Malmquist-Luenberger productivity indexes for dynamic network DEA with undesirable outputs and negative data

Open Access

Issue		RAIRO-Oper. Res. Volume 56, Number 2, March-April 2022


Page(s)		649 - 687
DOI		https://doi.org/10.1051/ro/2022023
Published online		14 April 2022

A.I. Ali and L.M. Seiford, Translation invariance in data envelopment analysis. Oper. Res. Lett. 9 (1990) 403–405. [CrossRef] [Google Scholar]
J. Aparicio, J.T. Pastor and J.L. Zofio, On the inconsistency of the Malmquist-Luenberger index. Eur. J. Oper. Res. 229 (2013) 738–742. [CrossRef] [Google Scholar]
J. Aparicio, J.T. Pastor and F. Vidal, The directional distance function and the translation invariance property. Omega 58 (2016) 1–3. [CrossRef] [Google Scholar]
J. Aparicio, J. Barbero, M. Kapelko, J.T. Pastor and J.L. Zofo, Testing the consistency and feasibility of the standard Malmquist-Luenberger index: environmental productivity in world air emissions. J. Environ. Manage. 196 (2017) 148–160. [CrossRef] [Google Scholar]
B. Arabi, S. Munisamy and A. Emrouznejad, A new slacks-based measure of Malmquist-Luenberger index in the presence of undesirable outputs. Omega 51 (2015) 29–37. [Google Scholar]
N.K. Avkiran, An illustration of dynamic network DEA in commercial banking including robustness tests. Omega 55 (2015) 141–150. [CrossRef] [Google Scholar]
W. Briec and K. Kerstens, Infeasibility and directional distance functions with application to the determinateness of the luenberger productivity indicator. J. Optim. Theory App. 141 (2009) 55. [CrossRef] [Google Scholar]
W. Briec and K. Kerstens, The luenberger productivity indicator: an economic specification leading to infeasibilities. Econ. Modell. 26 (2009) 597–600. [CrossRef] [Google Scholar]
D.W. Caves, L.R. Christensen and W.E. Diewert, The economic theory of index numbers and the measurement of input, output, and productivity. Econ. J. Econ. Soc. 50 (1982) 1393–1414. [Google Scholar]
Y.-T. Chang, H.K. Park, B. Zou and N. Kafle, Passenger facility charge vs. airport improvement program funds: a dynamic network DEA analysis for US airport financing. Transp. Res. Part E: Logistics Transp. Rev. 88 (2016) 76–93. [CrossRef] [Google Scholar]
A. Charnes, W.W. Cooper, B. Golany, R. Halek, G. Klopp, E. Schmitz and D. Thomas, Two phase data envelopment analysis approaches to policy evaluation and management of army recruiting activities: tradeoffs between joint services and army advertising. In: Center for Cybernetic Studies. University of Texas-Austin Austin, Texas, USA (1986). [Google Scholar]
Y. Chen, A non-radial Malmquist productivity index with an illustrative application to Chinese major industries. Int. J. Prod. Econ. 83 (2003) 27–35. [CrossRef] [Google Scholar]
K. Chen and J. Zhu, Scale efficiency in two-stage network DEA. J. Oper. Res. Soc. 70 (2019) 101–110. [CrossRef] [Google Scholar]
Y.H. Chung, R. Färe and S. Grosskopf, Productivity and undesirable outputs: a directional distance function approach. J. Environm. Manage. 51 (1997) 229–240. [CrossRef] [Google Scholar]
T.J. Coelli, D.S.P. Rao, C.J. O’Donnell and G.E. Battese, An Introduction to Efficiency and Productivity Analysis. Springer Science & Business Media (2005). [Google Scholar]
W.D. Cook, L. Liang and J. Zhu, Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega 38 (2010) 423–430. [Google Scholar]
C.A. Denizer, M. Dinc and M. Tarimcilar, Financial liberalization and banking efficiency: evidence from Turkey. J. Prod. Anal. 27 (2007) 177–195. [CrossRef] [Google Scholar]
J. Du, Y. Chen and Y. Huang, A modified Malmquist-Luenberger productivity index: assessing environmental productivity performance in China. Eur. J. Oper. Res. 269 (2018) 171–187. [CrossRef] [Google Scholar]
J. Du, Y. Duan and J. Xu, The infeasible problem of Malmquist-Luenberger index and its application on China’s environmental total factor productivity. Ann. Oper. Res. 278 (2019) 235–253. [CrossRef] [MathSciNet] [Google Scholar]
A. Emrouznejad and E. Thanassoulis, A mathematical model for dynamic efficiency using data envelopment analysis. Appl. Math. Comput. 160 (2005) 363–378. [Google Scholar]
A. Emrouznejad and E. Thanassoulis, Measurement of productivity index with dynamic DEA. Int. J. Oper. Res. 8 (2010) 247. [CrossRef] [Google Scholar]
A. Emrouznejad and G.-L. Yang, A framework for measuring global Malmquist-Luenberger productivity index with CO₂ emissions on Chinese manufacturing industries. Energy 115 (2016) 840–856. [CrossRef] [Google Scholar]
A. Emrouznejad, A.L. Anouze and 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) 297–304. [CrossRef] [Google Scholar]
G. Falavigna, R. Ippoliti and G.B. Ramello, DEA-based Malmquist productivity indexes for understanding courts reform. Soc.-Econ. Planning Sci. 62 (2018) 31–43. [CrossRef] [Google Scholar]
R. Färe and S. Grosskopf, Intertemporal production frontiers with dynamic DEA. In: Kluwer Academic, collaboration with R. Briinnlund et al. Boston (1996). [CrossRef] [Google Scholar]
R. Färe, S. Grosskopf and C.A.K. Lovell, Production Frontiers. Cambridge University Press (1994). [Google Scholar]
R. Färe, S. Grosskopf, M. Norris and Z. Zhang, Productivity growth, technical progress, and efficiency change in industrialized countries. Am. Econ. Rev. 84 (1994) 66–83. [Google Scholar]
D. Fernández, C. Pozo, R. Folgado, L. Jiménez and G. Guillén-Gosálbez, Productivity and energy efficiency assessment of existing industrial gases facilities via data envelopment analysis and the Malmquist index. Appl. Energy 212 (2018) 1563–1577. [CrossRef] [Google Scholar]
H. Fukuyama and W.L. Weber, Measuring Japanese bank performance: a dynamic network DEA approach. J. Prod. Anal. 44 (2015) 249–264. [CrossRef] [Google Scholar]
H. Fukuyama and W.L. Weber, Japanese bank productivity, 2007–2012: a dynamic network approach. Pac. Econ. Rev. 22 (2017) 649–676. [CrossRef] [Google Scholar]
H. Fukuyama, R. Matousek and N.G. Tzeremes, A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: evidence from the Turkish banking system. Omega 95 (2020) 102198 [CrossRef] [Google Scholar]
S. Ghobadi, A generalized DEA model for inputs (outputs) estimation under inter-temporal dependence. RAIRO-Oper. Res. 53 (2019) 1791–1805. [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
E. Grifell-Tatjé and C.A.K. Lovell, A note on the Malmquist productivity index. Econ. Lett. 47 (1995) 169–175. [CrossRef] [Google Scholar]
I.C. Henriques, V.A. Sobreiro, H. Kimura and E.B. Mariano, Two-stage DEA in banks: terminological controversies and future directions. Expert Syst. App. 161 (2020) 113–632. [Google Scholar]
O. Herrera-Restrepo, K. Triantis, J. Trainor, P. Murray-Tuite and P. Edara, A multi-perspective dynamic network performance efficiency measurement of an evacuation: a dynamic network-DEA approach. Omega 60 (2016) 45–59. [CrossRef] [Google Scholar]
D. Holod and H.F. Lewis, Resolving the deposit dilemma: a new DEA bank efficiency model. J. Banking Finance 35 (2011) 2801–2810. [Google Scholar]
J. Jablonsky, Ranking of countries in sporting events using two-stage data envelopment analysis models: a case of Summer Olympic Games 2016. Central Eur. J. Oper. Res. 26 (2018) 951–966. [CrossRef] [MathSciNet] [Google Scholar]
G.R. Jahanshahloo and M. Piri, Data Envelopment Analysis (DEA) with integer and negative inputs and outputs. J. Data Envelopment Anal. Decis. Sci. 2013 (2013) 1–15. [Google Scholar]
A.R. Jayaraman and P. Bhuyan, Impact of NPA and loan write-offs on the profit efficiency of Indian banks. Decision 47 (2020) 35–48. [CrossRef] [Google Scholar]
C. Kao, Dynamic data envelopment analysis: a relational analysis. Eur. J. Oper. Res. 227 (2013) 325–330. [CrossRef] [Google Scholar]
C. Kao, Network data envelopment analysis: a review. Eur. J. Oper. Res. 239 (2014) 1–16. [Google Scholar]
C. Kao, Network Data Envelopment Analysis: Foundations and Extensions. Springer (2017). [CrossRef] [Google Scholar]
S. Kourtzidis, R. Matousek and N.G. Tzeremes, Modelling a multi-period production process: evidence from the Japanese regional banks. Eur. J. Oper. Res. 294 (2021) 327–339. [CrossRef] [Google Scholar]
S. Kumar, Environmentally sensitive productivity growth: a global analysis using Malmquist-Luenberger index. Ecol. Econ. 56 (2006) 280–293. [CrossRef] [Google Scholar]
R. Lin and Z. Chen, Super-efficiency measurement under variable return to scale: an approach based on a new directional distance function. J. Oper. Res. Soc. 66 (2015) 1506–1510. [CrossRef] [Google Scholar]
R. Lin and Z. Chen, A directional distance based super-efficiency DEA model handling negative data. J. Oper. Res. Soc. 68 (2017) 1312–1322. [CrossRef] [Google Scholar]
R. Lin and Y. Liu, Super-efficiency based on the directional distance function in the presence of negative data. Omega 85 (2019) 26–34. [CrossRef] [Google Scholar]
C.A.K. Lovell, The decomposition of malmquist productivity indexes. J. Prod. Anal. 20 (2003) 437–458. [CrossRef] [Google Scholar]
C.A.K. Lovell, J.T. Pastor and J.A. Turner, Measuring macroeconomic performance in the OECD: a comparison of European and non-European countries. Eur. J. Oper. Res. 87 (1995) 507–518. [CrossRef] [Google Scholar]
S. Lozano and N. Soltani, Efficiency assessment using a multidirectional DDF approach. Int. Trans. Oper. Res. 27 (2020) 2064–2080. [Google Scholar]
S. Malmquist, Index numbers and indifference surfaces. Trabajos de Estadistica y de Investigacion Operativa 4 (1953) 209–242. [CrossRef] [Google Scholar]
F.B. Mariz, M.R. Almeida and D. Aloise, A review of dynamic data envelopment analysis: state of the art and applications. Int. Trans. Oper. Res. 25 (2018) 469–505. [CrossRef] [MathSciNet] [Google Scholar]
R.K. Matin, G.R. Amin and A. Emrouznejad, A modified semi-oriented radial measure for target setting with negative data. Measurement 54 (2014) 152–158. [CrossRef] [Google Scholar]
P. Moreno and S. Lozano, Super SBI Dynamic Network DEA approach to measuring efficiency in the provision of public services. Int. Trans. Oper. Res. 25 (2018) 715–735. [CrossRef] [MathSciNet] [Google Scholar]
E. Njuki, B.E. Bravo-Ureta and C.J. O’Donnell, Decomposing agricultural productivity growth using a random-parameters stochastic production frontier. Empirical Econ. 57 (2019) 839–860. [CrossRef] [Google Scholar]
D.-H. Oh and A. Heshmati, A sequential Malmquist-Luenberger productivity index: environmentally sensitive productivity growth considering the progressive nature of technology. Energy Econ. 32 (2010) 1345–1355. [CrossRef] [Google Scholar]
K.S. Park and K. Park, Measurement of multiperiod aggregative efficiency. Eur. J. Oper. Res. 193 (2009) 567–580. [CrossRef] [Google Scholar]
M.C.A.S. Portela and E. Thanassoulis, Malmquist-type indices in the presence of negative data: an application to bank branches. J. Banking Finance 34 (2010) 1472–1483. [CrossRef] [Google Scholar]
M.C.A.S. Portela, E. Thanassoulis and G. Simpson, Negative data in DEA: a directional distance approach applied to bank branches. J. Oper. Res. Soc. 55 (2004) 1111–1121. [CrossRef] [Google Scholar]
S.C. Ray and E. Desli, Productivity growth, technical progress, and efficiency change in industrialized countries: comment. Am. Econ. Rev. 87 (1997) 1033–1039. [Google Scholar]
R.R. Russell and W. Schworm, Technological inefficiency indexes: a binary taxonomy and a generic theorem. J. Prod. Anal. 49 (2018) 17–23. [CrossRef] [Google Scholar]
H. Scheel, Undesirable outputs in efficiency valuations. Eur. J. Oper. Res. 132 (2001) 400–410. [Google Scholar]
L.M. Seiford and J. Zhu, Modeling undesirable factors in efficiency evaluation. Eur. J. Oper. Res. 142 (2002) 16–20. [Google Scholar]
J.A. Sharp, W. Meng and W. Liu, A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs. J. Oper. Res. Soc. 58 (2007) 1672–1677. [CrossRef] [Google Scholar]
V. Shestalova, Sequential Malmquist indices of productivity growth: an application to OECD industrial activities. J. Prod. Anal. 19 (2003) 211–226. [CrossRef] [Google Scholar]
R.M. Solow, Technical change and the aggregate production function. Rev. Econ. Stat. 39 (1957) 312–320. [CrossRef] [Google Scholar]
M. Tavana, M. Izadikhah, D. Di Caprio and R.F. Saen, A new dynamic range directional measure for two-stage data envelopment analysis models with negative data. Comput. Ind. Eng. 115 (2018) 427–448. [CrossRef] [Google Scholar]
K. Tone and M. Tsutsui, Dynamic DEA: a slacks-based measure approach. Omega 38 (2010) 145–156. [CrossRef] [Google Scholar]
K. Tone and M. Tsutsui, Dynamic DEA with network structure: a slacks-based measure approach. Omega 42 (2014) 124–131. [CrossRef] [Google Scholar]
H. Tulkens and P.V. Eeckaut, Non-parametric efficiency, progress and regress measures for panel data: methodological aspects. Eur. J. Oper. Res. 80 (1995) 474–499. [CrossRef] [Google Scholar]
B. Walheer, Malmquist productivity index for multi-output producers: an application to electricity generation plants. Soc.-Econ. Plan. Sci. 65 (2019) 76–88. [CrossRef] [Google Scholar]
D.C. Wheelock and P.W. Wilson, Technical progress, inefficiency, and productivity change in us banking, 1984–1993. J. Money Credit Bank. 31 (1999) 212–234. [CrossRef] [Google Scholar]
A. Zanella, A.S. Camanho and T.G. Dias, Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis. Eur. J. Oper. Res. 245 (2015) 517–530. [Google Scholar]
Y. Zha, N. Liang, M. Wu and Y. Bian, Efficiency evaluation of banks in China: a dynamic two-stage slacks-based measure approach. Omega 60 (2016) 60–72. [CrossRef] [Google Scholar]
J.L. Zofio, Malmquist productivity index decompositions: a unifying framework. Appl. Econ. 39 (2007) 2371–2387. [CrossRef] [Google Scholar]

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