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Estimating global frontier shifts and global Malmquist indices

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  • Mette Asmild

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  • Fai Tam

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Abstract

The Malmquist index is a measure of productivity changes, of which an important component is the frontier shift or technological change. Often technological change can be viewed as a global phenomenon, and therefore individual or local measures of technological changes are aggregated into an overall measure, traditionally using geometric means. In this paper we propose a way of calculating global Malmquist indices and global frontier shift indices which provides a better estimation of the true frontier shift and furthermore is easy to calculate. Using simulation studies we show how this method outperforms the traditional aggregation approach, especially for sparsely populated production possibility sets and for frontiers that also change shape over time. Furthermore, our global indices can be used for unbalanced panels without disregarding any information. Finally, we show how the global indices are meaningful for calculating differences between frontiers from different groups rather than different time periods as illustrated in a small case study of bank branches in different countries. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Mette Asmild & Fai Tam, 2007. "Estimating global frontier shifts and global Malmquist indices," Journal of Productivity Analysis, Springer, vol. 27(2), pages 137-148, April.
  • Handle: RePEc:kap:jproda:v:27:y:2007:i:2:p:137-148
    DOI: 10.1007/s11123-006-0028-0
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    References listed on IDEAS

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    1. Ray, Subhash C & Desli, Evangelia, 1997. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Comment," American Economic Review, American Economic Association, vol. 87(5), pages 1033-1039, December.
    2. Fare, Rolf, et al, 1997. " Biased Technical Change and the Malmquist Productivity Index," Scandinavian Journal of Economics, Wiley Blackwell, vol. 99(1), pages 119-127, March.
    3. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. Denise McEachern & Joseph Paradi, 2007. "Intra- and inter-country bank branch assessment using DEA," Journal of Productivity Analysis, Springer, vol. 27(2), pages 123-136, April.
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    Citations

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    Cited by:

    1. Pierre Ouellette & Valérie Vierstraete, 2010. "Malmquist indexes with quasi-fixed inputs: an application to school districts in Québec," Annals of Operations Research, Springer, vol. 173(1), pages 57-76, January.
    2. Sverre Kittelsen & Benny Winsnes & Kjartan Anthun & Fanny Goude & Øyvind Hope & Unto Häkkinen & Birgitte Kalseth & Jannie Kilsmark & Emma Medin & Clas Rehnberg & Hanna Rättö, 2015. "Decomposing the productivity differences between hospitals in the Nordic countries," Journal of Productivity Analysis, Springer, vol. 43(3), pages 281-293, June.
    3. repec:spr:empeco:v:52:y:2017:i:4:d:10.1007_s00181-016-1104-6 is not listed on IDEAS
    4. Eskelinen, Juha & Kuosmanen, Timo, 2013. "Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5163-5175.
    5. LaPlante, A.E. & Paradi, J.C., 2015. "Evaluation of bank branch growth potential using data envelopment analysis," Omega, Elsevier, vol. 52(C), pages 33-41.
    6. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2014. "Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data," European Journal of Operational Research, Elsevier, vol. 233(3), pages 749-758.
    7. Portela, Maria C.A.S. & Thanassoulis, Emmanuel, 2010. "Malmquist-type indices in the presence of negative data: An application to bank branches," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1472-1483, July.
    8. Nicky Rogge, 2017. "Measuring the impact of the economic crisis on the level of change in EU social inclusion: period 2005–2012," Journal of Productivity Analysis, Springer, vol. 47(2), pages 103-116, April.
    9. Dakpo, K Hervé & Desjeux, Yann & Jeanneaux, Philippe & Latruffe, Laure, 2016. "Productivity, efficiency and technological change in French agriculture during 2002-2014: A Färe-Primont index decomposition," 149th Seminar, October 27-28, 2016, Rennes, France 244793, European Association of Agricultural Economists.
    10. Choi, Kanghwa, 2017. "Multi-period efficiency and productivity changes in US domestic airlines," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 18-25.
    11. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    12. Mette Asmild & Tomas Baležentis & Jens Leth Hougaard, 2016. "Multi-directional productivity change: MEA-Malmquist," Journal of Productivity Analysis, Springer, vol. 46(2), pages 109-119, December.
    13. Paradi, Joseph C. & Zhu, Haiyan, 2013. "A survey on bank branch efficiency and performance research with data envelopment analysis," Omega, Elsevier, vol. 41(1), pages 61-79.
    14. Mette Asmild & Bruce Hollingsworth & Stephen Birch, 2013. "The scale of hospital production in different settings: one size does not fit all," Journal of Productivity Analysis, Springer, vol. 40(2), pages 197-206, October.
    15. Tsunehiro Otsuki, 2013. "Nonparametric measurement of the overall shift in the technology frontier: an application to multiple-output agricultural production data in the Brazilian Amazon," Empirical Economics, Springer, vol. 44(3), pages 1455-1475, June.

    More about this item

    Keywords

    Data envelopment analysis (DEA); Malmquist productivity change index; Frontier shifts/technical change; Global indices; C14; D24; G21; C61; C67; B21;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • B21 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Microeconomics

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