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Malmquist Productivity Indexes and DEA

In: Handbook on Data Envelopment Analysis

Author

Listed:
  • Rolf Färe

    (Oregon State University)

  • Shawna Grosskopf

    (Oregon State University)

  • Dimitris Margaritis

    (University of Auckland Business School)

Abstract

In this chapter, we provide an overview of our recent work on data envelopment analysis (DEA) and Malmquist productivity indexes. First, we review the construction of static and dynamic DEA technologies. Based on these technologies we show how DEA can be used to estimate the Malmquist productivity index introduced by Caves et al. (Econometrica 50(6):1393–14, 1982) in the static case as well as its extension into the dynamic case.

Suggested Citation

  • Rolf Färe & Shawna Grosskopf & Dimitris Margaritis, 2011. "Malmquist Productivity Indexes and DEA," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 127-149, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-6151-8_5
    DOI: 10.1007/978-1-4419-6151-8_5
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    Citations

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    Cited by:

    1. Laurens Cherchye & Bram De Rock & Dieter Saelens, 2024. "Financial portfolio performance of Belgian households : a nonparametric assessment," Working Paper Research 448, National Bank of Belgium.
    2. Huguenin, Jean-Marc, 2015. "Adjusting for the environment in DEA: A comparison of alternative models based on empirical data," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 41-54.
    3. Simar, Léopold & W. Wilson, Paul, 2019. "Central limit theorems and inference for sources of productivity change measured by nonparametric Malmquist indices," European Journal of Operational Research, Elsevier, vol. 277(2), pages 756-769.
    4. Chia Nan Wang & Anh Phuong Le, 2018. "Application in International Market Selection for the Export of Goods: A Case Study in Vietnam," Sustainability, MDPI, vol. 10(12), pages 1-24, December.
    5. Hirofumi Fukuyama & William L. Weber, 2017. "Measuring bank performance with a dynamic network Luenberger indicator," Annals of Operations Research, Springer, vol. 250(1), pages 85-104, March.
    6. Yang, Guo-liang & Fukuyama, Hirofumi & Song, Yao-yao, 2018. "Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model," Journal of Informetrics, Elsevier, vol. 12(1), pages 10-30.
    7. Lipeng Chen & Yang Yu & Amsalu K. Addis & Xiao Guo, 2022. "Empirical Assessment and Comparison of Educational Efficiency between Major Countries across the World," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    8. Li, Hui & Wu, Dongdong, 2024. "Online investor attention and firm restructuring performance: Insights from an event-based DEA-Tobit model," Omega, Elsevier, vol. 122(C).
    9. Lim, Dong-Joon, 2016. "Inverse DEA with frontier changes for new product target setting," European Journal of Operational Research, Elsevier, vol. 254(2), pages 510-516.
    10. Aleksandar Medarević & Dejana Vuković, 2021. "Efficiency and Productivity of Public Hospitals in Serbia Using DEA-Malmquist Model and Tobit Regression Model, 2015–2019," IJERPH, MDPI, vol. 18(23), pages 1-22, November.

    More about this item

    Keywords

    DEA; Malmquist productivity index;

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