IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v73y2018icp395-406.html
   My bibliography  Save this article

A comparison of the robust conditional order-m estimation and two stage DEA in measuring healthcare efficiency among California counties

Author

Listed:
  • Gearhart, Richard S.
  • Michieka, Nyakundi M.

Abstract

This paper examines cross-county healthcare efficiency rankings using modern non-parametric estimators, while taking into account secondary environmental variables. Results indicate that output-direction efficiency estimates yield counties producing inefficiently for both order-alpha and order-m estimators. After accounting for a variety of secondary environmental variables, unconditional efficiency estimates improve by anywhere between 7.5 and 10-percentage points. Results show that there is little correlation between the highly visible Robert-Woods-Johnson Foundation estimates with those derived here. We also find that counties are more efficient when they possess lower rates of obesity, unemployment, and preventable hospital readmissions. In addition, demographic variables do not play much of a role in explaining cross-county inefficiency. The analysis shows that the two stage DEA is inappropriate and violates several assumptions in comparison to the conditional order-m estimation.

Suggested Citation

  • Gearhart, Richard S. & Michieka, Nyakundi M., 2018. "A comparison of the robust conditional order-m estimation and two stage DEA in measuring healthcare efficiency among California counties," Economic Modelling, Elsevier, vol. 73(C), pages 395-406.
    • Handle: RePEc:eee:ecmode:v:73:y:2018:i:c:p:395-406
    DOI: 10.1016/j.econmod.2018.04.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999317312312
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2018.04.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Varabyova, Yauheniya & Müller, Julia-Maria, 2016. "The efficiency of health care production in OECD countries: A systematic review and meta-analysis of cross-country comparisons," Health Policy, Elsevier, vol. 120(3), pages 252-263.
    2. Mallick, Sushanta & Matousek, Roman & Tzeremes, Nickolaos G., 2016. "Financial development and productive inefficiency: A robust conditional directional distance function approach," Economics Letters, Elsevier, vol. 145(C), pages 196-201.
    3. Bădin, Luiza & Daraio, Cinzia & Simar, Léopold, 2012. "How to measure the impact of environmental factors in a nonparametric production model," European Journal of Operational Research, Elsevier, vol. 223(3), pages 818-833.
    4. Mastromarco, Camilla & Simar, Léopold, 2018. "Globalization and productivity: A robust nonparametric world frontier analysis," Economic Modelling, Elsevier, vol. 69(C), pages 134-149.
    5. Alan M. Garber & Jonathan Skinner, 2008. "Is American Health Care Uniquely Inefficient?," Journal of Economic Perspectives, American Economic Association, vol. 22(4), pages 27-50, Fall.
    6. Deaton, Angus S & Paxson, Christina H, 1998. "Aging and Inequality in Income and Health," American Economic Review, American Economic Association, vol. 88(2), pages 248-253, May.
    7. Wheelock, David C. & Wilson, Paul W., 2009. "Robust Nonparametric Quantile Estimation of Efficiency and Productivity Change in U.S. Commercial Banking, 1985–2004," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 354-368.
    8. Luiza Bădin & Cinzia Daraio & Léopold Simar, 2014. "Explaining inefficiency in nonparametric production models: the state of the art," Annals of Operations Research, Springer, vol. 214(1), pages 5-30, March.
    9. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    10. Janet Currie & Reed Walker, 2011. "Traffic Congestion and Infant Health: Evidence from E-ZPass," American Economic Journal: Applied Economics, American Economic Association, vol. 3(1), pages 65-90, January.
    11. Robert E. Hall & Charles I. Jones, 2007. "The Value of Life and the Rise in Health Spending," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(1), pages 39-72.
    12. Wheelock, David C. & Wilson, Paul W., 2008. "Non-parametric, unconditional quantile estimation for efficiency analysis with an application to Federal Reserve check processing operations," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 209-225, July.
    13. Richard Gearhart, 2016. "The robustness of cross-country healthcare rankings among homogeneous OECD countries," Journal of Applied Economics, Universidad del CEMA, vol. 19, pages 113-144, May.
    14. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2015. "When Bias Kills The Variance: Central Limit Theorems For Dea And Fdh Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 31(2), pages 394-422, April.
    15. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    16. Léopold Simar & Paul Wilson, 2011. "Two-stage DEA: caveat emptor," Journal of Productivity Analysis, Springer, vol. 36(2), pages 205-218, October.
    17. Cinzia Daraio & Léopold Simar, 2007. "Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach," Journal of Productivity Analysis, Springer, vol. 28(1), pages 13-32, October.
    18. Lant Pritchett & Lawrence H. Summers, 1996. "Wealthier is Healthier," Journal of Human Resources, University of Wisconsin Press, vol. 31(4), pages 841-868.
    19. Kristof De Witte & Mika Kortelainen, 2013. "What explains the performance of students in a heterogeneous environment? Conditional efficiency estimation with continuous and discrete environmental variables," Applied Economics, Taylor & Francis Journals, vol. 45(17), pages 2401-2412, June.
    20. Richard Gearhart, 2016. "The Robustness of Cross-Country Healthcare Rankings Among Homogeneous Oecd Countries," Journal of Applied Economics, Taylor & Francis Journals, vol. 19(1), pages 113-143, May.
    21. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    22. Cawley, John & Meyerhoefer, Chad, 2012. "The medical care costs of obesity: An instrumental variables approach," Journal of Health Economics, Elsevier, vol. 31(1), pages 219-230.
    23. Daouia, Abdelaati & Simar, Leopold, 2007. "Nonparametric efficiency analysis: A multivariate conditional quantile approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 375-400, October.
    24. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
    25. Alan M. Garber & Jonathan Skinner, 2008. "Is American Health Care Uniquely Inefficient?," NBER Working Papers 14257, National Bureau of Economic Research, Inc.
    26. Macintyre, Sally, 1997. "The black report and beyond what are the issues?," Social Science & Medicine, Elsevier, vol. 44(6), pages 723-745, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Richard Gearhart & Nyakundi Michieka, 2020. "A non-parametric investigation of supply side factors and healthcare efficiency in the U.S," Journal of Productivity Analysis, Springer, vol. 54(1), pages 59-74, August.
    2. Gearhart, Richard & Michieka, Nyakundi, 2019. "Natural resource abundance and healthcare efficiency in Appalachia: A robust conditional approach," Energy Policy, Elsevier, vol. 129(C), pages 985-996.
    3. Xian‐Hui He & Yung‐ho Chiu & Tzu‐Han Chang & Liang‐Chun Lu & Shih‐Yung Chiu, 2021. "Analyzing hospital medical efficiency of administration and medical treatment in China," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1564-1578, September.
    4. Dino Rizzi & Michele Zanette, 2021. "Potential efficiency gains and expenditure savings in the Italian Regional Healthcare Systems," Politica economica, Società editrice il Mulino, issue 2, pages 187-214.
    5. Sushanta Mallick & Aarti Rughoo & Nickolaos G. Tzeremes & Wei Xu, 2020. "Technological Change and Catching-Up in the Indian Banking Sector: A Time-Dependent Nonparametric Frontier Approach," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 217-237, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gearhart, Richard & Michieka, Nyakundi, 2019. "Natural resource abundance and healthcare efficiency in Appalachia: A robust conditional approach," Energy Policy, Elsevier, vol. 129(C), pages 985-996.
    2. Gearhart Richard, 2019. "The Impact of Secondary Environmental Variables on OECD Healthcare Efficiency: A Robust Conditional Approach," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 19(2), pages 1-16, April.
    3. Cordero, José Manuel & Pedraja-Chaparro, Francisco & Pisaflores, Elsa C. & Polo, Cristina, 2016. "Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach," MPRA Paper 70674, University Library of Munich, Germany.
    4. Cordero, Jose M. & Polo, Cristina & Santín, Daniel & Simancas, Rosa, 2018. "Efficiency measurement and cross-country differences among schools: A robust conditional nonparametric analysis," Economic Modelling, Elsevier, vol. 74(C), pages 45-60.
    5. López-Torres, Laura & Johnes, Jill & Elliott, Caroline & Polo, Cristina, 2021. "The effects of competition and collaboration on efficiency in the UK independent school sector," Economic Modelling, Elsevier, vol. 96(C), pages 40-53.
    6. Belmonte-Martin, Irene & Ortiz, Lidia & Polo, Cristina, 2021. "Local tax management in Spain: A study of the conditional efficiency of provincial tax agencies," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    7. 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.
    8. Cordero Ferrera, Jose Manuel & Alonso Morán, Edurne & Nuño Solís, Roberto & Orueta, Juan F. & Souto Arce, Regina, 2013. "Efficiency assessment of primary care providers: A conditional nonparametric approach," MPRA Paper 51926, University Library of Munich, Germany.
    9. Daraio, Cinzia & Simar, Léopold, 2014. "Directional distances and their robust versions: Computational and testing issues," European Journal of Operational Research, Elsevier, vol. 237(1), pages 358-369.
    10. Richard Gearhart & Nyakundi Michieka, 2020. "A non-parametric investigation of supply side factors and healthcare efficiency in the U.S," Journal of Productivity Analysis, Springer, vol. 54(1), pages 59-74, August.
    11. Sushanta Mallick & Aarti Rughoo & Nickolaos G. Tzeremes & Wei Xu, 2020. "Technological Change and Catching-Up in the Indian Banking Sector: A Time-Dependent Nonparametric Frontier Approach," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 217-237, June.
    12. Cordero, José Manuel & Salinas-Jiménez, Javier & Salinas-Jiménez, M Mar, 2017. "Exploring factors affecting the level of happiness across countries: A conditional robust nonparametric frontier analysis," European Journal of Operational Research, Elsevier, vol. 256(2), pages 663-672.
    13. Nickolaos G. Tzeremes, 2018. "Financial Development and Countries’ Production Efficiency: A Nonparametric Analysis," JRFM, MDPI, vol. 11(3), pages 1-13, August.
    14. Polemis, Michael L. & Tzeremes, Nickolaos G., 2019. "Competitive conditions and sectors’ productive efficiency: A conditional non-parametric frontier analysis," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1104-1118.
    15. López-Herrera, Carmen & Cordero, José M. & Pedraja-Chaparro, Francisco & Polo, Cristina, 2023. "Fiscal rules and their influence on public sector efficiency," MPRA Paper 119018, University Library of Munich, Germany.
    16. Gearhart, Richard & Michieka, Nyakundi & Anders, Anne, 2023. "The effectiveness of COVID deaths to COVID policies: A robust conditional approach," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 376-394.
    17. Nickolaos G. Tzeremes, 2019. "Technological change, technological catch-up and export orientation: evidence from Latin American Countries," Journal of Productivity Analysis, Springer, vol. 52(1), pages 85-100, December.
    18. Jose M. Cordero & Francisco Pedraja-Chaparro & Elsa C. Pisaflores & Cristina Polo, 2017. "Efficiency assessment of Portuguese municipalities using a conditional nonparametric approach," Journal of Productivity Analysis, Springer, vol. 48(1), pages 1-24, August.
    19. Calogero Guccio & Domenico Lisi & Marco Martorana & Anna Mignosa, 2017. "On the role of cultural participation in tourism destination performance: an assessment using robust conditional efficiency approach," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 41(2), pages 129-154, May.
    20. Cinzia Daraio & Léopold Simar & Paul W. Wilson, 2020. "Fast and efficient computation of directional distance estimators," Annals of Operations Research, Springer, vol. 288(2), pages 805-835, May.

    More about this item

    Keywords

    Conditional order-m estimator; Conditional efficiency; Order- α; California counties; Nonparametric econometrics;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecmode:v:73:y:2018:i:c:p:395-406. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.