Measurement of Economics to Scale in Corporates of Tehran Stock Exchange

Document Type : Research Paper


1 Department of Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

2 Faculty of Social Sciences, Imam Khomeini International University, Qazvin, Iran



One of the most important effective factors in the economic growth is the increased efficiency of manufacturing sectors. Thus, it is necessary to review and measure the efficiency of business units from a variety of dimensions to plan the increase of efficiency in future courses. One of these dimensions is the economics to scale (ES) referring to the concept of enhanced earnings due to the increased manufacturing unit, which may be of an increasing, fixed or decreasing trend. ES can be measured through economic production functions. This paper has applied Translog production function to measure corporates' ES. Therefore, data of 105 manufacturing corporates have been collected during 2008-2017. With regard to linearity between independent variables, the elastic net regression method has been used. Results indicate that some industries are of increasing economics to scale trend and some have a decreasing ES. Results can be applied in order to plan and determine the appropriate production level in the management accounting of corporates.


Main Subjects

[1] Radfar, M., Zomorodian, G., Aligholi, M., Minouei, M., Hanifi, F., Designing Native Decision-Making Model for Selecting Venture Capital Investment in Emerging Companies, Advances in Mathematical Finance and Applications, 2019, 4(2), P.75-88. Doi: 10.22034/amfa.2019.584735.1178
[2] Fellingham, J., Schroeder, D., Essays in Accounting Theory in Honour of Joel S. Demski, 2002.
[3] Buchanan, D., Huczynski, A., Organizational Behavior, Introductory Text. Prentice Hall, Third Edition, 1997.
[4] Rezaeean, A., Principles of Organizational Behavior Management, Tehran, Sari, 2002, Third Edition.
[5] Jamkarani, R., Lalbar, A., Explaining the Relationship Between Sticky of Expenses with Prediction Error of Profit in Tehran Stock Exchange, Advances in Mathematical Finance and Applications, 2016; 1(1), P.11-18. Doi: 10.22034/amfa.2016.526240
[6] Khajavai, S., Sadeghzadeh Maharouli, M., Jokar, M., Taghizadeh, R., Cost Stickiness and Cost Inertia: Two Cost Driver Model of Cost Asymmetric Behavior, Knowledge of Accounting and Audit Management, 2019, 29, P.135-148. (in Persian).
[7] Mortazavi, M., Nikoomaram, H., Banimahd, B., Asymmetric Cost Behavior: Review of Literature and Methodology. Knowledge of Accounting and Audit Management, 2018, 28,P. 29-50. (In Persian).
[8] Banker, R., Byzalov, D., Fang, S., Liang, Y., Cost Management Research. Journal of Management Accounting Research, 2017. Doi:org/10.2308/jmar-51965
[9] Thompson, H., A physical production function for the US economy, Energy Economics, 2016, 56, 185-189. Doi:org/10.1016/j.eneco.2016.03.016
[10] Mizobuchi, H., Returns to scale effect in labour productivity growth, Journal of Productivity Analysis, 2014, 42(3): 293-304. Doi:org/ 10.1007/s11123-014-0408-9
[11] Gu, W., Lafrance, A., Productivity Growth in the Canadian Broadcasting and Telecommunications Industry, Evidence from Micro Data, Economic Analysis Division, 2014.
[12] Chen, X., Increasing returns to scale in U.S. manufacturing industries: evidence from direct and reverse regression, Working Paper, 2011
[13] Fleisher, B., Hu, Y., Li, H., Kim, S., Economic transition, higher education and worker productivity in China, Journal of Development Economics, 2011, 94, 86–94. Doi:org/10.1016/j.jdeveco.2010.01.001
[14] Diewert, E., Nakajima, T., Nakamura, A., Nakamura, E., Returns to scale: concept, estimation and analysis of Japan’s turbulent 1964–88 economy, Canadian Journal of Economics, 2011, 44(2). Doi:org/0008-40 85/11 /451-485
[15] Diewert, W., Fox, K., On the estimation of returns to scale, technical progress and monopolistic markups, Journal of Econometrics, 2008, 145, P. 174– 193. Doi:org/10.1016/j.jeconom.2008.05.002
[16] Oliveira, F., Jayme, F., Lemos, M., Increasing Returns to Scale and International Diffusion of Technology: An Empirical Study for Brazil, World Development, 2006, 34(1), P.75–88.  Doi:org/10.1016/j.worlddev .2005.07. 011
[17] Sharma, S., Sylwester, K., Margono, H., Decomposition of Total Factor Productivity Growth in US States, Quarterly Review of Economics and Finance, 2007, 47(6) 215-241. Doi:org/10.1016%2Fj.qref.2006.08.001
[18] Deliktas, E., Karadag, M., Onder, A., TFP Change in the Turkish Manufacturing Industry in the Selected Provinces: 1990-1998. Department of Economics, Ege University, Izmir, 2001.
[19] Piesse, J., Thirtle, C., A Stochastic Frontier Approach to Firm Level Efficiency, Technological Change and Productivity During the Early transition in Hungary, Journal of Comparative Economics, 2000, 28, P.473-501. Doi:org/10.1006/jcec.2000.1672
[20] Fingleton, B., McCombie, J., Increasing Returns and Economic Growth: Some Evidence for Manufacturing from the European Union Regions, Oxford Economic Papers, 1998, 50(1), P.89-105. Doi:org/10.1093/oxfordjournals.oep.a028638
[21] Basu, S., Fernald J., Returns to Scale in US Production: Estimates and Implications, Journal of Political Economy,1997, 105, P.249–283. Doi:org/10.1086/262073
[22] Azamzadeh Shurki, M., Khalilian, S., Mortazavi, A., Election Production Function and Estimate Important Coefficient of Energy in Agricultural Sector, 2011,19 (76). 205-230. (In Persian).
[24] Agah, M., Malekpoorb, H., Investigating the Effect of Financial Constraints and Different Levels of Agency Cost on Investment Efficiency, Advances in Mathematical Finance and Applications, 2017, 2(4), P. 31-47. Doi: 10.22034/amfa.2017.536264 (In Persian)
[25] Christensen, L., Jorgenson, R., Law, L., The translog function and the substitution of equipment, structures, and labor in U.S. manufacturing 1929-68, Journal of Econometrics. 1973, 1, P.81-113. Doi:org/10.1016/0304-4076(73)90007-9.
[26] Christensen, J, Demski, J., Accounting Information Management Applications. 2008.
[27] Demerjian, P., Lev, B., Lewis, M., MacVay, S., Managerial ability and earnings quality, The Accounting Review, 2013, 88 (2), P.463-498.  Doi:org/10.2308/accr-10665
[28] Arast, M., Arashi, M., Rabie M., Performance Study of Shrinkage Estimator Under a Linear Constrain in Penalized Regression, Journal of Statistical Sciences, 2019, 13(1), P.1-14. Doi:org/10.29252/jss. 13.1.1(In Persian).
[29] Izadikhah, M., M Tavana, M., Di Caprio, D., Santos-Arteaga, FJ, A novel two-stage DEA production model with freely distributed initial inputs and shared intermediate outputs, Expert Systems with Applications 99, 213-230. DOI: 10.1016/j.eswa.2017.11.005
[30] Aghamohammadi, A., Mohammadi, S., Bayesian Quantile Regression with Lasso and Adaptive Lasso Penalty for Binary Longitudinal Data, Journal of Statistical Sciences. 2016, 9(2). P.147-167. (In Persian).
[31] Price, B.S., Sherwood, B., A Cluster Elastic Net for Multivariate Regression, Journal of Machine Learning Research, 2018, 18, P.1-39