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Efficiency of Health Care Production in Low-Resource Settings: A Monte-Carlo Simulation to Compare the Performance of Data Envelopment Analysis, Stochastic Distance Functions, and an Ensemble Model

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  • Laura Di Giorgio
  • Abraham D Flaxman
  • Mark W Moses
  • Nancy Fullman
  • Michael Hanlon
  • Ruben O Conner
  • Alexandra Wollum
  • Christopher J L Murray

Abstract

Low-resource countries can greatly benefit from even small increases in efficiency of health service provision, supporting a strong case to measure and pursue efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning efficiency measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical efficiency in LMICs and offers an alternative approach for efficiency measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing efficiency. We found that an ensemble approach (ENS) combining efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze efficiency measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of efficiency analyses in LMICs, and thus inform policy dialogues about improving the efficiency of health service production in these settings.

Suggested Citation

  • Laura Di Giorgio & Abraham D Flaxman & Mark W Moses & Nancy Fullman & Michael Hanlon & Ruben O Conner & Alexandra Wollum & Christopher J L Murray, 2016. "Efficiency of Health Care Production in Low-Resource Settings: A Monte-Carlo Simulation to Compare the Performance of Data Envelopment Analysis, Stochastic Distance Functions, and an Ensemble Model," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
  • Handle: RePEc:plo:pone00:0147261
    DOI: 10.1371/journal.pone.0147261
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    as
    1. William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), 2011. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science, Springer, number 978-1-4419-6151-8, December.
    2. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2008. "Asymptotics And Consistent Bootstraps For Dea Estimators In Nonparametric Frontier Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1663-1697, December.
    3. Mehdi Farsi & Massimo Filippini, 2006. "An Analysis of Efficiency and Productivity in Swiss Hospitals," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 142(I), pages 1-37, March.
    4. Mark Andor & Frederik Hesse, "undated". "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers 201285, Institute of Spatial and Housing Economics, Munster Universitary.
    5. repec:zbw:rwirep:0394 is not listed on IDEAS
    6. Till Barnighausen & David E. Bloom & David Canning, 2010. "Universal antiretroviral treatment: the challenge of human resources," PGDA Working Papers 5510, Program on the Global Demography of Aging.
    7. Nyman, John A & Bricker, Dennis L, 1989. "Profit Incentives and Technical Efficiency in the Production of Nursing Home Care," The Review of Economics and Statistics, MIT Press, vol. 71(4), pages 586-594, November.
    8. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    9. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, vol. 41(1), pages 85-109, February.
    10. Dias, Carlos Tadeu dos Santos & Samaranayaka, Ari & Manly, Bryan, 2008. "On the use of correlated beta random variables with animal population modelling," Ecological Modelling, Elsevier, vol. 215(4), pages 293-300.
    11. Mark Andor & Frederik Hesse, "undated". "A Monte Carlo Simulation comparing DEA, SFA and two simple approaches to combine efficiency estimates," Working Papers 201177, Institute of Spatial and Housing Economics, Munster Universitary.
    12. Resti, Andrea, 2000. "Efficiency measurement for multi-product industries: A comparison of classic and recent techniques based on simulated data," European Journal of Operational Research, Elsevier, vol. 121(3), pages 559-578, March.
    13. Grosskopf, S. & Valdmanis, V., 1987. "Measuring hospital performance : A non-parametric approach," Journal of Health Economics, Elsevier, vol. 6(2), pages 89-107, June.
    14. Ruggiero, John, 1998. "A new approach for technical efficiency estimation in multiple output production," European Journal of Operational Research, Elsevier, vol. 111(2), pages 369-380, December.
    15. Banker, Rajiv D. & Chang, Hsihui & Cooper, William W., 2004. "A simulation study of DEA and parametric frontier models in the presence of heteroscedasticity," European Journal of Operational Research, Elsevier, vol. 153(3), pages 624-640, March.
    16. Maria Portela & Emmanuel Thanassoulis, 2006. "Zero weights and non-zero slacks: Different solutions to the same problem," Annals of Operations Research, Springer, vol. 145(1), pages 129-147, July.
    17. Yu, Chunyan, 1998. "The effects of exogenous variables in efficiency measurement--A monte carlo study," European Journal of Operational Research, Elsevier, vol. 105(3), pages 569-580, March.
    18. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    19. Ruggiero, John, 1999. "Efficiency estimation and error decomposition in the stochastic frontier model: A Monte Carlo analysis," European Journal of Operational Research, Elsevier, vol. 115(3), pages 555-563, June.
    20. Coelli, Tim & Perelman, Sergio, 1999. "A comparison of parametric and non-parametric distance functions: With application to European railways," European Journal of Operational Research, Elsevier, vol. 117(2), pages 326-339, September.
    21. Kneller, Richard & Andrew Stevens, Philip, 2003. "The specification of the aggregate production function in the presence of inefficiency," Economics Letters, Elsevier, vol. 81(2), pages 223-226, November.
    22. Sergio Perelman & Daniel Santin, 2011. "Measuring educational efficiency at student level with parametric stochastic distance functions: an application to Spanish PISA results," Education Economics, Taylor & Francis Journals, vol. 19(1), pages 29-49.
    23. Paul Marschall & Steffen Flessa, 2011. "Efficiency of primary care in rural Burkina Faso. A two-stage DEA analysis," Health Economics Review, Springer, vol. 1(1), pages 1-15, December.
    24. Banker, Rajiv D. & Gadh, Vandana M. & Gorr, Wilpen L., 1993. "A Monte Carlo comparison of two production frontier estimation methods: Corrected ordinary least squares and data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 67(3), pages 332-343, June.
    25. Jacobs,Rowena & Smith,Peter C. & Street,Andrew, 2006. "Measuring Efficiency in Health Care," Cambridge Books, Cambridge University Press, number 9780521851442.
    26. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    27. Bifulco, Robert & Bretschneider, Stuart, 2001. "Estimating school efficiency: A comparison of methods using simulated data," Economics of Education Review, Elsevier, vol. 20(5), pages 417-429, October.
    28. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    29. Xavier Irz & Colin Thirtle, 2004. "Dual Technological Development in Botswana Agriculture: A Stochastic Input Distance Function Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 55(3), pages 455-478, November.
    30. Collier, Trevor & Johnson, Andrew L. & Ruggiero, John, 2011. "Technical efficiency estimation with multiple inputs and multiple outputs using regression analysis," European Journal of Operational Research, Elsevier, vol. 208(2), pages 153-160, January.
    31. Sara Bennett & Sachiko Ozawa & Krishna D Rao, 2010. "Which Path to Universal Health Coverage? Perspectives on the World Health Report 2010," PLOS Medicine, Public Library of Science, vol. 7(11), pages 1-3, November.
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    2. Nicolas A. Menzies & Christian Suharlim & Stephen C. Resch & Logan Brenzel, 2020. "The efficiency of routine infant immunization services in six countries: a comparison of methods," Health Economics Review, Springer, vol. 10(1), pages 1-11, December.

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