulis, E. (2005). A Mathematical Model for Dynamic Efficiency Using Data Envelopment Analysis. Applied Mathematics and Computation, 160, 363–378
  • Galán, J. E., Veiga, H., Wiper, M.P. (2015). Dynamic effects in inefficiency: Evidence from the Colombian banking sector. European Journal of Operational Research, 240 (2), 562-571
  • Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-Based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association, 85, 398–409.
  • Jondrow, J., Lovell, C., Materov, I. S., Schmidt, P. (1982). Technical Inefficiency in the Stochastic Frontier Production Function Model. Journal of Econometrics, 19, 233–238
  • Hajihassaniasl, S., & Kök, R. (2016). Scale Effect in Turkish Manufacturing Industry: Stochastic Metafrontier Analysis. Journal of Economic Structures, 5(13): 1-17
  • Hamermesh, D. S., & Pfann, G. A. (1996). Adjustment Costs in Factor Demand. Journal of Economic Literature, 34(3), 1264–1292
  • Hultberg, P. T., Nadiri, M. I., Sickles, R. C. (1999). An International Comparison of Technology Adoption and Efficiency: A Dynamic Panel Model. Annals of Economics and Statistics, (55/56), 449–474
  • Kodde, D., & Palm, F. C. (1986). Wald Criteria for Jointly Testing Equality and Inequality Restrictions. Econometrica, 54(5), 1243–1248
  • Kök, R., & Yeşilyurt, M. E. (2006). İlk Beş Yüz İmalat Sanayi Kuruluşunun Etkinlik Analizi ve Sigma Yakınsaması-Türkiye Örneği: 1993- 2000. İktisat İşletme ve Finans, 249, 46-60
  • Kumbhakar, S. (1990). Production Frontiers, Panel Data, and Time-Varying Technical Inefficiency. Journal of Econometrics, 46, 201–211
  • Kumbhakar, S., Ghosh, S., McGuckin, T. (1991). A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms. Journal of Business and Economic Statistics, 9(3), 279–286
  • Kumbhakar, S. C., Heshmati, A., Hjalmarsson, L. (2002). How Fast Do Banks Adjusts? A Dynamic Model of Labor Use with an Application to Swedish Banks. Journal of Productivity Analysis, 18, 79–102
  • Kumbhakar, S. C., Lien, G., Hardaker, J. B. (2014). Technical Efficiency in Competing Panel Data Models: A Study of Norwegian Grain Farming. Journal of Productivity Analysis, 41(2), 321–337
  • Lang, G., & Welzel, P. (1996). Efficiency and Technical Progress in Banking: Empirical Results for a Panel of German Cooperative Banks. Journal of Banking & Finance, 20, 1003–23
  • Lucas, R. E. (1967b). Adjustment Costs and the Theory of Supply. Journal of Political Economy, 75 (4), 321–334
  • Meeusen, W., & Van den Broek, J. (1977). Efficiency Estimation from Cobb- Douglas Production Functions with Composite Errors. International Economic Review, 18, 435–44
  • Mayes, D., Harris, C., Lansbury, M. (1994). Inefficiency in Industry. Harvester Wheatsheaf, New York, London
  • Nemoto, J. ve Goto, M. (2003). Measuring Dynamic Efficiency in Production: An Application of Data Envelopment Analysis to Japanese Electric Utilities. Journal of Productivity Analysis. 19, 191–210.Sengupta, 1995: 127-128
  • Ouellette, P. ve Yan, L. (2008). Investment and Dynamic DEA. Journal of Productivity Analysis, 29: 235–247
  • 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
  • Ramanathan, T. V., Rohan, N., Bovas, A. (2015). A Stochastic Frontier Regression Model with Dynamic Frontier, Communications in Statistics - Simulation and Computation
  • Sala-i-Martin, X. X. (1996). Regional Cohesion: Evidence and Theories of Regional Growth and Convergence. European Economic Review, 40, 1325–1352.
  • Sengupta, J.K. (1995). Dynamics of Data Envelopment Analysis: Theory of Systems Efficiency. Dordrecht: Kluwer Academic Publishers.
  • Schmidt, P, & Sickles, R. C. (1984). Production Frontiers and Panel Data. Journal of Business & Economic Statistics, 2(4), 367–374
  • Tanner, M. A., & Wong, W. H. (1987). The Calculation of Posterior Distributions by Data Augmentation (with discussion). Journal of the American Statistical Association, 82, 528–550
  • Taymaz, E., & G. Saatçi. (1997). Technical Change and Efficiency in Turkish Manufacturing Industries. Journal of Productivity Analysis, 8(4), 461–475
  • Treadway, A. B. (1971). The Rational Multivariate Flexible Accelerator. Econometrica, 35(5), 845–856
  • Tsionas, E. (2006). Inference in Dynamic Stochastic Frontier Models. Journal of Applied Econometrics, 21, 669–676
There are 50 citations in total.

Details

Primary Language Turkish
Subjects Economics
Journal Section Articles
Authors

Ramazan Ekinci

Publication Date December 31, 2020
Acceptance Date December 10, 2020
Published in Issue Year 2020 Volume: 16 Issue: 2

Cite

APA Ekinci, R. (2020). ETKİNLİĞİN ÖLÇÜMÜNDE BAYEZGİL YAKLAŞIM: DİNAMİK STOKASTİK SINIR MODELİ BULGULARI. Ekonomik Ve Sosyal Araştırmalar Dergisi, 16(2), 389-412.

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Tel: 0 374 254 10 00 / 14 86 Faks: 0 374 253 45 21 E-posta: iibfdergi@ibu.edu.tr

ISSN (Basılı) : 1306-2174 ISSN (Elektronik) : 1306-3553