ESTIMATION OF COBB – DOUGLAS PRODUCTION FUNCTION FOR DEVELOPING COUNTRIES

Year 2021, Volume: 6 Issue: 1, 54 - 68, 21.06.2021

Hülya Başeğmez

Abstract

ESTIMATION OF COBB – DOUGLAS PRODUCTION FUNCTION FOR DEVELOPING COUNTRIES

Abstract

Discussions on the production function have always taken care of the attention of economists. The production function is a mathematical expression that shows the relationship between inputs and outputs. The characteristics of this relationship can be expressed in three different concepts, scale flexibility, output flexibility and substitution flexibility, respectively. Gross Domestic Product (GDP) is an indicator of economic growth. This study aims to estimate the Cobb - Douglas production function in developing countries by using capital, labor and energy consumption input factors and investigate the effect of economic input factors on economic growth. For this purpose, the Cobb - Douglas production model was created using capital, labor and energy consumption inputs. In this study, linear panel data analysis techniques were used for 22 developing countries with the data of the 1980-2016 period. Substitution elasticity of capital, labor and energy consumption inputs in Cobb - Douglas production function is 0.602, 0.455, 0.147, respectively, which means that the economies of developing countries are capital intensive. The total share of all production factors is 1.203 and there is an increasing return to scale. Capital, labor and energy consumption inputs of these economies have a positive impact on GDP. In addition, insufficient capital in these countries can be compensated by labor and/or energy.

Keywords

Production function, Panel Data Analysis, Economic Growth, Developing countries

References

• Barro, Robert J., and Jong Wha Lee. 2013. “A New Data Set of Educational Attainment in the World, 1950-2010.” Journal of Development Economics 104:184–98.
• Behera, Jaganath, and Alok Kumar Mishra. 2020. “Renewable and Non-Renewable Energy Consumption and Economic Growth in G7 Countries: Evidence from Panel Autoregressive Distributed Lag (P-ARDL) Model.” International Economics and Economic Policy 17(1):241–58. doi: 10.1007/s10368-019-00446-1.
• Berndt, Ernst R., and Laurits R. Christensen. 1973. “The Internal Structure of Functional Relationships: Separability, Substitution, and Aggregation.” The Review of Economic Studies 40(3):410.
• Besanko, David A., and Ronald R. Braeutigam. 2010. Microeconomics. 4 th. John Wiley & Sons, Inc.
• Bhargava, A., L. Franzini, and W. Narendranathan. 1982. “Serial Correlation and the Fixed Effects Model.” The Review of Economic Studies 49(4):533–49.
• Brockway, Paul E., Harry Saunders, Matthew K. Heun, Timothy J. Foxon, Julia K. Steinberger, John R. Barrett, and Steve Sorrell. 2017. “Energy Rebound as a Potential Threat to a Low-Carbon Future: Findings from a New Exergy-Based National-Level Rebound Approach.” Energies 10(51):1–24. doi: 10.3390/en10010051.
• Bronfenbrenner, M., and Paul H. Douglas. 1939. “Cross-Section Studies in the Cobb-Douglas Function.” Journal of Political Economy 47(6):761–85.
• Cameron, T. A., and S. L. Schwartz. 1980. “Inflationary Expectations and the Demand for Capital, Labor, and Energy in Canadian Manufacturing Industries.” Pp. 50–64 in Energy Policy Modeling: United States and Canadian Experiences. Springer Netherlands.
• Cantos, P., M. Gumbau‐Albert, and J. Maudos. 2005. “Transport Infrastructures, Spillover Effects and Regional Growth: Evidence of the Spanish Case.” Transport Reviews 25(1):25–50.
• Çermikli, A., and İ. Tokatlıoğlu. 2015. “The Effects of Technological Growth on Energy Intensity in High and Middle Income Countries.” Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 12(32):1–22.
• Cheng, Maolin, and Yun Han. 2017. “Application of a New Superposition CES Production Function Model.” Journal of Systems Science and Information 5(5):462–72. doi: 10.21078/JSSI-2017-462-11.
• Chikabwi, Douglas, Clainos Chidoko, and Calvin Mudzingiri. 2017. “Manufacturing Sector Productivity Growth Drivers: Evidence from SADC Member States.” Technology, Innovation and Development 9(2):163–71. doi: 10.1080/20421338.2017.1299343.
• Chow, Gregory C. 1960. “Tests of Equality Between Sets of Coefficients in Two Linear Regressions.” Econometrica 28(3):591–605.
• Cobb, Charles W., and Paul H. Douglas. 1928. “A Theory of Production.” American Economic Review 70(3):139–65.
• Cohen, Daniel, and Marcelo Soto. 2007. “Growth and Human Capital: Good Data, Good Results.” J Econ Growth 12:51–76. doi: 10.1007/s10887-007-9011-5.
• Daly, P., and P. H. Douglas. 1943. “The Production Function for Canadian Manufactures.” Journal of the American Statistical Association 38(222):178–86.
• Daly, P., E. Olson, and P. H. Douglas. 1943. “The Production Function for Manufacturing in the United States.” Journal of Political Economy 51(1):61–65.
• Driscoll, John C., and Aart C. Kraay. 1998. “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data.” Review of Economics and Statistics 80(4):549–59.
• Eğilmez, Mahfi. 2016. Mikroekonomi: Güncel Örneklerle. 2nd ed. İstanbul: Remzi Kitapevi.
• Ferguson, C. E., and Ralph W. Pfouts. 1962. “Aggregate Production Functions and Relative Factor Shares.” International Economic Review 3(3):337.
• Field, Barry C., and Charles Grebenstein. 1980. “Capital-Energy Substitution in U.S. Manufacturing.” The Review of Economics and Statistics 62(2):207–12.
• Frees, Edward W. 2004. Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Cambridge University Press.
• Friedman, Milton. 1937. “The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance.” Journal of the American Statistical Association 32(200):675–701.
• Greene, William H. 2018. Econometric Analysis. 8th. Pearson India.
• Gunn, G. T., and P. H. Douglas. 1941. “The Production Function for American Manufacturing for 1919.” The American Economic Review 31(1):67–80.
• Gunn, G. T., and P. H. Douglas. 1942. “The Production Function for American Manufacturing for 1914.” Journal of Political Economy 50(4):595–602.
• Hausman, J. A. 1978. “Specification Tests in Econometrics.” Econometrica 46(6):1251–71.
• Hoechle, Daniel. 2007. “Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence.” Stata Journal 7(3):281–312. doi: 10.1177/1536867x0700700301.
• Inglesi-Lotz, Roula. 2016. “The Impact of Renewable Energy Consumption to Economic Growth: A Panel Data Application.” Energy Economics 53:58–63. doi: 10.1016/j.eneco.2015.01.003.
• Liao, Qian, Zhibin Wu, and Jiuping Xu. 2010. “A New Production Function with Technological Innovation Factor and Its Application to the Analysis of Energy-Saving Effect in LSD.” UK World Journal of Modelling and Simulation 1(4):257–66.
• Mishra, Sudhanshu K. 2010. “A Brief History of Production Functions.” The IUP Journal of Managerial Economics 8(4):6–34. doi: 10.2139/ssrn.1020577.
• Onalan, Omer, and Hulya Basegmez. 2018. “Estimation of Economic Growth Using Grey Cobb-Douglas Production Function: An Application for US Economy.” Journal of Business, Economics and Finance 7(2):178–90.
• Pesaran, M. Hashem. 2004. General Diagnostic Tests for Cross-Sectional Dependence in Panels. 0435.
• Songur, M., and F. Saraç Elmas. 2017. “A General Evaluation on Estimates of Cobb-Douglas, CES, VES and Translog Production Functions.” Bulletin of Economic Theory and Analysis 2(3):235–78.
• Thompson, Henry. 2016. “A Physical Production Function for the US Economy.” Energy Economics 56:185–89. doi: 10.1016/j.eneco.2016.03.016.
• Wakelin, Katharine. 2001. “Productivity Growth and R&D Expenditure in UK Manufacturing Firms.” Research Policy 30(7):1079–90. doi: 10.1016/S0048-7333(00)00136-0.
• Yerdelen Tatoğlu, Ferda. 2018. Panel Veri Ekonometrisi. 4th ed. İstanbul: Beta Yayınevi.