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SETSCI - Volume 4 (6) (2019)
ISAS WINTER-2019 (ENS) - 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Turkey, Nov 22, 2019

Statistical precision of Malmquist Productivity Index and DEA: A bootstrap application to OECD healthcare
Serpil Aydın1*
1Ondokuz Mayıs University, Samsun, Turkey
* Corresponding author: serpil.gumustekin@omu.edu.tr
Published Date: 2019-12-22   |   Page (s): 462-465   |    209     2
https://doi.org/10.36287/setsci.4.6.125

ABSTRACT Data Envelopment Analysis (DEA) is a non-parametric mathematical programming that uses multi inputs to produce multi outputs. It is investigated whether the decision making unit is effective or not. However, it has been debated for years that the results obtained from DEA do not provide statistical inference. Simar and Wilson (1998) develop a bootstrap procedure which may be used to estimate confidence intervals for distance functions used to measure technical efficiency, and demonstrate that the key to statistically consistent estimation of these confidence intervals lies in the replication of the unobserved data-generating process. This paper examines to the case of Malmquist indices constructed from nonparametric distance function estimates using data of OECD countries during the period of 2007 through 2017.I use DEA to derive efficiency scores, Malmquist indices to assess productivity growth, second-stage regression analysis to infer the impact of external factors and, finally, bootstrapping to determine the accuracy of estimators and whether conclusions would change after considering this statistical information. According to the study, due to the widespread return among countries, the most appropriate scale of operational health services should be investigated instead of treatment centered on people and focused on disease prevention.
KEYWORDS Efficiency, Malmquist DEA, Bootsra, Healthcare
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