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Bootstrapping the data envelopment analysis Malmquist productivity index

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  • Mickael Lothgren
  • Magnus Tambour

Abstract

This paper presents a bootstrap approach to calculate confidence intervals for firm-specific Malmquist productivity indices obtained from data envelopment analysis (DEA) models. The bootstrap is easily implemented and allows identification of production units that have significant productivity changes. An application using data from Swedish eye-care departments is included. We find that 40% of departments have significant progress in productivity whereas only 10% of the departments have a significant regress in productivity. This differs from the original results where about half the sample have estimates of progress and the other half have estimates of regress in productivity.

Suggested Citation

  • Mickael Lothgren & Magnus Tambour, 1999. "Bootstrapping the data envelopment analysis Malmquist productivity index," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 417-425.
    • Handle: RePEc:taf:applec:v:31:y:1999:i:4:p:417-425
    DOI: 10.1080/000368499324129
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    References listed on IDEAS

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    1. WILSON, Paul & SIMAR, Leopold, 1995. "Bootstrap Estimation for Nonparametric Efficiency Estimates," LIDAM Discussion Papers CORE 1995071, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. Kanybek Nur-tegin, 2007. "Do Transition Economies and Developing Countries Have Similar Destinies?," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 35(3), pages 327-342, September.
    2. Lamb, John D. & Tee, Kai-Hong, 2012. "Resampling DEA estimates of investment fund performance," European Journal of Operational Research, Elsevier, vol. 223(3), pages 834-841.
    3. X. M. Gonzalez & D. Miles, 2002. "Statistical precision of DEA: a bootstrap application to Spanish public services," Applied Economics Letters, Taylor & Francis Journals, vol. 9(2), pages 127-132.
    4. Matthews, Kent & Zhang, Nina (Xu), 2010. "Bank productivity in China 1997-2007: Measurement and convergence," China Economic Review, Elsevier, vol. 21(4), pages 617-628, December.
    5. Peipei Chai & Quan Wan & Yohannes Kinfu, 2021. "Efficiency and productivity of health systems in prevention and control of non-communicable diseases in China, 2008–2015," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(2), pages 267-279, March.
    6. Mickael Lothgren, 1999. "Bootstrapping the Malmquist productivity index - a simulation study," Applied Economics Letters, Taylor & Francis Journals, vol. 6(11), pages 707-710.
    7. Halkos, George & Argyropoulou, Georgia, 2024. "Use of indexes in evaluating environmental and health efficiency," MPRA Paper 119800, University Library of Munich, Germany.
    8. Matthews, Kent & Zhang, Nina, 2009. "Bank Productivity in China 1997-2007: An Exercise in Measurement," Cardiff Economics Working Papers E2009/14, Cardiff University, Cardiff Business School, Economics Section.
    9. Jose Zofio, 2007. "Malmquist productivity index decompositions: a unifying framework," Applied Economics, Taylor & Francis Journals, vol. 39(18), pages 2371-2387.
    10. Olawale Ogunrinde & Ekundayo Shittu, 2023. "Benchmarking performance of photovoltaic power plants in multiple periods," Environment Systems and Decisions, Springer, vol. 43(3), pages 489-503, September.
    11. José M. Cordero & Agustín García-García & Enrique Lau-Cortés & Cristina Polo, 2021. "Efficiency and Productivity Change of Public Hospitals in Panama: Do Management Schemes Matter?," IJERPH, MDPI, vol. 18(16), pages 1-21, August.
    12. Anirban Pal & Piyush Kumar Singh, 2021. "Do socially motivated self‐help groups perform better? Exploring determinants of micro‐credit groups’ performance in Eastern India," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 92(1), pages 119-146, March.
    13. Dong‐Sing He & Imen Tebourbi, 2021. "Measuring the continuation effects of market order entry: A dynamic model," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(3), pages 762-777, April.
    14. Thanh Ngo & Kan Wai Hong Tsui, 2022. "Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines," Operational Research, Springer, vol. 22(4), pages 3411-3434, September.
    15. Joseph G. Hirschberg & Jenny N. Lye, 2001. "Clustering in a Data Envelopment Analysis Using Bootstrapped Efficiency Scores," Department of Economics - Working Papers Series 800, The University of Melbourne.
    16. Daniel Friesner & Matthew McPherson & Robert Rosenman, 2006. "Are Hospitals Seasonally Inefficient? Evidence from Washington State Hospitals," Working Papers 2006-3, School of Economic Sciences, Washington State University.
    17. Hans J. Czap & Kanybek D. Nur-tegin, 2011. "Big Bang vs. Gradualism – A Productivity Analysis," EuroEconomica, Danubius University of Galati, issue 29, pages 38-56, August.
    18. André Leclerc & Mario Fortin, 2003. "Mesure De La Production Bancaire, Rationalisation Et Efficacité Des Caisses Populaires Desjardins," Cahiers de recherche 03-07, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    19. G Souza & M Souza & E Gomes, 2011. "Computing confidence intervals for output-oriented DEA models: an application to agricultural research in Brazil," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(10), pages 1844-1850, October.
    20. Rafael Benítez & Vicente Coll-Serrano & Vicente J. Bolós, 2021. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    21. Amir Moradi-Motlagh & Ali Emrouznejad, 2022. "The origins and development of statistical approaches in non-parametric frontier models: a survey of the first two decades of scholarly literature (1998–2020)," Annals of Operations Research, Springer, vol. 318(1), pages 713-741, November.
    22. Theodoridis, A.M. & Psychoudakis, A. & Christofi, A., 2006. "Data Envelopment Analysis as a Complement to Marginal Analysis," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 7(2), pages 1-11, July.
    23. George Halkos & Georgia Argyropoulou, 2021. "Pollution and Health Effects: A Nonparametric Approach," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 691-714, October.

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