Volume 28, Issue 1 (Spring 2023)
JPBUD 2023, 28(1): 3-30 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Khiabani N, Zomorrodi Anbaji M. (2023). A Review of Measuring Inefficiency Models. JPBUD. 28(1), 3-30. doi:10.61186/jpbud.28.1.3
URL: http://jpbud.ir/article-1-2150-en.html
URL: http://jpbud.ir/article-1-2150-en.html
1- Department of Economics, Faculty of Economics, Allameh Tabataba'i University, Tehran, Iran. , naser.khiabani@atu.ac.ir
2- Institute for Management and Planning Studies, Tehran, Iran.
2- Institute for Management and Planning Studies, Tehran, Iran.
Abstract: (1209 Views)
An inefficient firm causes a waste of resources by using a non-optimal combination of capital and labor and especially energy. Measuring inefficiency and identifying the factors that cause it is vital in achieving the firm's potential production and, as a result, the sustainability of economic growth and increasing the economic well-being of the society. Meanwhile, choosing the right model to measure inefficiency is always one of the basic challenges among researchers in this field. This study attempts to provide an overview of the concept of efficiency and its types and different measurement models with an emphasis on the Frontier Analysis. Considering the large volume of theoretical and experimental studies in inefficiency, our review aims to provide a proper understanding of their strengths and weaknesses by investigating the evolution of the models demonstrated in the literature, and thus helps the researcher to create a clear framework for analysis and choosing the appropriate model. Furthermore, this study offers suggestions to measure inefficiency as accurately as possible from the theoretical and technical viewpoints, which have been understudied in the literature.
Keywords: Technical Efficiency, Distributive Efficiency, Stochastic Frontier Production, Trans-log Cost Function System, Bayesian Approach
Type of Study: Review |
Subject:
Microeconomics
Received: Oct 05 2022 | Accepted: Jan 17 2023 | ePublished: Jun 17 2023
Received: Oct 05 2022 | Accepted: Jan 17 2023 | ePublished: Jun 17 2023
References
1. Afriat, S. N. (1972). Efficiency Estimation of Production Functions. International Economic Review, 13(3), 568-598. [DOI:10.2307/2525845]
2. Aigner, D. J., & Chu, S.-f. (1968). On Estimating the Industry Production Function. The American Economic Review, 58(4), 826-839.
3. Aigner, D., Lovell, C. K., & Schmidt, P. (1977). Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics, 6(1), 21-37. [DOI:10.1016/0304-4076(77)90052-5]
4. Atkinson, S. E., & Cornwell, C. (1994). Parametric Estimation of Technical and Allocative Inefficiency with Panel Data. International Economic Review, 35(1), 231-243. [DOI:10.2307/2527099]
5. Battese, G. E., & Coelli, T. J. (1992). Frontier Production Functions, Technical Efficiency and Panel Data: With Application to Paddy Farmers in India. Journal of Productivity Analysis, 3(1), 153-169. [DOI:10.1007/BF00158774]
6. 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(1), 325-332. [DOI:10.1007/BF01205442]
7. Bauer, P. W. (1990). Recent Developments in the Econometric Estimation of Frontiers. Journal of Econometrics, 46(1-2), 39-56. [DOI:10.1016/0304-4076(90)90046-V]
8. Brissimis, S. N., Delis, M. D., & Tsionas, E. G. (2010). Technical and Allocative Efficiency in European Banking. European Journal of Operational Research, 204(1), 153-163. [DOI:10.1016/j.ejor.2009.09.034]
9. Chen, Z., Barros, C. P., & Borges, M. R. (2015). A Bayesian Stochastic Frontier Analysis of Chinese Fossil-Fuel Electricity Generation Companies. Energy Economics, 48(1), 136-144. [DOI:10.1016/j.eneco.2014.12.020]
10. Christensen, L. R., & Greene, W. H. (1976). Economies of Scale in US Electric Power Generation. Journal of Political Economy, 84(4, Part 1), 655-676. [DOI:10.1086/260470]
11. Coelli, T. J., Rao, D. S. P., O'Donnell, C. J., & Battese, G. E. (2005). An Introduction to Efficiency and Productivity Analysis: Springer Science & Business Media.
12. Debreu, G. (1951). The Coefficient of Resource Utilization. Econometrica: Journal of the Econometric Society, 19(3), 273-292. [DOI:10.2307/1906814]
13. Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281. [DOI:10.2307/2343100]
14. Fernandez, C., Osiewalski, J., & Steel, M. F. (1997). On the Use of Panel Data in Stochastic Frontier Models with Improper Priors. Journal of Econometrics, 79(1), 169-193. [DOI:10.1016/S0304-4076(97)88050-5]
15. Førsund, F. R., Lovell, C. K., & Schmidt, P. (1980). A Survey of Frontier Production Functions and of Their Relationship to Efficiency Measurement. Journal of Econometrics, 13(1), 5-25. [DOI:10.1016/0304-4076(80)90040-8]
16. Greene, W. H. (1990). A Gamma-Distributed Stochastic Frontier Model. Journal of Econometrics, 46(1-2), 141-163. [DOI:10.1016/0304-4076(90)90052-U]
17. Griffiths, W. E., O'Donnell, C. J., & Cruz, A. T. (2000). Imposing Regularity Conditions on a System of Cost and Factor Share Equations. Australian Journal of Agricultural and Resource Economics, 44(1), 107-127. [DOI:10.1111/1467-8489.00101]
18. Haghiri, M. (2003). Stochastic Non-Parametric Frontier Analysis in Measuring Technical Efficiency: A Case Study of the North American Dairy Industry. (Doctor of Philosophy). University of Saskatchewan,
19. Jondrow, J., Lovell, C. K., Materov, I. S., & Schmidt, P. (1982). On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Function Model. Journal of Econometrics, 19(2-3), 233-238. [DOI:10.1016/0304-4076(82)90004-5]
20. Judge, G. G., Griffith, W., Hill, C. R., Lütkepohl, H., & Lee, T. (1985). The Theory and Practice of Econometrics: Wiley.
21. Khiabani, N., & Hasani, K. (2010). Technical and Allocative Inefficiencies and Factor Elasticities of Substitution: An Analysis of Energy Waste in Iran's Manufacturing. Energy Economics, 32(5), 1182-1190. [DOI:10.1016/j.eneco.2010.05.004]
22. Koopmans, T. C. (1951). An Analysis of Production as an Efficient Combination of Activities. In Activity Analysis of Production and Allocation, T. C. Koopmans (Ed.), (pp. 33-97): Wiley.
23. Kumbhakar, S. C. (1997). Modeling Allocative Inefficiency in a Translog Cost Function and Cost Share Equations: an Exact Relationship. Journal of Econometrics, 76(1-2), 351-356. [DOI:10.1016/0304-4076(95)01796-8]
24. Kumbhakar, S. C., & Tsionas, E. G. (2005). Measuring Technical and Allocative Inefficiency in the Translog Cost System: A Bayesian Approach. Journal of Econometrics, 126(2), 355-384. [DOI:10.1016/j.jeconom.2004.05.006]
25. Kumbhakar, S. C., & Tsionas, M. G. (2021). Estimation of Costs of Technical and Allocative Inefficiency. Journal of Productivity Analysis, 55(1), 41-46. [DOI:10.1007/s11123-020-00596-4]
26. Kumbhakar, S., & Lovell, K. (2000). Stochastic Frontier Analysis: Cambridge University Press. [DOI:10.1017/CBO9781139174411]
27. Kutlu, L., & McCarthy, P. (2016). US Airport Ownership, Efficiency, and Heterogeneity. Transportation Research Part E: Logistics and Transportation Review, 89(1), 117-132. [DOI:10.1016/j.tre.2016.03.003]
28. Lau, L. J., & Yotopoulos, P. A. (1971). A Test for Relative Efficiency and Application to Indian Agriculture. The American Economic Review, 61(1), 94-109.
29. Martín, J. C., & Voltes-Dorta, A. (2011). The Econometric Estimation of Airports' Cost Function. Transportation Research Part B: Methodological, 45(1), 112-127. [DOI:10.1016/j.trb.2010.05.001]
30. Meeusen, W., & van Den Broeck, J. (1977). Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error. International Economic Review, 18(2), 435-444. [DOI:10.2307/2525757]
31. Morana, C. (2007). Factor Demand Modelling: The Theory and the Practice. International Centre for Economic Research, Working Paper No. 9/2007.
32. O'Donnell, C. J., & Coelli, T. J. (2005). A Bayesian Approach to Imposing Curvature on Distance Functions. Journal of Econometrics, 126(2), 493-523. [DOI:10.1016/j.jeconom.2004.05.011]
33. Pitt, M. M., & Lee, L.-F. (1981). The Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry. Journal of Development Economics, 9(1), 43-64. [DOI:10.1016/0304-3878(81)90004-3]
34. Richmond, J. (1974). Estimating the Efficiency of Production. International Economic Review, 15(2), 515-521. [DOI:10.2307/2525875]
35. Schmidt, P., & Lovell, C. K. (1979). Estimating Technical and Allocative Inefficiency Relative to Stochastic Production and Cost Frontiers. Journal of Econometrics, 9(3), 343-366. [DOI:10.1016/0304-4076(79)90078-2]
36. Schmidt, P., & Sickles, R. C. (1984). Production Frontiers and Panel Data. Journal of Business & Economic Statistics, 2(4), 367-374. [DOI:10.1080/07350015.1984.10509410]
37. Stevenson, R. E. (1980). Likelihood Functions for Generalized Stochastic Frontier Estimation. Journal of Econometrics, 13(1), 57-66. [DOI:10.1016/0304-4076(80)90042-1]
38. Terrell, D. (1996). Incorporating Monotonicity and Concavity Conditions in Flexible Functional Forms. Journal of Applied Econometrics, 11(2), 179-194. [DOI:10.1002/(SICI)1099-1255(199603)11:23.0.CO;2-G]
39. Winsten, C. (1957). Discussion on Mr. Farrell's Paper. Journal of the Royal Statistical Society, 120(3), 282-284. [DOI:10.2307/2343114]
| Rights and permissions | |
.jpg) |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
