A semi-oriented radial measure for Malmquist Productivity Index: A case study of regional electricity companies in Iran

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Mathematics, Shahr-e-Qhods Branch, Islamic Azad University, Tehran, Iran

Abstract

One of the important applications of data envelopment analysis is to determine the progress and regress of the units under evaluation at two different times, which has been addressed in many papers. Also, one of the distinctions of data coverage analysis technique with other methods is the introduction of achievable and flexible benchmarks. In the present paper, we intend to study the progress and regress of Iranian regional electricity companies during two consecutive years of 2015 and 2016. Since some of the evaluated indicators are semi-positive and semi-negative indicators, in this study we will develop Emrooznejad et al.  [7] to determine the productivity index of Malmquist for semi-positive and semi-negative indicators. Finally, for further explanation, we have used the proposed models to determine the progress and regression of 16 regional electricity companies in Iran with 3 semi-positive and semi-negative indices in the presence of the limitation on the benchmark, an undesirable index and 11 completely positive indices in the nature of input with constant scale returns as a black box.

Keywords

[1] S.A. Berg, F.R. Forsund and E.S. Jansen, Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980-89, Scand. J. Econ. 94 (1992), 211–228.
[2] D.W. Caves, L.R. Christensen and W. Erwin Diewert, The economic theory of index numbers and the measurement of input, output, and productivity, Econometrica: J. Econometric Soc. 50 (1982), no. 6, 1393–1414.
[3] A.S. Camanho and R.G. Dyson, Data envelopment analysis and Malmqutist indices for measuring group performance, J. Prod. Anal. 26 (2006), 35–49.
[4] A. Charnes, W.W. Cooper and E. Rhodes, Measuring the efficiency of decision making units, Eur. J. Oper. Res. 2 (1978), no. 6, 429–444.
[5] H. Essid, P. Ouellette and S. Vigeant, Productivity, efficiency, and technical change of Tunisian schools: A bootstrapped Malmquist approach with quasi-fixed inputs, Omega 42 (2014), no. 1, 88–97.
[6] A. Emrouznejad, G.R. Amin, E. Thanassoulis and A.L. Anouze, On the boundedness of the SORM DEA models with negative data, Eur. J. Oper. Res. 206 (2010), 265–268.
[7] A. Emrouznejad, A.L. Anouze, E. Thanassoulis, A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. Eur. J. Oper. Res. 200 (2010), no. 1, 297–304.
[8] R. Fare, S. Grosskopf, M. Norris and Z. Zhang, Productivity growth, technical progress and efficiency change in industrialized countries, Amer. Econ. Rev. 1 (1994), 66–83.
[9] M.J. Farrell, The measurement of productive efficiency, J. Roy. Statist. Soc. Ser. A (General) 120 (1957), no. 3, 253–281.
[10] E. Grifell-Tatje, C.A. Knox Lovell, and J.T. Pastor., A quasi-Malmquist productivity index, J. Prod. Anal. 10 (1998), no. 1, 7–20.
[11] G. Halkos and K.N. Petrou, Treating undesirable outputs in DEA: A critical review, Econ. Anal. Policy 62 (2019),97–104.
[12] S.Z. Hosseini, F. Hosseinzadeh Lotfi and M. Ahadzadeh, Presenting a model for evaluating regional electricity companies in 2016, Fifteenth Nat. Conf. Data Env. Anal., Shiraz, September, 2017.
[13] T. Joro and P. Korhonen, Extension of Data Envelopment Analysis with Preference Information, Springer, 2015.
[14] S. Kaffash, E. Aktas and M. Tajikk, The impact of oil price changes on efficiency of banks: An application in the Middle East oil exporting countries using SORM-DEA, RAIRO-Oper. Res. 54 (2020), 719–748.
[15] S. Kaffash, R. Kazemi Matin and M. Tajik, A directional semi-oriented radial DEA measure: an application on financial stability and the efficiency of banks, Ann. Oper. Res. 264 (2018), 213–234.
[16] R. Kazemi Matin, G.R. Amin and A. Emrouznejad, A modified semi-oriented radial measure for target setting with negative data, Measurement 54 (2014), 152–158.
[17] R. Kazemi Matin and R. Azizi, A two-phase approach for setting targets in DEA with negative data, Appl. Math. Model. 35 (2011), 5794–5803.
[18] M. Khoveyni, R. Eslami and G.-L. Yang, Negative data in DEA: Recognizing congestion and specifying the least and the most congested decision making units, Comput. Oper. Res. 79 (2017), 39–48.
[19] N. Kiani Mavi and R. Kiani Mavi, Energy and environmental efficiency of OECD countries in the context of the circular economy: Common weight analysis for Malmquist productivity index, J. Envir. Manag. 247 (2019), 651–661.
[20] R. Lin, W. Yang, and H. Huang, A modified slacks-based super-efficiency measure in the presence of negative data, Comput. Ind. Engin. 135 (2019), 39–82.
[21] L. Orea, Parametric decomposition of a generalized Malmquist productivity index, J. Prod. Anal. 18 (2002), no. 1, 5–22.
[22] J.T. Pastor and C.A. Knox Lovell, A global Malmquist productivity index, Econ. Lett. 88 (2005), no. 2, 266–271.
[23] M. Soltanifar and H. Sharafi, A modified DEA cross efficiency method with negative data and its application in supplier selection, J. Combin. Optim. 43 (2022), 265–296.
[24] K. Tone, Malmquist Productivity Index, Springer, Boston, MA, 2004.
[25] K. Wang, S. Yu and W. Zhang., China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation, Mathematical and Computer Modelling 58(2013), no. 5-6, 1117–1127.
[26] Z. Zhou, G. Xu, C. Wang and J. Wu, Modeling undesirable output with a DEA approach based on an exponential transformation: An application to measure the energy efficiency of Chinese industry, J. Cleaner Prod. 236 (2019), 117717.
Volume 14, Issue 12
December 2023
Pages 241-252
  • Receive Date: 20 August 2022
  • Revise Date: 28 December 2022
  • Accept Date: 31 December 2022