Abstract
Increasing marginal rate of transformation (MRT) in production is a generally accepted economic presumption. So how to reflect the increasing MRT in linear and non-linear production functions? However, in Leontief’s input-output model, it is assumed that production is performed on a fixed proportion of inputs and usually leads to the constant MRT. This paper analyzes the linear output possibility frontier under the Leontief production function in detail, the author tried to fix this problem by considering the non-unique primary inputs in a simplified two-sector economy. Then we discuss the production possibility frontier under the assumption of nonlinear production function: First, when there are many primary inputs (heterogeneity) constraints, it is possible to curve the net output possibility frontier of Leontief production function and make it meet the assumption of increasing MRT. Second, in the intertemporal production process, a total output possibility frontier with increasing MRT can be obtained under a general production function without the assumption of non-unique primary inputs.
The research was supported by grant number 14AZD085, a key project titled “Research on the Evolution Trend and Countermeasures of China’s Economic Growth Quality under the Background of New Normal” financed by the Social Science Foundation of China, and grant number 71373106, a project titled “Transformation Dynamic Research and Policy Simulation of Industrial Value-added Rate: A Case Study of Manufacturing Industry in the Yangtze River Delta Region” financed by the Natural Science Foundation of China.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Stolper, W.F., Samuelson, P.A.: Protection and real wages. Rev. Econ. Stud. 9(1), 58–73 (1941)
Hal, R.: Intermediate Microeconomics: A Modern Approach, p. 938. W.W. Norton, New York (1987)
Barthwal, R.: Industrial Economics: An Introductory Textbook. New Age International, New Delhi (2007)
Anderson, D.A.: Cracking the AP Economics Macro Micro Exams, vol. 9. Princeton Review, New York (2009)
Hall, R.E., Lieberman, M.: Macroeconomics: Principles and Applications, 6th edn. Longman, Harlow (1989)
Brooke, M.Z., Buckley, P.J.: Handbook of International Trade. Springer, Heidelberg (2016)
Jones, C.I.: Intermediate goods and weak links in the theory of economic development. Am. Econ. J.: Macroecon. 3(2), 1–28 (2011)
Jones, C.I.: Misallocation, economic growth, and input-output economics. NBER Working Paper No. 16742. National Bureau of Economic Research (2011)
Dorfman, R., Samuelson, P.A., Solow, R.M.: Linear Programming and Economic Analysis. Dover Publications, New York, p. ix, 525 (1987)
Mas-Colell, A., Whinston, M.D., Green, J.R.: Microeconomic Theory, pp. 370–372. Oxford University Press, Oxford (1995)
Acemoglu, D.: Introduction to Modern Economic Growth. Princeton University Press, Princeton (2008)
Ljungqvist, L., Sargent, T.J.: Recursive Macroeconomic Theory. MIT Press, Cambridge (2018)
Hinojosa, M.A., Mármol, A.M., Monroy, L., Fernández, F.R.: A multi-objective approach to fuzzy linear production games. Int. J. Inf. Technol. Decis. Making 12(05), 927–943 (2013)
Peidro, D., Mula, J., Poler, R.: Fuzzy linear programming for supply chain planning under uncertainty. Int. J. Inf. Technol. Decis. Making 09(03), 373–392 (2010)
Fukuyama, H., Weber, W.L.: Modeling output gains and earnings gains. Int. J. Inf. Technol. Decis. Making 04(03), 433–454 (2005)
Wang, J.: Estimation of the allocation efficiency of factor of production in China. Stat. Decis. 23, 129–132 (2018)
Peng, H., Pang, T., Cong, J., et al.: Coordination contracts for a supply chain with yield uncertainty and low-carbon preference. J. Cleaner Prod. 205, 291–302 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, W., Fan, J. (2020). Study on Production Possibility Frontier Under Different Production Function Assumptions. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_13
Download citation
DOI: https://doi.org/10.1007/978-981-15-2810-1_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2809-5
Online ISBN: 978-981-15-2810-1
eBook Packages: Computer ScienceComputer Science (R0)