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Technical efficiency in texas nursing facilities: A stochastic production frontier approach

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  • Kris Knox
  • Eric Blankmeyer
  • J. Stutzman

Abstract

A rapidly aging U. S. population is straining the resources available for long term care and increasing the urgency of efficient operations in nursing homes. The scope for productivity improvements can be examined by estimating a stochastic frontier production function. We apply the methods of maximum likelihood and quantile regression to a panel of Texas nursing facilities and infer that the average productivity shortfall due to avoidable technical inefficiency is at least 8 percent and perhaps as large as 20 percent. Non-profit facilities are notably less productive than comparable facilities operated for profit, and the industry has constant returns to scale. Copyright Springer 2007

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  • Kris Knox & Eric Blankmeyer & J. Stutzman, 2007. "Technical efficiency in texas nursing facilities: A stochastic production frontier approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 31(1), pages 75-86, March.
    • Handle: RePEc:spr:jecfin:v:31:y:2007:i:1:p:75-86
    DOI: 10.1007/BF02751513
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    Cited by:

    1. Bernstein, David H. & Parmeter, Christopher F. & Tsionas, Mike G., 2023. "On the performance of the United States nuclear power sector: A Bayesian approach," Energy Economics, Elsevier, vol. 125(C).
    2. repec:dau:papers:123456789/11700 is not listed on IDEAS
    3. Per J. Agrell & Mehdi Farsi & Massimo Filippini & Martin Koller, 2013. "Unobserved heterogeneous effects in the cost efficiency analysis of electricity distribution systems," Working Papers 0038, Swiss Economics.
    4. Galina Besstremyannaya & Sergei Golovan, 2023. "Measuring heterogeneity in hospital productivity: a quantile regression approach," Journal of Productivity Analysis, Springer, vol. 59(1), pages 15-43, February.
    5. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    6. Knox, Kris Joseph & Blankmeyer, Eric C. & Trinidad, José A. & Stutzman, J.R., 2009. "Predicting bankruptcy in the Texas nursing facility industry," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 1047-1064, August.
    7. Chunping Liu & Audrey Laporte & Brian S. Ferguson, 2008. "The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1073-1087, September.
    8. William C. Horrace & Christopher F. Parmeter & Ian A. Wright, 2024. "On asymmetry and quantile estimation of the stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 61(1), pages 19-36, February.
    9. Galina Besstremyannaya, 2015. "Heterogeneous effect of residency matching and prospective payment on labor returns and hospital scale economies," Discussion Papers 15-001, Stanford Institute for Economic Policy Research.
    10. Marie-Eve Joël & Romain Sibille, 2013. "Education and career path of French nursing home managers : impact on their human resources practices," Post-Print hal-01522519, HAL.
    11. Tsionas, Mike G. & Assaf, A. George & Andrikopoulos, Athanasios, 2020. "Quantile stochastic frontier models with endogeneity," Economics Letters, Elsevier, vol. 188(C).
    12. Besstremyannaya, Galina & Dasher, Richard & Golovan, Sergei, 2022. "Quantifying heterogeneity in the relationship between R&D intensity and growth at innovative Japanese firms: A quantile regression approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 27-45.
    13. Mamatzakis, E & Koutsomanoli-Filippaki, Anastasia & Pasiouras, Fotios, 2012. "A quantile regression approach to bank efficiency measurement," MPRA Paper 51879, University Library of Munich, Germany.
    14. E. Fusco & R. Benedetti & F. Vidoli, 2023. "Stochastic frontier estimation through parametric modelling of quantile regression coefficients," Empirical Economics, Springer, vol. 64(2), pages 869-896, February.
    15. Alice Tran & Kim-Huong Nguyen & Len Gray & Tracy Comans, 2019. "A Systematic Literature Review of Efficiency Measurement in Nursing Homes," IJERPH, MDPI, vol. 16(12), pages 1-18, June.
    16. Iparraguirre, José Luis & Ma, Ruosi, 2015. "Efficiency in the provision of social care for older people. A three-stage Data Envelopment Analysis using self-reported quality of life," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 33-46.
    17. Shiovan Ni Luasa & Declan Dineen & Marta Zieba, 2018. "Technical and scale efficiency in public and private Irish nursing homes – a bootstrap DEA approach," Health Care Management Science, Springer, vol. 21(3), pages 326-347, September.
    18. Zhang, Ning & Huang, Xuhui & Liu, Yunxiao, 2021. "The cost of low-carbon transition for China's coal-fired power plants: A quantile frontier approach," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    19. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, New Economic School (NES).
    20. Martin, Cécile, 2014. "Concurrence, prix et qualité de la prise en charge en EHPAD en France : Analyses micro-économétriques," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/13712 edited by Dormont, Brigitte.

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