Gelişmiş ve Gelişmekte Olan Ülkelerde Emek-Sermaye-Enerji Faktörlerinin İkame Esnekliği ve Çıktı Esneklikleri

Abstract

Üretim fonksiyonu üzerine tartışmalar iktisatçıların daima ilgisini çekmiştir. Bu çalışmanın amacı, gelişmiş ve gelişmekte olan ülkelerde, Cobb-Douglas, CES ve VES üretim fonksiyonlarından hareketle, ölçek esnekliği,  çıktı esnekliği ve ikame esnekliğini tahmin etmektir. Amaç doğrultusunda, dört girdili (emek, sermaye, doğalgaz ve petrol) olacak şekilde model oluşturulmuştur. Çalışmada 1982-2014 dönemine ait verilerle 22 gelişmiş, 12 gelişmekte olan ülke için doğrusal ve doğrusal olmayan panel veri analiz tekniklerinden yararlanılmıştır. Cobb-Douglas üretim fonksiyonu’ndan elde edilen bulgular, sermayenin çıktı esnekliğinin gelişmiş ülkelerde, emeğin çıktı esnekliğinin ise gelişmekte olan ülkelerde daha düşük olduğunu göstermektedir. Ayrıca doğalgaz tüketimi çıktı esnekliği, gelişmekte olan ülke grubunda istatistiksel olarak anlamsız iken, petrol tüketimi çıktı esnekliğinin gelişmiş ülkelerde daha yüksek olduğu görülmüştür. CES üretim fonksiyonundan elde edilen bulgulara göre, emek ve sermaye arasındaki ikame esnekliği gelişmiş ülkelerde birden büyük, gelişmekte olan ülkelerde ise birden küçük olarak tahmin edilmiştir.

Keywords

• Üretim Fonksiyonu,İkame Esnekliği,Çıktı Esnekliği,Panel Veri Analizi

Elasticity of Substitution and Output Elasticities of Labor-Capital-Energy Factors in Developed and Developing Countries

Abstract

Discussions on the production function have always attracted the attention of economists. The aim of this study is to predict scale elasticity, output elasticity and elasticity of substitution for developed and developing countries, based on Cobb-Douglas, CES and VES production functions. Model has been created for four inputs (labor, capital, natural gas and oil). In the study, linear and nonlinear panel data analysis techniques were used for 22 developed and 12 developing countries by data for the period 1982-2014. Findings obtained from the Cobb-Douglas production function show that output elasticity of capital is higher in developed countries and output elasticity of capital is lower in developing countries. Moreover, while the output elasticity of natural gas consumption was statistically insignificant in the developing country group, the output elasticity of oil consumption was seen to be higher in developed countries. According to the CES production function, the elasticity of substitution between labor and capital was estimated to be greater than one in developed countries. Similarly, the elasticity of substitution between labor and capital was estimated to be smaller than one in developing countries.

Keywords

• Production Function,Elasticity of Substitution,Output Elasticity,Panel Data Analysis

References

1. Arrow, K. J., Chenery, H. B., Minhas, B. S., and Solow, R. M. (1961). “Capital-labor substitution and economic efficiency”. The Review of Economics and Statistics, 43(3), 225-250.
2. Blundell, R., and Bond, S. (2000). “GMM estimation with persistent panel data: An application to production functions”. Econometric Reviews, 19(3), 321-340.
3. Breusch, T. S., and Pagan, A. R. (1980). “The lagrange multiplier test and its applications to model spesification in econometrics”. The Review of Economic Studies, 47(1), 239-253.
4. Brockway, P. E., Saunders, H., Heun, M. K., Foxon, T. J., Steinberger, J. K., Barrett, J. R., and Sorrell, S. (2017). “Energy rebound as a potential threat to a low-carbon future: Findings from a new energy-based national-level rebound approach”. Energies, 10(1), 1-24.
5. Broyden, C. G. (1970). “The convergence of a class of double-rank minimization algorithms”. Journal of the Institute of Mathematics and Its Applications, 6, 76-90.
6. Byrd, R., Lu, P., Nocedal, J. and Zhu, C. (1995). “A limited memory algorithm for bound constrained optimization”. SIAM Journal for Scientific Computing, 16, 1190-1208.
7. Cantos, P., Gumbau‐Albert, M., and Maudos, J. (2005). “Transport infrastructures, spillover effects and regional growth: Evidence of the Spanish case”. Transport Reviews, 25(1), 25-50.
8. Chikabwi, D., Chidoko, C., and Mudzingiri, C. (2017). “Manufacturing sector productivity growth drivers: Evidence from SADC member states”. African Journal of Science, Technology, Innovation and Development, 9(2), 163-171.

9. Chow, G. C., and Li, K. W. (2002). “China’s economic growth: 1952–2010”. Economic Development and Cultural Change, 51(1), 247-256.
10. Cobb, C. W., and Douglas, P. H. (1928). “A theory of production”. The American Economic Review, 18(1), 139-165.
11. Çermikli, A. H., ve Tokatlıoğlu, İ. (2015). “Yüksek ve orta gelirli ülkelerde teknolojik gelişmenin enerji yoğunluğu üzerindeki etkisi”. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 12(32), 1-22.
12. Duffy, J., and Papageorgiou, C. (2000). “A cross-country empirical investigation of the aggregate production function specification”. Journal of Economic Growth, 5(1), 87-120.
13. Eberhardt, M. and Bond, S. (2009). Cross-section dependence in nonstationary panel models: A novel estimator. Erişim Tarihi: 15 Ağustos 2017, https://mpra.ub.uni-muenchen.de/17692/
14. Fang, Y. (2011). “Economic welfare impacts from renewable energy consumption: The china experience”. Renewable and Sustainable Energy Reviews, 15(9), 5120-5128.
15. Fletcher, R. (1970). “A new approach to variable metric algorithms”. Computer Journal, 13, 317-322.
16. Goldfarb, D. (1970). “A family of variable metric updates derived by variational means”. Mathematics of Computation, 24, 23-26.
17. Henningsen, A. and Henningsen, G. (2011). “Econometric estimation of the “constant elasticity of substitution” function in R: Package micEconCES”. Institute of Food and Resource Economics, Working Paper, No. 2011/9.
18. Inglesi-Lotz, R. (2016). “The impact of renewable energy consumption to economic growth: A panel data application”. Energy Economics, 53, 58-63.
19. Kemfert, C. (1998). “Estimated substitution elasticities of a nested CES production function approach for Germany”. Energy Economics, 20(3), 249-264.
20. Kmenta, J. (1967). “On estimation of the CES production function”. International Economic Review, 8(2): 180-189.
21. Koesler, S. and Schymura, M. (2012). “Substitution elasticities in a CES production framework an empirical analysis on the basis of non-linear least squares estimations”. Centre for European Economic Research, No. 12-007.
22. Leontief, W. (1951). Input-Output Economy. New York: Oxford University Press.
23. Li, K. W. and Liu, T. (2011). “Economic and productivity growth decomposition: An application to post-reform China”. Economic Modelling, 28(1), 366-373.
24. Maddala, G. S. and Kadane, J. B. (1967). “Estimation of returns to scale and the elasticity of substitution”. Econometrica, Journal of the Econometric Society, 35(3/4), 419-423.
25. McCoskey, S. and Kao, C. (1998). “A residual-based test of the null of cointegration in panel data”. Econometric Reviews, 17(1), 57-84.
26. Nelder, J. A. and Mead, R. (1965). “A simplex algorithm for function minimization”. Computer Journal, 7, 308-313.
27. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Erişim Tarihi: 15 Ağustos 2017, http://www.dspace.cam.ac.uk/bitstream/1810/446/1/cwpe0435.pdf
28. Pesaran, H. M. (2007). “A simple panel unit root test in the presence of cross-section dependence”. Journal of Applied Econometrics, 22(2), 265-312.
29. Pesaran, M. H. and Yagamata, T. (2008). “Testing slope homogeneity in large panels”. Journal of Econometrics, 142(1), 50-93.
30. Pesaran, M. H., Ullah, A. and Yamagata, T. (2008). “A bias‐adjusted LM test of error cross‐section independence”. The Econometrics Journal, 11(1), 105-127.
31. Sato, R. (1970). “The estimation of biased technical progress and the production function”. International Economic Review, 11(2), 179-208.
32. Shahiduzzaman, M. and Alam, K. (2014). “Information technology and its changing roles to economic growth and productivity in Australia”. Telecommunications Policy, 38(2), 125-135.
33. Shanno, D. F. (1970). “Conditioning of quasi-Newton methods for function minimization”. Mathematics of Computation, 24, 647-656.
34. Shen, K. and Whalley, J. (2013). “Capital-labor-energy substitution in nested CES production functions for China”. National Bureau of Economic Research. No. w19104.
35. Shen, K., Wang, J. and Whalley, J. (2015). “Measuring changes in the bilateral technology gaps between China, India and the US 1979-2008”. National Bureau of Economic Research. No. w21657.
36. Söderbom, M. and Teal, F. (2004). “Size and efficiency in African manufacturing firms: evidence from firm-level panel data”. Journal of Development Economics, 73(1), 369-394.
37. Swamy, P. A. (1970). “Efficient inference in a random coefficient regression model”. Econometrica, 38(2), 311-323.
38. Tronconi, C. and Marzetti, G. V. (2011). “Organization capital and firm performance. empirical evidence for european firms”. Economics Letters, 112(2), 141-143.
39. Thursby, J. G. and Lovell, C. K. (1978). “An investigation of the Kmenta approximation to the CES function”. International Economic Review, 19(2), 363-377.
40. Wakelin, K. (2001). “Productivity growth and R&D expenditure in UK manufacturing firms”. Research Policy, 30(7), 1079-1090.
41. Westerlund, J. and Edgerton, D. L. (2007). “A panel bootstrap cointegration test”. Economics Letters, 97(3), 185-190.
42. Van der Werf, E. (2008). “Production functions for climate policy modeling: An empirical analysis”. Energy Economics, 30(6), 2964-2979.