Determining the Factors that Influence the Effectiveness of the Health Sector in the OECD Countries

Year 2020, Volume: 24 Issue: 5, 1094 - 1104, 01.10.2020

Selin Ceren Turan , Mehmet Ali Cengiz

https://doi.org/10.16984/saufenbilder.714736

Cited By: 1

Abstract

The purpose of this study is to determine the factors that influence the effectiveness of the health sector by combining Stochastic Frontier Analysis (SFA), Generalized Linear Models (GLM) and Heuristic Algorithms methods. In accordance with this purpose, firstly, the health system efficiencies of 29 OECD countries are estimated by the SFA method. Within the scope of this study, it is also aimed to select the factors influencing the efficiency of the health systems in OECD countries by employing Heuristic Algorithm methods such as Artificial Bee Colony Algorithm, Genetic Algorithm, and Differential Evolution Algorithm. Furthermore, GLM’s such as Truncated, Normal, Gamma and Tweedie distributions are employed for comparisons.

Keywords

Efficiency, Generalized linear models, Heuristic algorithms, SFA

Project Number

PYO. SCIENCE. 1904.17.004

References

• A. Afonso and M. St Aubyn, “Non-parametric approaches to education and health efficiency in OECD countries,” Journal of Applied Economics, vol. 8, no. 2, pp. 227, 2005.
• J. Spinks and B. Hollingsworth, “Health production and the socioeconomic determinants of health in OECD countries: the use of efficiency models,” Centre for Health Economics Working Paper 151, pp. 3, 2005.
• A. Afonso and M. St Aubyn, “Relative efficiency of health provision: A DEA approach with non-discretionary inputs,” ISEG-UTL Economics Working Paper, no. 33, pp. 3, 2006.
• J. Kujawska, “Measurement of healthcare system efficiency in OECD Countries,” Metody Ilościowe w Badaniach Ekonomicznych, vol. 16, no. 2, pp. 23-32, 2015.
• V. R. Çetin and S. Bahçe, “Measuring the efficiency of health systems of OECD countries by data envelopment analysis,” Applied Economics, vol. 48, no. 37, pp. 3497-3507, 2016.
• Y. A. Özcan and J. Khushalani, “Assessing the efficiency of public health and medical care provision in OECD countries after a decade of reform,” Central European Journal of Operations Research, vol. 25, no. 2, pp. 325-343, 2017.
• D. Wranik, “Healthcare policy tools as determinants of health system efficiency: evidence from the OECD,” Health Economics, Policy, and Law, vol. 7, no. 2, pp. 197–226, 2012.
• P. H. De Cos and E. Moral-Benito, “Determinants of health-system efficiency: evidence from OECD countries,” International Journal of Health Care Finance and Economics, vol. 14, no. 1, pp. 69-93, 2014.
• T. Şenel and M. A. Cengiz, “A Bayesian approach for evaluation of determinants of health system efficiency using stochastic frontier analysis and beta regression,” Computational and Mathematical Methods in Medicine, vol. 2016, pp. 1-5, 2016.
• V. Paris, M. Devaux, and L. Wei, “Health systems institutional characteristics: a survey of 29 OECD countries,” OECD Health Working Papers, no. 50, pp. 22-28, 2010.
• D. Aigner, C. Lowell, and P. Schmidt, “Formulation and estimation of stochastic frontier production function models,” Journal of Econometrics, vol. 6, no. 1, pp. 21–37, 1977.
• G. H. Dunteman, and M. H. R. Ho. “An introduction to generalized linear models”, vol. 145, Sage Publications, 2005.
• D. Karaboğa, Artificial Intelligence Optimization Algorithms, Ankara: Nobel, 2011.
• D. Karaboğa and B. Baştürk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, vol. 8, no. 1, pp. 687-697, 2008.
• J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, USA: MIT Press, 1992.
• Ç. Elmas, Artificial Intelligence Applications, Ankara: Seçkin, 2010.
• R. Storn and K. Price, “Differential evolution a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341-359, 1997.
• T. Keskintürk, “Differential evolution Algorithm,” Istanbul Commerce University Journal of Science, vol. 9, pp. 85-99, 2006.
• H. Akaike, “Information theory and extension of the maximum likelihood principle,” 2nd International Symposium on Information Theory, pp. 267-281, 1973.
• Hurvich, C. M. and C. L. Tsai, “Regression and time series model selection in small samples,” Biometrika, vol. 76, no. 2, pp. 297-307, 1989.
• G. Schwarz, “Estimating the dimension of model,” Annals of Statistics, vol. 6, pp. 461-464, 1978.
• H. Bozdogan, “Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions,” Psychometrika, vol. 52, no. 3, pp. 345-370, 1987.