JACIII Vol.19 No.3 pp. 430-438
doi: 10.20965/jaciii.2015.p0430


Nonparametric Optimization of Preference in Technical Efficiency in China

Juying Zeng

School of Mathematics and Statistics, Zhejiang University of Finance & Economics
No.18, Xueyuan Street, Xiasha Education Park, Hangzhou, Zhejiang 310018, China

December 31, 2013
March 1, 2015
May 20, 2015
preference in technical efficiency, path-converged approach, optimization
Applying nonparametric path-converged approach, the research innovatively provides the measurement of preference in technical efficiency by the ratio of labor elasticity to capital elasticity and further attempts to realize the optimization of preference in technical efficiency by a strategy of 30% abolishment of initial Drug Addition and a strategy with combination of smoothed governmental fiscal expenditure, which sheds fresh light on promoting hospitals’ efficiency in China from perspective of management engineering. With sample data of provincial public hospitals in Zhejiang Province during period of 200901-201306, the research obtains following conclusions. First, benchmark preference in technical efficiency shows production has shifted from physical capital preference to labor skilled preference in technical efficiency. Second, the changing trend of preference in technical efficiency validates initial Drug Addition and governmental fiscal expenditure pushes and restrains the labor skilled preference in technical efficiency respectively. Third, the strategy of 30% abolishment of Drug Addition will strengthen labor skilled preference in technical efficiency with less promotion intensity of initial Drug Addition. The strategy with combination of governmental fiscal expenditure restrains labor skilled preference in technical efficiency. The facts validate great urgency of raising payments for doctors and nurses so as to promoting efficiency effectively.
Cite this article as:
J. Zeng, “Nonparametric Optimization of Preference in Technical Efficiency in China,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.3, pp. 430-438, 2015.
Data files:
  1. [1] A. Wagstaff, W. Yip, M. Lindelow, and W. C. Hsiao, “China’s health system and its reform: A review of recent studies,” Health Economics, No.18, pp. S7-S23, 2009.
  2. [2] R. Herd, Y. Hu, and V. Koen, “Improving China’s health care system,” Economics department working papers, OECD, No.75, 2010.
  3. [3] R. Ling, F. Liu, X. Lu, and W. Wang, “Emerging issues in public health: a perspective on China’s healthcare system,” Public Health, Vol.125, pp. 9-14, 2011.
  4. [4] W. Yip and W. C. Hsiao, “China’s health care reform: A tentative assessment,” China Economic Review, Vol.20, No.4, pp. 613-619, 2009.
  5. [5] J. Zeng, “Semi-parametric identification of determinants of health expenditures – evidence from inpatients in China,” Management Decision, Vol.52, No.7, pp. 1302-1318, 2014.
  6. [6] L. Feng and H. Li, “Medicine price change: prevent solving one problem only to find another cropping up,” Guangming Daily, pp. 11-25, 2009.
  7. [7] L. Zeng and C. Lu, “Break drug dependent doctors pattern, financial compensation needs 89.6 billion yuan,” Economic Information Daily, 04-13, 2007.
  8. [8] K. Eggleston, J. Wang, and K. Rao, “From plan to market in the health sector? China’s experience,” J. of Asian Economics, Vol.19, No.5-6, pp. 400-412, 2008.
  9. [9] J. Clement, V. Valdmanis, G. Bazzoli, M. Zhao, and A. Chukmaitov, “Is better? an analysis of hospital outcomes and efficiency with a DEA model of output congestion,” Health Care Management Science, Vol.11, No.1, pp. 67-77, 2008.
  10. [10] J. Yang and W. Zeng, “The trade-offs between efficiency and quality in the hospital production: Some evidence from Shenzhen, China,” China Economic Review, Vol.31, pp. 166-184, 2014.
  11. [11] P. Berta, G. Callea, G. Martini, and G. Vittadini, “The effects of upcoding, cream skimming and readmissions on the Italian hospitals efficiency: a population-based investigation,” Economic Modelling, Vol.27, No.4, pp. 812-821, 2010.
  12. [12] Y. C. Ng, “The productive efficiency of Chinese hospitals,” China Economic Review, Vol.22, No.3, pp. 428-439, 2011.
  13. [13] H. Chang, M. Cheng, and S. Das, “Hospital ownership and operating efficiency: evidence from Taiwan,” European J. of Operational Research, Vol.159, No.2, pp. 513-527. 2004.
  14. [14] H. Hu, Q. Qi, and C. Yang, “Analysis of hospital technical efficiency in China: Effect of health insurance reform,” China Economic Review, No.23, pp. 865-877, 2012.
  15. [15] Y. Varabyova and J. Schreyogg, “International comparisons of the technical efficiency of the hospital sector: Panel data analysis of OECD countries using parametric and non-parametric approaches,” Health Policy, No.112, pp. 70-79, 2013.
  16. [16] L. Asanduluia, M. Romanb, and P. Fatulescua, “The efficiency of healthcare systems in Europe: a data envelopment analysis approach,” Procedia Economics and Finance, No.10, pp. 261-268, 2014.
  17. [17] B. Xu, J. Zeng, and J. Watada, “Changes in Production Efficiency in China: Identification and Measuring,” Springer New York, ISBN 978-1-4614-7719-8, Book DOI: 10.1007/978-1-4614-7720-4, 2014.
  18. [18] B. Xu, “The effect of foreign direct investment on regional economic growth: an application with path converged design approach,” Economic Research, No.2, pp. 44-54, 2010.
  19. [19] J. Zeng and B. Xu, “Measurement of technological progress and efficiency improvement based on the governmental investment driven mode,” The J. of Quantitative & Technical Economics, Vol.28, No.5, pp. 78-92, 2011.
  20. [20] J. Zeng, B. Xu, and J. Watada, “Identification and realization of changing technical efficiency based on path-converged design,” Int. J. of Innovative Computing, Information and Control, Vol.6, No.4, pp. 1463-1654, 2010.
  21. [21] C. Stone, “An asymptotically optimal window selection rule for kernel density estimates,” The Annals of Statistics, Vol.12, No.4, pp. 1285-1297, 1984.

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